By Frank Harmsen, Partner, Government and Public Sector, EY.

Amsterdam has never been a city that has shied away from innovation. It was in Amsterdam that the groundwork for modern capitalism was laid, with the emergence of the first multinational companies and the evolution of stock markets from small-time lenders into muscular institutions capable of financing international enterprise.

But where once the city’s fortunes hinged on the return to port of a spice-filled frigate, growth now sails on seas of data. The capture, analysis and trading of huge troves of raw information is at the core of Amsterdam’s growth plans as it transforms itself into a truly smart city, fit to compete globally as a hub for business, innovation — and leisure.

The benefits of becoming a smart city are varied, but for Amsterdam, three clear opportunities stood out.

1. To use data to make the city as convenient as possible as a place to live and work — making it an attractive destination for businesses and talented people
2. To experiment with, learn from and pioneer best practice in the application of data to help overcome urban and organizational challenges
3. To harvest large volumes of data — on everything from energy usage to traffic volumes — that could be shared or traded with businesses and institutions in order to develop better or new services

Transforming Amsterdam into a smart city

As with so many transformational initiatives, strong leadership and collaboration between stakeholders can make a significant difference. Amsterdam’s collection of vocal and independent stakeholders includes elected officials from varied urban and suburban districts, government department managers and a diverse group of businesses and citizens, from a population of nearly a million people. Getting these people to come together behind a single goal could have been challenging.

But come together they did. Setting the standard from modern urban stakeholder collaboration and innovation, they took their city’s vaunted history of public-private partnerships into the 21st century via the Amsterdam Smart City (ASC) initiative, started in 2009 to bring Amsterdam into the upper echelons of global connected cities.

The Amsterdam Smart City program’s success has been driven by several key features, which can serve as valuable lessons for any organization — or city — planning to make a similar transition.

1. Strong leadership and support from the top

Two men in particular are responsible for the success of the program. The first is Amsterdam’s CTO Ger Baron, dubbed “Mr. Outside” for his strong advocacy work and political skills in engaging city leaders and other stakeholders.

The second is Berent Daan, Amsterdam’s Director of Research, Information and Statistics, and previously a wethouder, (a kind of “city alderman”) for eight years, giving him invaluable insight into municipal politics. Daan is the “Mr. Inside,” to Baron’s Mr. Outside and, together with his team, implemented the development processes at the heart of Amsterdam’s transformation into a data-driven city of the future.

The backing of the political establishment has also been critical. Admirably, Amsterdam’s political leaders continued to pursue the Smart City project despite changes of administration, and despite the modest early results that flew in the face of pressure to show concrete benefits.

2. Growing a talent pool

One of the key challenges in a Smart City initiative — and in any major analytics strategy — is attracting and retaining talent.

Key to getting the right talent on board was the Amsterdam Institute for Advanced Metropolitan Solutions (formed by the city’s CTO). The institute is a university program dedicated to developing smart cities; it generates ideas that can be directly applied to Amsterdam while also making the city a hub for people more generally interested in using data to make a positive difference to the world.

3. Keeping customer needs at the forefront

Amsterdam’s Smart City managers have learned that even though they can make very sophisticated presentations of data, it’s the consumers of that data who ultimately dictate the best methods of communication. Utrecthsestraat is one of the most exclusive shopping streets in the city, and has now redubbed itself “Climate Street” after embarking on a green activity campaign.

The shop owners wanted to receive annual reports with simple personalized recommendations on how to reduce energy use, and the cities data analytics program helped them get these tailored solutions.

4. Emphasis on proof-of-concept projects

Amsterdam has produced more than 80 pilots as part of the Smart City initiative. These range from the straightforward to grander strategies that involve whole communities. Letting welfare recipients know when their payments are coming via SMS was a simple but valuable change. A scheme to have residents separate biomass from recycling streams to feed the city’s waste-to-energy power plant, and which saw remarkable success in participating households, required a much greater degree of collaboration. From the very big to the very small, this kind of granular approach to innovation turns the entire city into a lab.

5. Building effective alliances

It pays to work with partners in data efforts.

For example, tapping into grocery store data helped formulate and evaluate a city healthy eating campaign for children. And private insurance companies helped gather data on city areas that needed more mental health services.

While Amsterdam may be criss-crossed by canals, the Smart City is not an island. No single group of stakeholders in Amsterdam could do this alone. These partnerships highlight how, after more than 400 years as a center of business and enterprise, public and private bodies are still working together for mutual gain.

By Jonas Groes, Partner, Advisory, Ernst & Young Global Limited.

I heard less than half the population in OECD countries trust their governments. Less than half? It seems there is widespread apprehension among citizens about sharing personal data with governments. Terrorism, increased information and awareness about how personal data can be misused – and news about data breach incidents in the public sector, no matter how few – seem to be adding fuel to this fire of fear and doubt.

Perspectives on this topic are very diverse. There is a view that we, as consumers, are more open to providing online private players access to our data than we are, as citizens, to governments. There is also a view that our expectations from governments on how our data is handled are much higher than our expectations from private companies.

Governments today have a strong, even ineluctable, case to go digital; therefore, gaining back citizens’ trust, getting them to share personal data confidently and living up to their expectations seem to be the only next steps. Here are a few lessons that we can learn from the experiences of governments that have succeeded.

• Design for security: Design solutions and systems with data protection in mind instead of retrofitting privacy “fences.” Look at new models for privacy, and new ways of segmenting the systems and using encryption models if needed.
• Improve user experience: Improve the experience of the individuals who give government agencies their data. This could be done by ensuring individuals only give their data to a single agency — which, by the way, would require agencies to share data more often than they might have in the past.
• Make lives easier with digital: Use digital to make the lives of citizens easier, for example, by creating apps that make citizens’ interaction with government agencies and departments (for income tax, utilities, children’s education purposes, etc.) seamless and through a single point of contact.
• Balance compliance and efficiency: This means working together across agencies, conducting risk assessments to inform decision-making, and matching capabilities to need.
• Generate awareness: Educate citizens on data security and on the importance of digital to ensure proactive public participation. Governments can also help drive awareness campaigns on the importance of people and organizations treating their data carefully.

Going beyond compliance

While governments are required to comply with regulations, such as the EU General Data Protection Regulation (GDPR) due to come into effect on 25 May 2018, it shouldn’t be only about compliance. Governments can use regulations to think harder and more strategically about how it captures, stores and uses data. And by doing so, it would be taking great strides toward renewing and building trust with its people.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited

By Hans Jessen, Global RPA Innovation Leader, EY.

There is a new robot in town that is creating a disruption in IT but it is not the physical, human-inspired robot that we are used to. Robotic process automation (RPA) is more about the software that consists of a set of coded commands that communicate with digital systems. These robots are programmed to follow rules, completing back-office and repetitive process-oriented tasks much more efficiently than humans, and they are beginning to change the way the outsourcing industry works.

Software robots could potentially transform the workforce by eliminating some jobs and creating new skill sets and roles for employees. Many rules-driven and process-oriented job functions, such as data entry positions, can be automated. Robots would not necessarily eliminate the need for human workers — they would enable employees to focus on more strategic initiatives and opportunities — but companies need to be sure that they have the right talent to successfully make this transition.

The advantages of RPA

Enterprises and their CIOs are under constant pressure to control cost and increase output in today’s fast-changing business landscape. RPA is one solution that can go a long way in helping enterprises meet these demands.

The use cases for RPA are increasing, and as more organizations begin automating their processes, some benefits include:

• Cost savings: Software robots are estimated to cost one-third of the price of an offshore full-time employee (FTE) and as little as one-fifth of the price of an onshore FTE.
• Availability: Unlike humans, software robots are available round the clock, which leads to higher productivity.
• Accuracy: Accuracy levels are high with software robots and eliminate the need for human intervention and the possibility of human errors.
• Compliance: RPA can be programmed to follow standard operating procedures without any lapse in productivity.

Roadblocks in RPA adoption

While the benefits of RPA are compelling, it does have some limitations that have slowed its widespread enterprise adoption:

• RPA is best suited for highly rules-driven processes, and can lead to processing errors when there are frequent changes to those standard procedures.
• RPA is not ideal for processing scanned images and unstructured data, such as free-flowing emails and attachments.
• Given the nature of RPA, an error in one entry or transaction can be replicated across multiple entries. Correcting such mistakes can be challenging in the absence of proper monitoring.

RPA impact on the outsourcing industry

Automation is forcing business process outsourcing (BPO) and IT outsourcing leaders to evaluate RPA as an alternative business model. Companies report increased competition from those that have shifted some parts of their business using RPA capabilities. The bottom line is that RPA will change the dynamics of the outsourcing industry, and outsourcing providers will have to carefully consider the following questions:

1. Do you have the automation capabilities to take on increasing competition?
2. How do you make yourself relevant to your clients in the new era of RSA?
3. How do you retrain your workforce to take advantage of the increased capacity without reducing staff?

Stay tuned

The European Parliament has proposed the drafting of rules that provide legal status for robots. Stay tuned to the CIO’s bag of tricks for a detailed look into the regulation and its implications.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

By Ellis Lindsay, Senior Principal, IoT Strategy, Nokia.

The internet of things (IoT) is complex with many participants and stakeholders. Conversations about the IoT are increasingly about security, but rarely are they about a bigger and much more important topic: trust.

Why is trust so big and important? Because it is the foundation of success for all systems. Organizations, products, societies, governments and kingdoms have risen and fallen because of trust. For the IoT to become truly secure and effective, all of the different entities involved, including end users, must trust the IoT as a whole.

Key challenges to trust

There are three significant challenges to trust in an IoT environment:

1. Devices tend to become more vulnerable to security threats over time

IoT devices are typically designed with specific purposes in mind and are in service for several years. Security threats evolve and become more sophisticated over time, and do not have any sort of permanence in the physical world. Device software is not always updated once threats and defects are identified, which leaves them vulnerable to attack. At the same time, decommissioning and replacing IoT devices is out of the question as they are usually deployed in environments like factories or on another larger device, where long life is paramount.

2. Signaling storms are becoming increasingly frequent

A signaling storm is when the application server or device is overwhelmed with requests. This disrupts smooth service and prevents the device or application from communicating effectively with the server.

3. Antivirus software is generally not viable

IoT devices are generally low-powered and cannot accommodate antivirus software that would provide an extra layer of protection.

For the IoT to flourish, it needs to evolve into the “internet of trusted things” and, eventually, into the “internet of trust.” To build this ecosystem of trust, our focus should widen to include the broader relationships between devices, services and the identities that interact with them.

The trust ecosystem in individual interactions

For an end user to assume a high degree of trust, there are many layers at work. Take online banking for example: a secure HTTP connection, identity verification and login requirements, network monitoring and other back-end activities all help ensure secure communication.

IoT players can learn from this to build end-user trust in the IoT:

1. Employing networking and endpoint security

Encryption using public key infrastructure or certificates can help make connections more secure. Evolving to chip-level, rather than software-level, cryptography can also help ensure that a device is less susceptible to being compromised. Deploying solutions over a managed or private network is also an option for end to end solution deployment.

2. Leveraging behavioral analytics and machine learning

Having knowledge of what the network traffic is and what it should look like is vital. For example, there are products that can identify malware behaviors when they are trying to attack equipment or other users in the network, by identifying these behaviors, malicious communication between the affected device and the service can be isolated. Using machine learning to recognize the communication patterns of viruses and threats, it is possible to perform behavioral analytics for threat and anomaly detection. Identifying anomalies can help flag connections, isolate devices, identify sources of threats and ultimately help increase the level of trust at the network communication level before it escalates to the application level.

3. Improving device and identity management

Most IoT devices are unmanaged today, leaving them vulnerable. An unmanaged device is essentially an unknown device and thus inherently not trustworthy. Determining the unique identity of a device before it is deployed enables management and tracking of changes. Coupling device management with the identities of software services and authorized end users creates a trusted relationship between device and service. Only when the identity of a device is known can it be looked at specifically to identify issues related to performance and behavior.

Establishing trusted relationships between device and application through a device management function helps enhance the value of the application. In many cases, devices are simply collecting data. Robust device management helps enable the decoupling of devices from applications so that best practices can be maintained at scale, without impacting the application itself.

4. Leveraging intelligent end points

As processor costs decrease and their capabilities increase, there is an opportunity to embed more intelligence such as security analytics into the end points. Developers could include security as part of the requirements from the beginning and install more capable software on devices.

Building an internet of trust can’t happen at the press of a button. No one organization can make it happen either – we have to work together. However, as we build the internet of trust, the organization that can deploy security capabilities at multiple levels to ensure trust among devices, applications and identities will likely have a higher level of success.

More than that, in the near future, trust could become the competitive differentiator for companies in the IoT world.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

By Christian Mertin, Global Business Services Leader, Advisory Services, EY.

Imagine your organization in 2025: your purpose and your Global Business Services (GBS) have intertwined and evolved together to drive stakeholder value. With a robust governance model in place, GBS has become a key pillar of the organization; it is the main keeper of processes, data and technology, and hothouses innovation and talent.

Software robots now handle every back-office process, and self-learning cognitive computers answer customer queries, support business areas such as marketing and make mission-critical decisions.

This leaves your human employees to focus on value-adding, relationship-based and strategic activities, always working closely with technology. New career paths have opened and in-house talent pools are continually developed and upskilled.

The transformation has achieved enormous efficiency, harnessed automation and innovation, and enabled your organization to pursue ever-greater value — both financially and emotionally. You have leaner cost structures, greater competitiveness and agility, and more control over disruption. Your customers (whether employees, consumers or clients) enjoy exceptional service — deeper relationships, seamless interfaces and faster workflow.

Trend #1: Global integration — a single superorganization

Organizations of all kinds can now behave just like start-ups. This is because GBS has delivered on all its promises of efficiency: processes, governance and IT architecture are clearly defined and standardized globally, so companies can operate in a more agile way.

Process design is going beyond end-to-end, and now addresses how people engage with the organization one-to-one, drawing on a vast catalog of services. Global IT architecture is integrated across an ecosystem of specialist suppliers, and a constantly evolving set of rules governs how technology and processes combine to deliver a consistent experience to customers, wherever they are.

Within a new “hybrid” operating model, the balance of captive and outsourced services shifts constantly according to demand; in some places outsourcing is disappearing as intercompany sourcing models are established and technology enables every process to be brought in-house or in-country.

Trend #2: Interaction technology — rewriting the DNA of future businesses

Cumbersome ERP dinosaurs are a distant memory — organizations no longer spend years and fortunes on vast platform integrations that are obsolete by the time they’re deployed. Instead, plug-in robotics solutions have acted as middleware to fill the gaps where legacy platforms and technologies are not delivering, on the way to a completely new bespoke platform that can constantly evolve. There is a proliferation of technology vendors all vying for the opportunity to co-create new and agile outcome-based solutions to build into overall platform.

All this technology resides in the cloud, the primary data and communications infrastructure. The cloud enables intelligent, actionable realtime analytics and bespoke plugin apps that help increase agility. It also enables mobility in every area of operations — customers and employees can complete any process or transaction seamlessly via a mobile device, creating value through convenience.

Trend #3: Automation and innovation — performance through perfecting processes

Every process that can be automated, has been: transactions are fulfilled without touch; tax returns are completed automatically; audit robots analyze complete sets of corporate data. As software robots have become even more advanced and, at last, trusted, organizations are deploying them extensively to support higher-value activities. In this way, cognitive technology and the automation of everything is the foundation for efficiency.

Disruptive trends can be predicted and harnessed because GBS is setting up experience centers with co-innovators to test new methods and approaches. This is critical particularly for older organizations, competing against “born digital” emergent disruptors.

Trend #4: Intelligent analytics — from decision support to decision-making

Intelligent analytics continuously feed self-learning computers: and AI is capable of making huge decisions by mining vast oceans of data and considering millions of possible outcomes.

That data is coming from transactions, blockchain, social media, the IoT and elsewhere in the ecosystem — and it’s all housed in GBS. As the data gets deeper and more disparate, real-time analytics as a service is pulling out new insights and make them instantly actionable. Decisions are made faster than ever before — and with certainty.

Trend #5: Customer — seeing the individual in the crowds

By showing the wider enterprise how customers can be uniquely served, and the value this creates, GBS has redefined CRM for the whole organization.

Customer experience — whether for consumer, client, employee or candidate — is now a key differentiator and driver of value for organizations. Individualization is key: everyone is understood and their needs met, interfacing via their own preferred channels. The fully integrated digital GBS enables consistent transparency in customer interactions, anywhere in the world, and employees can leverage automation and cognitive computing to nurture more attuned individual customer relationships.

Trend #6: People — still a valuable resource

GBS is the hothouse for talent, finding and developing people, particularly in emerging markets, to bring into the main organization. This is redesigning career paths — rotational trainee programs include extensive GBS exposure, and future leaders are forged in its agile, innovative environment.

Widespread automation is, contrary to initial doubts, actually creating more value for employees: it is facilitating a focus on accelerated, high-value work. The ecosystem model is the norm, so collaboration and networking is key; where there’s chemistry there’s differentiation. And there’s satisfaction too: an end-to-end view of the organization’s processes and performance helps employees see the importance of their own role.

Trend #7: Acute awareness — risk and cybersecurity as daily considerations

The most successful companies, those fully embracing the potential of GBS, get these fundamentals right from day one: all areas of risk (including digital, cyber and financial) are daily considerations. These companies take huge risks in their strategies — but decisions that are driven by intelligent analytics, and made by cognitive computers, are much less “risky”. However, organizations like this are mostly virtual, so security demands the tightest cybersecurity. Accordingly, GBS centers of excellence are developing security bots capable of automatically testing every fiber of the cloud infrastructure for vulnerabilities without downtime.

Making this future happen

This is how EY sees the future of GBS: a highly effective, bespoke combination of automation, innovation, efficiency and technology, all tied together with human connections and insight, driving unique streams of value for the organization.

So how can you make this future happen?

There are three overarching steps in the GBS evolution:

• Address maximum efficiency
•  Embrace automation and innovation
•  Drive value

The journey firstly achieves maximum efficiency, and via automation and innovation drives the organization’s activities ever-further up the value chain — taking processes, technology and people with it.

However, there are no shortcuts. While it’s not a linear A to B journey, each step has to be taken to yield all the potential benefits.

From the very beginning of the digital- and value-based transformation that GBS will drive, governance will be critical: organizations must map out SLAs and establish disciplined reporting lines for processes at the design stage so that GBS governance covers the whole organization; creating a GBS board that represents every function will avoid complications and enable GBS to focus on value-adding strategy.

And once again the key ingredient will be people: the transformation will rely on a strong corporate mandate, effective change management and collaboration. Working toward people excellence will become critical as roles (C-suite included) will be repurposed to become more business-focused.

There will be challenges, without a doubt. But they will be worth it. And it’s now up to organizations to define their own transformation and yield their advantage — before their competitors do.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

By Axel Preiss, Global Oil&Gas Leader Advisory, EY.

In recent years, technology has helped oil and gas (O&G) companies explore deeper waters, remote reservoirs and extract from shale resources. The success of this technological advancement has created an oversupply which is responsible for lower crude prices, and now O&G companies are aggressively pursuing capital and operational efficiency to reduce costs.

Most of the innovation in the O&G sector is focused at the well site – but digital technology can also help transform business operations and deliver sustained value to the bottom line, for example by connecting disparate operations across organizations, harnessing data via the industrial internet of things (IoT), and sharing that data to improve decision-making.

In the short term, digital technology has significant potential for compressing across siloed operational supply chain processes, bringing together producers and consumers (whether internal or external) of common sources of information to create greater operational efficiency. The broader these networks, the greater the potential.

Process compression combines three foundational digital capabilities: smart assets, paperless processes and data analytics — all in a secure environment. When applied across the O&G supply chain, digital technology can simplify and synchronize processes, and speed up integrated decision-making.

Opportunities for process compression

1. Topside production optimization: Topside production can be improved at lower cost with better connectivity between data sources and physical locations to drive more informed, fact-based decisions.
2. Predictive maintenance and repair: Reactive decisions on production assets can lead to overspend in maintenance, duplication of inventory and inefficient allocation of resources. Today, tracking devices on inventory and asset management applications can help synchronize tool delivery and management with preventive maintenance schedules.
3. Logistics and warehousing: Tracking the equipment and resources that serve critical assets across all sites, and checking their connection to business processes, is critical to improving production.
4. Integrated planning and execution: Digital toolsets such as process collaboration and analytics can enable cross-functional understanding and collaborative decision-making.
5. Digital finance transformation: Digital solutions can combine mobile, cloud and finance systems to automate invoicing in the field. Real-time views of back-office activities and spend can also increase accuracy and timeliness in billing.

These compressed processes can not only drive down operational costs, but also position early-adopter organizations for the future in their ability to attract and retain the next generation of digital-native talent. On a wider note, change initiatives and technologies also offer another layer of benefit: improved safety for workers.

CIOs are increasingly involved in business decisions, and for innovative CIOs in the O&G industry there is a great opportunity. Technology is often the key, and sometimes the only, competitive differentiator in this industry, and CIOs can step up to claim greater responsibility – shifting from running IT to driving innovation, and encouraging the relentless pursuit of optimization.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

By Chris Mazzei, Global Chief Analytics Officer and Emerging Technology Leader, Ernst & Young LLP.

Data and analytics are not new to most of us. Large enterprises have always invested in data and analytics to improve understanding of customers and markets, and enable better decision-making. What’s different now? Well, the stakes are higher. And in this era of disruption, leaders aren’t using advanced analytics to simply improve existing activities. Strategic use of data is transforming traditional process-driven organizations to help them become more competitive, increase revenues and profits, reduce risk, and guide them to new initiatives.

What about organizations that are less mature in their use of analytics? They risk getting disrupted by the “2.0 organizations” of the information age that use data as a strategic asset. Companies have moved from pilot projects that originated in business units or countries to using data and advanced analytics at an enterprise-level to rethink and reimagine their entire business to identify new opportunities.

In new research by EY and Forbes Insights, more than 1500 C-suite executives were surveyed from Asia-Pacific; Europe Middle East India and Africa (EMEIA); and the Americas in August and September 2016. The respondents’ companies had at least US$500 million in annual revenues and 21% had revenues greater than $50 billion. Represented industries included technology, energy, pharmaceuticals, health care, financial services, manufacturing, consumer products, and government.

What drives success with analytics?

• Boosting investment doesn’t always translate to success: Findings from the survey show that simply boosting investment doesn’t necessarily lead to better outcomes. In fact, many large enterprises throughout the world still struggle to achieve the promise of today’s analytics capabilities. In between organizations moving from identifying new business opportunities, acting on insights and measuring the outcomes of their data-driven strategies, fundamental problems crop up. We’ve split this analytics journey into the five synapses that are represented below:

• Advanced analytics drives double digit growth: The flow of information between these synapses isn’t always smooth. And the way global enterprises handle these junctures can translate into business success and help define clear stratifications in analytics maturity. In fact, nearly two-thirds of companies with well-established advanced analytics strategies report operating margins and revenues of 15% or more, as well as improved risk profiles.

• Other benefits of leveraging advanced analytics: Sophisticated users of advanced analytics also experience additional benefits. Seventy percent of top performers have used advanced analytics to overhaul business strategies and update how they compete in their respective markets. Seventy-five percent operate a full range of enterprise, departmental and line-of-business analytics groups within a well-aligned framework.

These sophisticated users can capitalize on robotic process automation, artificial intelligence and other forms of predictive and prescriptive modeling for insights about possible future outcomes and ways to address them. Because advanced users of analytics incorporate them early in the business development processes, they can deliver better outcomes by shaping initiatives based on actual data rather than gut instinct. Leaders can more accurately measure business value to demonstrate the impact – and validity – of their investments in advanced analytics.

Download the survey here.

Becoming a leader in advanced analytics
So, what can you do to better leverage advanced analytics to drive business benefits? Here are a few recommendations :

• Understand the opportunities and risks associated with the synapses listed earlier. Also understand how each threat is impacting your own organizations. This requires a detailed assessment of the processes used when formulating data-driven strategies, an honest review of key analytics capability maturity and a plan for closing any gaps.

• Pay particular attention to overarching themes that emerged in the survey results. For example, a common pain point is the lack of collaboration among business units and analytics specialists when defining desired outcomes, designing operational models and measuring the results. Without this cross-department cooperation, the goal of turning analytical insights into action can break down at any stage of the process. This was particularly apparent at the synapse stage of Initiative Design (defining the specific activities and projects that will achieve desired business outcomes). For example, while 71% of CIOs/CTOs and 67% of CEOs/Presidents/COOs believe there is a high level of effectiveness among business users and technical people, department managers aren’t nearly as upbeat. Just 46% of chief financial officers, 43% of chief analytics officers and 37% of chief risk officers agree with that assessment. The divergence in rankings below the CEO level illustrates the difference between vision and reality – while everyone may share a desire to use data and advanced analytics effectively, people who actually tap the resource to do their jobs develop a keener awareness of where the gaps lie. These results also suggest that those who are frustrated by the level of effectiveness could do a better job of communicating this, and proposing solutions, to top leaders.

• Apply best practices specific to each synapse. This is to avoid common stumbling blocks and set a clear path to deriving immediate and long-term value from data initiatives. We dissect survey findings to reveal important distinctions between best-in-class companies and their emerging peers across industry sectors, geographical regions, and functional departments.

The survey results have also been used to create a four-stage maturity rankings to show trends among respondents that are leaders, challengers, developing companies and lagging organizations. These rankings highlight success factors in each of the five synapse categories and pinpoint what it takes to become an analytics leader. We hope you find the report insightful.

In a world of technology disruption, , leaders in advanced analytics are not merely trying to improve current processes; they are also trying to address what to sell, how to sell, who to sell to and how to outsmart competition. That requires using data and analytics at each step of the maturity cycle and ensuring that the process continually evolves and improves over time. Those that are not making progress quickly enough are at an increased risk of falling behind both current competitors and emerging players that were “born” digital with data and analytics at the center of their strategy.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

By Beatriz Sanz Saiz, EY – Global Advisory Analytics Leader.

When I heard that the theme for International Women’s Day 2017 was #BeBoldForChange, I was really excited. I passionately believe that by living bolder lives, we can make bigger, faster changes to gender parity — and in tech, this can’t come soon enough.

Whichever way you look at the statistics, the news isn’t good: women make up only 30% of the seven million people working in Europe’s technology sector, and they are especially under-represented in decision-making positions.¹ In the US, only 26% of the computing workforce is female — a drop from 36% in 1991.² And in the UK, women hold only 17% of technology jobs — with only 1 in 10 in leadership positions.³

Role models are critical if change is to happen. Women and girls around the world need to know what is possible so that they can fulfil their potential in the workplace.

That is why those of us who can live boldly must do so. We can’t afford to worry about what others will think of our choices or conform to the status quo in case we are chastised for speaking out. We must share our experiences — as I’m about to do here — and encourage others to share their experiences also.

For me, living a bold life means many things. Firstly, it means being able to say no. I made a decision to say no when there are unnecessary demands that would reduce the quantity and quality of time that I spend with my children. Undoubtedly, I take quite seriously the responsibilities that come with my role as EY’s Global Data Analytics Leader. But I am also a single mother, and there are precious few things that so important that they stop me reading bedtime stories to my boys.

Being bold also means stretching yourself and facing challenges head-on. After leaving school, I combined a Master’s degree in Mathematics with an economics course. At the same time, I was working part-time in IT support with the technology company Front Porch Digital, which was later acquired by Oracle. Was it very hard work? Yes, of course. But the statistical analysis skills that I developed at that time set me on the path to where I am today.

Another important part of being bold is refusing to let your age, gender, sexuality or background get in the way of what you want to do. When I was an executive at Santander, I was often the only woman at meetings, but I didn’t let that bother me. I became EY’s youngest Spanish partner after I built up a new Advisory service based on advanced customer management. I believe you are never too young, too old or too different to achieve your goals.

Then, of course, there is a link between boldness and new ideas. Only by being bold will you expand the boundaries of life. In my role at EY, I have brought together a really diverse group of people — a global team of leading class data scientists, and business and technology professionals — to create one of the organization’s most dynamic innovation centres, which is located in Madrid. And here we are now using artificial intelligence to help our clients accelerate down the path of becoming smart, data-driven companies, with immersive journeys designed to stimulate conversations and show clients how they can enhance their businesses through data and innovation.

Finally, being bold means being bold for others. It means being a coach, a mentor, a leader and a visionary. It means making a difference to the world. So I set up a car sales business in Ethiopia that helps more women in that country to go to university, by giving them financial support and work experience. To date, more than 20 women have benefited from the not-for-profit scheme.

In support of International Women’s Day 2017, I would conclude: whatever your role in technology — or, in fact, any sector — we, as women, should be the role models we want to see. And that means living boldly all the time — starting now.

EY member firms are committed to supporting the achievement of gender parity. Find out more about the activities of our #WomenFastForward platform.

References:
¹Where are the women in tech? 3 charts that reveal the gender gap”, World Economic Forum website: weforum.org/agenda/2016/04/where-are-the-women-in-computing/
²“Balancing the genders in STEM”, Women in Technology blog, womenintechnology.org/index.php?option=com_dailyplanetblog&view=entry&year=2016&month=05&day=25&id=1:balancing-the-genders-in-stem
³“Gender equality in the tech sector will benefit the global economy”, Financial Times, ft.com/content/e2f8ad0a-bdd6-11e5-9fdb-87b8d15baec2

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

Ask most individuals about farming and they will describe a farmer with an old tractor and plow. They are often surprised to know that modern agriculture is highly technical. For example, the first commercial drone license issued by the US’s Federal Aviation Association (FAA) was to a company for agricultural application.

The agriculture industry has begun to harness the power big data can bring to operations. For example:

• Historical yield, soil, nutrient and weather data can be analyzed to help a producer make a decision on how much fertilizer to apply to a certain soil type in specific climates.
• An agronomist can analyze field data to recommend the optimal seed that a producer should purchase.

Software and algorithms are being created to leverage data to increase yields, improve farm profitability and increase farm sustainability. Investors are taking note.

Over US$4.6 billion has been invested in agriculture technology during 2015. Much of this investment is in software and technology to enable digital agriculture — a combination of data and algorithms that provides specific recommendations at the sub-field level. For example, whereas today most farmers make a decision to plant one seed variety on an entire 40 acre field, digital agriculture allows farmers to identify and plant the “optimal seed variety” in every square meter of the field.

The promise to feed a growing world sustainably

By 2050, the global population is expected to increase by 40% to 9.6 billion people. In order to feed this drastically increasing population, the agriculture industry will need to produce 70% more food while only using 5% more land.

Current production rates and distribution methods will not be nearly enough to feed the population. It is generally acknowledged that digital agriculture and big data will be needed to meet these demands.

The impact of digital agriculture on the field is well-documented and researched. In the future, data creation, analysis and decision-making will almost certainly increase at the field level. Farming operations will have the opportunity to prosper from targeted field solutions, data-driven agronomic advice and smarter inputs. Software is being developed to help propel developing countries toward modern farming practices. Farms are consolidating at an increasing rate as technology supports automation and economies of scale. Input applications are based on factual data and investments into farming tech are funded by profit saved by data-driven efficiency.

Great promise, but significant challenges too

While the benefits of digital agriculture are compelling, it has also been met by significant challenges:

• Difficulty using software, data usage, disparate and propriety data formats and an unclear return on investment are concerns.
• Agribusiness has struggled to provide immediate, tangible results from digital agriculture equipment and software.
• Challenges around gathering and standardizing data make adoption difficult across all stakeholder groups.
• Undeveloped countries lag in adoption with weak network infrastructure and limited capital.
• The gap between modern, advanced farming and subsistence farming is growing at an alarming rate

All of these factors raise important questions for the industry. Producers face problems and decisions every day, both on and off the field. These decisions are projected and magnified up and down the entire value chain — from field to fork.

How is digital agriculture changing agribusiness

While the first two revolutions in agriculture — mechanization and biotech — had a major impact for farmers and select agribusinesses, digital agriculture (the third agricultural revolution or Ag 3.0) will fundamentally transform every part of the agribusiness value chain.

It will affect producer buying behavior, and seed and equipment product design, and could enable dynamic pricing at the consumer retail level. These implications will gradually affect multiple business functions across a single company. For example, digital agriculture and big data will change the way seed and agrichemical companies market, price and sell products, select and invest in their R&D pipeline, recommend and technically support product sales, manufacture and distribute products, and manage credit and financial risk. Business strategy, product design, customer preferences and even organizational structure will change as more digital agriculture data is available.

This revolution will also challenge traditional company roles, intercompany relationships, reward systems and, potentially, entire business models. Digital agriculture is creating competition among both traditional and nontraditional competitors. The industry is in a storming phase and agribusinesses are working to solidify their place in Ag 3.0. Several companies are investing heavily in internal data activities such as standardization, storage, software and analytics. Others are focusing on outsourcing strategies or licensing software from other companies. Still others are taking a wait-and-see approach.

As the industry evolves, disruption will follow. It is essential for agribusinesses to transform their business and themselves to differentiate and provide more value to customers. Although the challenges are concerning, they present thought-provoking opportunities to all stakeholder groups.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

Not many technologies have been embraced as quickly as analytics. Analytics has moved from helping us gain insights into past events, to real-time monitoring using dashboards, and now to predictive analytics that can forecast what can happen hours, days, weeks and months from now.

Many industrial environments are becoming so complex to manage that making decisions without analytics may jeopardize millions of dollars’ worth of products and services. Executives may continue to make gut-feel decisions, but taking into account the risk this might create, they’d also want to delegate some of the decision-making to analytics. Data and analytics are becoming common in health care, energy production and manufacturing. As a result, patient outcomes are improving, power generation is becoming more efficient, and manufacturers are improving product yield and quality.

As machine learning and predictive analytics become more widely used in commercial settings, new trends are emerging. Predictive analytics is now accessible to people not traditionally engaged with analytics, such as those who manage shop floors and shipping docks. This is giving rise to operational analytics, which answers questions like, “What is actually happening on my production line now and what does this mean for us in the near future?” This new segment of analytics users is trying to figure out how to work smarter and act proactively.

Translating insights into actions and value
So, what can industrial users of analytics do to more quickly see value from their investments in analytics? When we begin working with a new customer, we immediately determine what the high-value question is that they want to answer. For example, how to extract more oil from a certain location? Or how to care for more patients in a hospital with the same staffing levels? If they have the data to help answer those questions, we then need to get busy building the right analytics.

We’re at a very nascent stage of analytics adoptions. And companies are willing to adopt analytics only after their peers or vendors can demonstrate results. This growing body of results will lead over the next two to three years to the broad expansion of industrial predictive analytics.

If companies are convinced with the results demonstrated at this stage and decide to make an investment in predictive analytics, they’d also want to turn insights into action. The best place to start is with a persona and decision inventory — who is making the decisions, what are the types of decisions you’re currently making and which of these are the truly high-value ones. As you work through this auditing process, you’ll identify the best places to focus your analytics efforts.

It’s also important to avoid a big bang approach to analytics. For example, instead of spending a lot of time in the data gathering before proof of concept, bits of data could be pulled out for use in rapid prototyping. This data could be read to identify if there is value and to check whether more data has to be pulled for more value. Starting small and prototyping your way to the right data sets help you find high-value answers and quickly deliver value to the organization. The secret of getting ahead, after all, is getting started.

Learn more about the EY Forbes Insights survey and GE point of view.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

Data volumes in global enterprises are growing exponentially as workloads move to the cloud, IoT brings in big data through sensors, and other digital trends. In the past, we may have analyzed spreadsheets with 30 columns of data, but now, making sense of all the available data to inform new business initiatives has become daunting.

Here are five questions you might find yourself asking while on your journey to analytics maturity:

Does analytics mark the end of the gut feeling?

Though daunting, making sense of huge volumes of data is lucrative. Companies that use data to inform decisions significantly outperform peers. However, a good number of decision makers still do not take full advantage of data to drive business decisions and instead continue to rely on gut feeling and recent anecdotes. Experience-based intuition is definitely valuable, but undervaluing the power of analytics is courting disaster. Analytics provides a more complete answer than prevailing as­sumptions and business truisms.

For example, sales vice presidents may answer that the difference between winning or losing accounts hinges on having experienced sales reps. That assumption has an element of truth to it, so you are likely to find some data that correlates to it. But other factors also matter, including language, market, channel, industry and competitors. There are patterns uncovering why com­panies win or lose, and executives must be open to using tools that can pattern data for a complete answer to their business question.

Whose yard is analytics — IT’s or the business’s?

In the past, IT would manage large volumes of data and use it to serve up reports to the business. But over time, business users began saying, “We have expertise in our departments, just give us the data, and we’ll analyze the data ourselves.” As a result, fric­tion can be created when IT tries to control the data but the business wants open access. A better alternative is what we can call the “shop for data” paradigm. Business users have access to governed data managed by IT, can easily join it with any outside data they choose, and then explore it for business insights. What’s important is it’s always clear what was the en­terprise data and what was outside data, so when people make decisions based on the data, they always know where the data came from.

Is my enterprise’s data enough?

The signals that underlie business performance may not all be in enterprise data, but rather in market data like social media or even a combination of enterprise and market data. One example arises if a competitor has a large product recall. That event may not register in your internal data, but it will likely be no­ticeable in social media, where people express their dissatisfaction with the product and their intent to purchase another product. Weather data is another good example. If a big snowstorm is ap­proaching and you’re a car rental business, you’ll want to combine current and historical weather data to anticipate customer behav­ior and the corresponding impact on fleet management.

Who will make more decisions — humans or machines?

People and machines handle 40% of strategic decisions today. This mix will vary on the basis of individual business needs. For example, ma­chines have become quite accurate at identifying suspicious activity on credit cards. Fraud detection models are constantly being improved, thanks in part to advancements in machine learning. Models become smarter and smarter all the time, and they’re at the point where machines can act on their own.

But that won’t be true in other areas, such as healthcare. For example, doctors can work closely with machines when a pa­tient presents with a rare disease. The machine can process all the relevant data worldwide — something that’s impossible for a person to do as quickly — and bring the results to the doctor. The doctor would combine that information with his or her own in­sights about the patient and devise an appropriate treatment.

Solve problems or look for patterns?

With traditional predictive analytics tools, business users typically came to the data science team with a specific question and request­ed a piece of analysis that presented an answer, such as use cases around fraud, customer churn or employee attrition. In other words, shining the spotlight on a business problem. That is bene­ficial, but what’s even more valuable with all the new data is turning on the stadium lights and illuminating your business to find every­thing that’s interesting and has a pattern. There may be areas where no one’s looking for insights about improving product quality or better segmenting and targeting customers. Companies must use new and smarter analytical tools to both narrow in and zoom out of the data to influence decisions.

Learn more about the EY Forbes Insights survey and IBM point of view.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

For industrial and manufacturing companies particularly, the Internet of Things (IoT) presents enormous opportunities — but also substantial challenges to integration with the siloed worlds of Information Technology (IT) and Operational Technology (OT).

In the past, IT systems tended to exist only on mainframes and servers that were locked inside organizations, based on a client server architecture. As a result, they were relatively easy to protect and CIOs focused primarily on the confidentiality of the information those systems contained.

In sectors such as manufacturing or power and utilities, heavy Operational Technology (OT) such as oil rigs, pipelines, mills, energy grids, factories and power stations existed in parallel with IT and OT considerations centered on the availability of service.

Bringing IT and OT together is a complex proposition, and potentially contradictory, as each has different priorities: availability leads to a deprioritization of security, and with IoT the focus needs to be on confidentiality of all the data generated and transmitted — particularly acute now that previously closed systems are open to new vulnerabilities online.

Opening up to the world

Today, OT ranging from production lines to pipelines as well as many finished products — have become IoT-enabled. In the new IoT landscape, virtually everything has, or can have, sensors on it. The value is locked in the integrity of the data coming from those sensors — feeding into analytics and offering the potential to infer trends, reduce operating costs, provide new and better services to customers, predict maintenance needs, and manage stock levels.

But IT, that supports OT, has previously been isolated. In the IoT era, IT and OT are now becoming connected to the internet. This opens up many new opportunities: at one end, to reimagine the role and effectiveness of hardware and machinery, and at the other to rethink entire business models and move ever closer to customers.

So, the challenge facing CIOs, who oversee IT, OT and IoT, is in bringing these discrete worlds together in a way that maintains an acceptable level of integrity and confidentiality without compromising physical safety.

Balancing priorities and considerations

In the OT world, there has historically been a focus on a culture of safety, how to respond to a crisis and the preservation of life. Because of the physical controlled access to a site, once inside the boundary, confidentiality was not prioritized — in an isolated or secured site, everyone who is there is supposed to be there. For example, operatives on an offshore oil rig may have shared logon credentials — it being more important to enable them to run the platform than having unique, frequently updated passwords. Contrast this to an office environment where people come and go, and screens (and the information they display) are visible to anyone who walks by — confidentiality is the priority in this IT context.

But, in an IoT context, remote OT sites no longer need to have engineers present — remote staff or even AI could log on and carry out tasks instead. Logon credentials, being exposed to the internet, are suddenly much more vulnerable to being accessed by a third party. And if there is a weak link in the security chain, the IoT can present real risks to human life.

Conclusion

As the IT, OT and IoT worlds increasingly overlap, it’s essential for CIOs to consider how best to balance the desire to harness IoT potential with the need to protect systems and the people that use them.

It’s up to CIOs to think about how to bring the worlds together effectively without creating yet more risk.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

The internet of things (IoT) is unexpectedly forcing an intersection between Information Technology (IT) and Operational Technology systems and the internet. And organizations need to address it now.

With IT systems coming out of the back rooms, and OT hardware being fitted with internet-connected sensors, the IoT is pushing together what were completely siloed technology layers and launching them into the online world.

Of course, that is not necessarily any organization’s intention – but the intersection of these layers poses serious potential risks to almost everyone.

For the CIO, addressing this should be an urgent priority.

How should organizations address these layers?

As most CIOs know, cybersecurity should never be a bolt-on – it needs to be embedded in daily operations. However, the intersection of IT, OT and IoT needs extra care and attention to get there.

EY recommends one of two approaches.

The top-down approach

The digital revolution is causing the technology and operations of numerous different organizational siloes to be pushed together – but the organizational structure is often not keeping pace, so may need to be reengineered.

Organizations by and large are well practiced at managing the information systems and health and safety procedures of the pre-IoT era. Today, many organizations have innovation officers to develop new IoT-driven revenue streams, officers who look after the physical safety of operatives in the industrial environment, and the CIO or CISO whose role was to protect systems.

But because all these responsibilities are now being mixed together, the CIO needs to coordinate and oversee the transformation to a state where the organization can drive innovation without opening itself to cyber risks.

CIOs can begin by conducting a cybersecurity maturity assessment across a number of domains including:

• Governance and organization
• Awareness
• Asset management
• Data protection
• Identity and access management

This top-down view can help identify the weakest link in operations and begin to redefine the policies, standards, procedures and governance of the entire organization.

The bottom-up approach

This approach involves a risk-based assessment of current operations, asking: what vulnerabilities exist and can be exploited? What is the potential business impact? And what can we do to manage them?

With IoT technology already installed, CIOs can create a timetable for regular security testing across all technology layers – from sensors to analytics to applications, whether whole products or individual components.

The bottom-up approach may also cover:

• The use of secure code from code libraries
• Regular patching of the firmware
• Sourcing technology from reputable companies
• Passing security requirements to suppliers and third parties all the way down supply chains

Testing of IoT technology in live production OT environments is very different to traditional enterprise IT. Often, when new vulnerabilities are identified they cannot be fixed right away, but need careful management until a scheduled maintenance window so downtime is minimized.

What is the imperative?

CIOs need to take the initiative to secure their technology layers to mitigate risks.

In the very near future, General Data Protection Regulation (GDPR) harmonization in the EU will require organizations to handle data securely – and set fines of up to 4% of gross turnover for non-compliance.

Currently there is no equivalent of universal health and safety standards for IoT, and information security standards for IoT design, build and operate are still several years away.

Conclusion

CIOs can address the security issues resulting from IoT:

• Examine whether they are considering risks from across their entire network
• Consciously integrate IT, OT and IoT
• Manage the governance of all three layers

By not thinking about the security flipside of innovation and digital, CIOs may be at best missing out on an opportunity – and at worse needlessly opening their organizations to risk.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

Ransomware attacks are a reality, and they are happening more and more often: the FBI estimate 4.000 ransom attacks per day!Organizations of all kinds are becoming targets for this form of cyber threat. So what are the risks, and how can CIOs mitigate and respond to them? And what even are the demands of ransomware hackers?

Ransomware is exactly what it sounds like: Malware used by cyber attackers who demand a ransom for the restoration of the data or service it threatens. Some ransomware is capable to encrypt 100.000 files in under 2 minutes.

Over the last year, attackers have targeted the industries that have been more likely to pay up. These are primarily health care, education, government organizations, critical infrastructure and small businesses.

That said, all industries are under attack, with the mechanical and industrial engineering industry suffering, on average, 15% of ransomware hits, pharmaceutical and financial services 13%, and real estate 12%.*

“Locky” was the most deployed ransomware in 2016. The malware is distributed using spam emails in which an invoice is presented. If the file is opened, the reader is asked to enable macros which then encrypts files and locks up the system. A bitcoin ransom amount is then demanded to decrypt the data. Locky alone was responsible for more than US$500m in losses in 2016.*

How could it evolve?
Ransomware attacks have increased 170 times year-on-year in the period from 2014 to 2016. This growth trajectory will lead to an estimated US$1b in global losses in 2017.*

Since it is easy to remain anonymous and buy ransomware services, it requires little effort and presents a very low risk for attackers to conduct operations — vastly increasing the risk of the frequency and number of attacks.

These attacks can have a devastating impact on businesses. EY research indicates that only 42% of companies are able to recover their data fully from their backup systems. The actual ransom money paid is only a small portion of the total costs companies have to incur to overcome the damage that is done. One also has to factor in other costs, such as the response team, stabilization and restoration efforts, and enhancements to the cybersecurity framework to prevent future attacks.

The execution model of ransomware attacks is evolving and has now reached a level of maturity. Over time, the model has incorporated innovations such as digital currencies — for example, ransom money can now be paid in bitcoin — and the introduction of ransomware-as-a-service (RaaS), which offers unlimited access to ransomware on the dark web for one bitcoin a year. New innovations are expected, especially related to attacks on internet of things (IoT) devices.

How can organizations strengthen their defenses?
We expect that companies will respond by putting more emphasis on backups that are isolated from the network and increasing user awareness around phishing emails and the use of USB sticks.

Equally important are training in good practices, building awareness around the threat and establishing a process to monitor, detect and report any suspicious activity that is noticed. Phishing emails are typically the primary attack vector, so deploying solutions that block these malicious emails and attachments is essential. Once on the network, an endpoint and network solution that detects ransomware behavior can limit the spread.

Recovery is equally important. Unfortunately, recovery policies are rarely tested. Restoring data is a very sensitive process, and a minor omission can have a far-reaching impact. And yes, user awareness and education programs are essential in making a difference.

How can organizations respond to ransomware demands?
Much depends on whether the organization has a recent backup of the affected data or not. It also depends on whether the backup itself has also been encrypted or deleted by the malware, and the thoroughness of that backup. Additionally, it depends on which part of the organization has been impacted by the attack — for example, whether it is in operations or in an area that includes sensitive data that requires reporting.

If it has no backup or the quality of backup is poor, an organization may consider paying the ransom. But, if it does decide to pay, it is definitely a case of “buyer beware.” Another aspect to consider is the fact that negotiations with attackers generally have mixed results. There is absolutely no guarantee that the data will then be “returned” — also, by paying once, an organization may become a more likely target for a follow-up attack.

Also, potential violation of sanctions established by the Office of Foreign Assets Control — such as inadvertently contributing to terrorist organizations — may present another unexpected risk.

If a reliable backup is in place, the organization needs to look immediately at how to refresh the systems, assess what needs to be replaced completely — whether that is hardware or software — and determine which stakeholders may have been affected by the attack. Appropriate communication with those stakeholders is then required, including any relevant regulators.

A cyber attack can happen to any organization in any industry. An organization’s network system can be infected with ransomware through even the slightest breach in security. As attackers find newer, foolproof ways to infiltrate systems, organizations can be prepared with the best defense: secure backup systems and strong malware detectors.

Protecting the organization from cyber attacks is crucial for the business. It can prevent financial damage and, most importantly, data loss.

*Source: 2017 SonicWall Annual Threat Report

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

Few things are as popular as a competitive differentiator as analytics is, especially in this age of disruption and breakneck competition. Advanced analytics can make the difference between which businesses are running live and which ones are running behind. Not all enterprises that use data and analytics to inform business decisions see the kind of success enjoyed by the high performers in this area. Less-mature analytics practitioners experience breakdowns at critical stages of the business initiative process, starting when they identify new business opportunities and model ways to act on insights to measuring the outcomes of their data-driven strategies. Incentives to correcting these problems are big — significant gains can be achieved in operating margins and revenues, and by improving risk profiles.

The democratization of predictive analytics
Traditional analytics has been successful in telling us what happens, but not so in showing us the reasons behind these events. Thanks to predictive analytics, this has started to change. Companies can now understand why something is happening and also what should be done to improve performance.

Traditionally, predictive analytics has been the data scientists’ forte. Now, however, it is embedded within traditional business intelligence applications, making it available to a broader set of business users. This is part of a larger trend — the blurring of lines between business intelligence, planning and predictive applications. Let’s take the example of planning. Once we create a plan, the next step is measuring that against actuals. In the past, this required a separate business intelligence tool. But today, capabilities have come together and end-to-end scenarios can be completed without having to switch tools.

In spite of the obvious benefits of analytics that have been discussed by many authors on this blog, it is surprising that many executives still rely on their gut instincts instead of data and analytics. But companies that use analytics extensively are outperforming their peers in terms of revenue generation, profits and market valuation. This makes it clear that our attitudes towards analytics need revisiting.

The journey to analytics success
One of the most important ways to achieve analytics maturity and success is to combine an enterprise-wide approach to data with resources that serve the unique needs of individual business units. Often, analytics starts at the business unit’s level – that is, when a department or team is trying to address a specific problem. It is important to address immediate department-level needs, but it is also important to ensure the approach can be scaled across the organization safely. Thus, the key to analytics maturity is moving from a federated approach to one that is organization-wide and benefits all business units.

Organizations also need close collaboration among business and technology leaders, and an enterprise strategy that is embraced by employees at all levels and departments. This enterprise strategy requires a portfolio of data and analytics technologies – for planning, data discovery and dashboards, predictive analytics, etc. It is also ideal if all of these components run on a single platform, so there is agility for individual use cases and also the trust in the data that comes from consistent management, security and governance.

To drive wider consumption and use of analytics for decision-making, a culture around analytics should be created. This starts with executive sponsorship and a business intelligence strategy that focuses on the objectives of the organization, the business needs and the desired business benefits. Once business priorities are defined, determine what KPIs are required to help you realize those goals, and the technology and organizational structures that will be needed to support it. And finally, measure everything to ensure you are on track and to help you “course correct” as needed.

For organizations aiming to achieve better analytics outcomes, here are five tips:

• Combine an enterprise-wide analytics strategy with resources for individual business units
• Bring together business and technical resources to define high-value analytics initiatives and develop project road maps
• Delineate clear milestones and track KPIs on the basis of business requirements, then continuously measure the success of analytics investments
• Choose integrated analytics platforms that address local and enterprise-wide requirements versus collections of point solutions
• Evaluate the cost-savings and advanced capabilities possible with cloud-based analytics options

To learn more about SAP’s alliance with EY and how you could use analytics to solve your most complex questions, visit EY at SAPPHIRENOW in Orlando. From 16–18 May, EY can be found in booth 511 and helps you to answer the question — “Is your business running live or running behind?” Learn more on ey.com/sap or follow @EY_SAP.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

Jonas Groes, Advisory Government and Public Sector Leader, Ernst & Young Global Limited interviews Jørgen Brolykke Rasmussen, Chief IT & Digitization Officer, Odense Kommune

Odense — one of the oldest cities in Denmark with proof of settlement dating back to over 4,000 years. It is the official bicycle capital of the country, and the birthplace of Hans Christian Anderson. With a rich mix of art, culture, tradition and the snazzy, Odense is picturesque, quaint, sprightly and contemporary — all at the same time.

To testament its willingness to stay current and adopt what is new and effective while still being rooted in its core values, the city of Odense recently launched an automation project. I had the chance recently to talk to Jørgen Brolykke about the city’s automation project.

Q: Government and public sector (GPS) bodies are often looked upon as old-fashioned. But projects like Odense’s transform the way people look at the sector. What prompted the city of Odense to consider robotic process automation (RPA)?

A: Government bodies are certainly keen on improving the experiences of citizens they interact with. And they do take action to improve these experiences. However, not all of these bodies might be open to leverage and adopt new and unfamiliar high-end technology like robotics. A certain degree of openness and courage is definitely required, and that degree of openness can add tremendous value. Now, we actually have the technology and software that can enable this value addition.

For the city of Odense, the objectives of turning to robotic automation were to:

• Improve efficiency and accuracy
• Save costs
• Reduce manual and repetitive tasks
• Give our employees the opportunity to work on creative, value-added tasks and also reduce their stress

Q: Whenever automation is discussed, some people get worried. There is often concern that automation might also result in headcount reduction. Did the city employees have similar concerns? If so, how were those addressed?

A: You are right. But, what we have understood from our experience with RPA is that the robots are more like colleagues to employees, than like replacements. The software bots take care of the repetitive, monotonous tasks while letting employees intervene where emotions, education and experience are required to take decisions. This creates a symbiotic environment where employees and software programs work hand in hand to power increased efficiency, cost savings, higher degrees of accuracy and reduced error. The time and cost we save can be directed toward other activities aimed at improving the lives of our citizens.

Q: How are things now after the RPA project? What difference has it made to the city and its people? What benefits have you drawn?

A: The benefits of RPA go beyond just cost and efficiency savings. Of course, cost and efficiency are important. What is, however, also important are speed, accuracy and safety. Where we brought in RPA, it has improved the security of our data and processes, increased the speed at which the citizens are provided information and service, and made our data accurate. This gives us some very tangible long-term benefits and also improves the experiences of the citizens that interact with us.

Q: What’s next for Odense? Are there more processes that you are looking to automate? Are there other technologies you are considering adopting?

A: There is a lot more processes in our different departments that we plan to automate. RPA is an important tool in our digital toolbox and our organization simply demands these new tools. In the next year, we also plan to get some experiences with machine learning in which we also see a lot of exciting possibilities.

Q: What advice would you give to governments that want to adopt RPA?

A: A key aspect of introducing automation into your processes is identifying the right processes that can be automated. Some processes and departments would have a higher level of automation than others. Different departments process different types of data and, therefore, we might not be able to automate similar processes across departments. Do not hesitate to get help if required.

In the face of increasing costs, governments, much like the private sector, should be open to thinking out of the box to come up with solutions that help them save costs and time. A certain degree of open-mindedness can go a long way. In the coming months and years, adopting latest technology will no longer be a value add, it will be the imperative.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

Launching an innovation program can be an energizing time for an enterprise. It can start in many different places and for many different reasons. With the proliferation of data and the diversity in education and experiences, innovation has become ubiquitous. In many cases, innovation “happens over there” by a few executives or teams, operating separately from the rest of the business.

At EY, I am responsible for facilitating innovation throughout our consulting business. As we formalized those efforts, the most common question we’d hear from our people was “How can I get involved with our innovation initiative?” The only answer we had at the moment was that “Unfortunately, you can’t unless you are part of the core team” … and we hated that answer! I’d leave those exchanges with a profound sense of guilt. Not only was this diluting their passion and interest, but we were missing out on the opportunity to harness this ambition and the potential contributions they could make. We needed to change our approach. Innovation is not something that can sit in a silo, it needs to be part of everyone’s responsibility and mindset. The most successful companies are the ones that embed innovation into all aspects of the business. It’s not having ping pong tables, free lunches and open workspaces for your employees. It’s the overall company disposition: how they organize the business, how they reward their employees, how they talk and how they listen. It’s their culture. Innovation was the foundation for their business, and it’s fueled by their culture. And this is where we started our journey.

Nice to meet you Ms. Ambassador

Finding people throughout the company who have a true passion for innovation was not the issue. How we could harness that passion and make them part of the process became the question. We needed to give them a say in our future and have them evangelize our successes. We needed to cultivate this energy, so we created a program to activate “Innovation Ambassadors.” This initiative quickly formed into a movement within EY and  has become the foundation for our innovation initiatives.

Since its inception 18 months ago, thousands of practitioners have enrolled to become “Innovation Ambassadors.” This program provides the platform and resources for them to engage in innovation no matter their career level or their primary assignments. Ambassadors have the opportunity to host or participate in a number of innovation-related activities including: contributing to ideation campaigns, hosting or attending innovation workshops and writing internal publications to share their innovation voice. They are kept up-to-date on the latest corporate innovations and emerging technology trends through a speaker series featuring some of the most influential minds and most-watched Ted Talk speakers. Recognizing that cultural change is both a top-down and bottom-up process, the program has been both executive sponsored, and localized in each office — providing the opportunity to create and shape smaller communities that contribute to the program’s overall identity.

Breaking ground and getting started

By building a program focused on embedding a culture of innovation, we embrace three philosophies:

1. Anyone/anytime: Everyone is welcome to participate at any time to enable innovation to happen “everywhere.” This visibility and transparency allows a true community to form.
2. Connected independence: We support local office autonomy which allows the ambassadors to leverage and shape the program to their needs.
3. Sourcing the crowd: The best ideas will likely come from unexpected places. Fostering an anyone or anytime framework will require the need to harness these ideas and coordinate with the extended innovation team. Most enterprises that move to this model will require a technology platform to capture these ideas.

These philosophies can assist you to create an innovation environment that reaches all interested innovators on their terms. It provides the structure required to harness the best collective thinking while allowing enough independence to foster the desired culture.

The threads that make the quilt

The essence of an organization’s purpose is its culture. The heart of an organizations culture are its people. The best way to innovate is to become a continuous learning organization and foster a culture of innovation that is truly engrained in the organization’s DNA. This will help to empower creativity and disseminate ideas in a way that doesn’t only reward success and punish failures.

Organizations can empower creative thinking by embracing the imperative that successful innovation does not come from technology; it comes from people. It’s not a “one-time” activity and it doesn’t happen “over there.” Those eager to drive innovation are those willing to take risks that will allow today’s companies to become tomorrow’s enterprises. A culture of innovation will be your foundation.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

Much has been written on how Silicon Valley emerged. The early combination of world-class research institutions, military establishments, funding and talent certainly helped. Its growth into a huge domestic market with a single language and currency has provided a foundation for global expansion. Importantly, it benefits from a deep, sophisticated capital market that provides diverse and flexible funding for second- and third-stage funding. And, we have also seen the emergence of wealthy private investors and family offices prepared to offer direct support.

America’s West Coast has essentially matured into an ecosystem that is geared toward innovation. This ecosystem encompasses start-ups, universities, and a wealth of domestic and international talent in the form of leading data scientists, engineers, mathematicians and technologists. And, this engine for innovation is leading the transformation of multiple industries, including financial services, automotive with driverless cars, health care and many others.

In sharp contrast, the European technology scene lacks the presence of a truly global player, a top technology player or similar, to complete. Overall, it is relatively fragmented compared with the US. Yes, Europe does have a number of established and exciting tech hubs, such as Amsterdam, Berlin, London, Madrid, Paris and Stockholm, to name just a few. Recent data suggests that Europe is creating more “unicorns” — start-ups with valuations of over US$1b. However, we have not seen enough of these companies break through to the US$50b-plus valuations. Also, many of the European unicorns have merged or been acquired by US technology players and have not pursued an independent strategy — the highest profile ones being in the video chat, mobile gaming and travel fare aggregation spaces.

Across Europe, historically, individual countries have pursued their own digital and technology strategies with the absence of an overarching technology framework. Encouragingly, this could soon change, thanks to the European Union’s Digital Single Market strategy, which aims to remove online barriers and create a single market to allow European firms to scale more rapidly. If successful, this could add €415b to the European Union’s economy. Logic should dictate that if a technology business can successfully serve the diversity of Europe in terms of language, currency and business practices, then becoming global business should be easier.

Another challenge in Europe has been technology funding. While it has improved in recent years, Europe is still behind the US. In the US, VC funds raised over US$100b new investment plus existing cash to invest over US$160b — of which US$70b went into Silicon Valley alone. In Europe, while VCs and private investors are growing in terms of funding, countries still remain reliant on the traditional banking system. According to the Association for Financial Markets in Europe, around 70% of Europe’s debt funding is still provided by banks, compared with 30% in the US. Also, Europe’s pool of capital remains fragmented at the country-level and there are fewer private investors prepared to support tech businesses in reaching the next stage of development.

So how can Europe improve its chances of incubating the next tech giant?

1. Create the business environment for tech entrepreneurs: We’re already seeing emerging political debate around the merits of taxing robotics and automation as way of funding a universal benefit payment for people whose jobs may get displaced. However, it would be a mistake to introduce policies that discourage the development of technology that could potentially transform industries and lives. We must understand that, socially and politically, we have a responsibility to support and retrain people who have been displaced by technology while recognizing that it would be counterproductive to put up barriers or to assume that we can slow down the pace of change. It’s a careful balance and we need to get it right.

2. Harness historical strengths: In Europe, we have seen clusters or hubs emerge around specific capabilities and cities. For example, London is a center of FinTech innovation, which complements the city’s leading role in financial services, while Stockholm is known for gaming and sport tech.

   We can observe a “networking” or “multiplying” effect when we have a concentration of businesses in the same industry. So, as the Fourth Industrial Revolution takes hold, the opportunity for Europe is for governments to digitize their historic industrial strengths by transforming today’s existing centers into tomorrow’s future technology hubs. This might be FinTech in London, the future of auto mobility in Germany, luxury goods in Italy or the internet of things for advanced manufacturing in Switzerland and France. As centers develop, with a view to creating the broader ecosystem necessary to incubate a tech giant, it would be logical for governments in individual countries to associate their upcoming tech industries with their traditional industries. The political and economic challenge is to then leverage and connect these new and emerging hubs to create the “virtual” scale necessary to compete with the US West Coast.

3. Deepen the funding pool and attract the interest of investors: The EU already has a plan to create a Capital Markets Union — a single market for capital, which would provide businesses with a greater choice of funding at lower costs. This would potentially reduce tech start-ups’ dependence on bank funding, which is less flexible than other forms of investment.

   But, policy makers also need to think about how they can incentivize private equity firms and private individuals to invest in technology businesses. The good news is that investors generally are attracted to the idea of investing in Europe. According to the EY European Attractiveness Survey 2017: plan B for Brexit, over half (56%) of global investors plan to grow their presence in Europe over the next three years.

   A key issue here will be the future role of London post Brexit with UK-based firms being involved in over half of debt and equity issuance by EU27 borrowers. Over the past decade, the city’s average annual debt capital markets issuance alone has stood at roughly US$328b. An acrimonious Brexit risks limiting the supply and access to much-needed capital.

4. Ensure that tech start-ups have access to academics and skills: Universities are a vital component of the tech ecosystem, which is why UK universities must be able to keep collaborating with European companies on contract research. In the academic year 2014–15, universities in the UK attracted over GB£836m in research grants and contracts from EU sources. This represented 14.2% of all income that they received from research grants and contracts in that year. Start-ups also need access to deep talent pools, which means that people need to leave schools and universities with skills that will be needed in the workplace of the future.

5. Welcome immigration: Technology companies compete globally for talent. They need the best engineers, technologists and data scientists. Europe has some of the best universities and colleges in the world, but studying in Europe needs to be welcoming and attractive to the best talent from abroad, particularly students from India and China. Ensuring that we continue to welcome and support free movement of the right expertise not just across the European Union, but all of Europe, is key. A positive attitude to high-skilled immigration could become a real differentiator in the future.

Of course, none of these suggestions are necessarily quick or simple fixes to the scaling challenges that European start-ups face. Neither will they guarantee that Europe produces the next technology giant. But, if we can do our best to remove both the soft and hard barriers that prevent tech start-ups from operating within the wider European market, the chances of Europe incubating its own tech giant will be greatly increased.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

Companies of all sizes feel compelled to innovate or get left behind — and they are right. But, innovation for the sake of innovation often leads to disappointing results. The need to innovate is a component of achieving a much broader purpose. Enterprises looking to develop their innovation strategies must first answer the question, “why” innovate? The link between the need to innovate and your purpose is your “innovation agenda.”

Many companies do not realize the complexity of adopting a holistic innovation agenda. Across an enterprise, business units will have varying innovation drivers that shape their unique agendas. Understanding and accepting these differences is critical to achieving a common enterprise goal.

Grow, protect or optimize?

Every decision a business makes is to either grow, protect or optimize their enterprise. When applied to innovation, these strategies define a company’s innovation agenda. The need to innovate can always be traced back to one of these three distinct agendas:

1. The “growth” agenda — those seeking new revenue streams in order to grow the business and build platforms to continually add to the top line. The growth agenda has the most historic legacy of all agendas as most companies usually associate this with their R&D efforts. But today’s companies realize more is needed, as growth must be captured by driving new business models, leveraging emerging technologies and exploiting social trends.
2. The “protect” agenda — those seeking to disrupt themselves before they get disrupted, safeguarding the core of their business. This is the most recent and evolving innovation agenda. The age of industry disruption has most companies in search of ways to protect their future relevance. They are concerned about revenue contraction, as well as market segment disruption from nontraditional competitors (i.e., start-ups).
3. The “optimize” agenda — those seeking to operate their business more effectively and efficiently, usually by cutting costs or driving greater workforce productivity. Many internal business functions, such as IT and HR, operate on an optimize agenda. A successful optimize agenda will usually result in margin improvements and lower SG&A spend.

When you look at innovation through these three lenses, the path forward becomes clearer. Enterprises also quickly realize that it is nearly impossible for one team to drive all of these agendas; rather a team approach is necessary to work in harmony across the business.

Why and why now?

With the complexity of outside influences affecting today’s businesses, the need to innovate has never been more important. The fear of disruption is real; the need to grow is ever-present; and the pursuit of the “next new thing” can be all-encompassing. Innovation agendas will vary across the enterprise, so it is critical for leaders to understand which innovation agenda is driving their decision-making.

I recently met with a company in the early stages of launching a new innovation platform. Various parts of the business had differing motivations to innovate, and they had not viewed their goals through the lenses of grow, protect or optimize. They had several declining revenue streams and very few ways to replace these losses. They wanted to reduce internal costs to offset margin losses, but also wanted to capitalize on emerging industry trends to grow their top line. Various teams within the enterprise were looking at the same emerging tech disruptors, but there was little coordination across these teams. As a result, their efforts were not moving at the speed they expected. Once they viewed their respective goals through the innovation agenda lenses, they gained greater clarity on how to best collaborate and direct their efforts.

Agendas collide … residual benefits

Collisions of innovation agendas are inevitable, and that can be a good thing. There will be many situations where an organization may have both a primary and secondary agenda. It is possible that an innovation initiative could be charged with retiring a rapidly declining revenue stream and replacing it with a new, fast-growing channel that will create a greater top line impact. This example has both protect and growth agenda characteristics.

The collision of these agendas can also be driven by outside trends and market forces. One example is the use of chatbots. There may be a strategy to leverage chatbots to increase e-commerce channels (growth agenda), and an entirely separate strategy to reduce headcount within the customer service organization (optimize agenda). Sharing lessons-learned, technology investments and chatbot strategies across teams would benefit the enterprise. While a team’s motivation to innovate will be driven by a primary agenda, there may be residual benefits from these efforts.

The fastest way to point A 

The best leaders will quickly recognize which agenda — grow, protect or optimize — is driving their innovation efforts and put a plan in place to achieve the desired outcome. They will also keep the agenda on course, not allowing it to encroach on another team’s primary agenda. Leaders and teams who understand the different innovation agendas will achieve their goals more quickly and more efficiently. A good plan starts with knowing your “why.” In this age of innovate or fade away, this has never been more important.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.

Gone are the days when design thinking was a concept that stayed within the four walls of start-ups. With disruption affecting all sectors and businesses at all levels of maturity, organizations have realized they cannot afford to rely on traditional approaches to problem solving. They are challenging the status quo and weaving design thinking into strategy and decision-making. So what does design thinking mean? Why do we need it? And what can we do to embed it into the DNA of our teams and organizations?

Design thinking is about putting the customer and people first and collaboration between diverse mindsets to arrive at innovative and often human-centric solutions to complex problems. Advances in technology — such as 3-D printing and prototyping, virtualized R&D processes and crowdfunding — is accelerating innovation, making the design thinking process easier to implement. Today’s definition and vision for design thinking includes:

• Diverse mindsets versed in design, business and technology
• Clear perspective of the art of the possible and an external stakeholder view
• A focus on improving the potential interactions between all stakeholders
• Integration of digital disruptors into planning
• Elastic, customer-oriented, cross-functional teams
• Agility innovation

While most organizations realize the importance of design thinking, they also acknowledge the lack of expert capabilities to incorporate it into their systems.

Seven steps to design thinking

To use design thinking to drive growth, you should consider changing the way your business is led and run. A design thinking culture cannot be created overnight. Here are seven steps that can help with successful design thinking integration:

1. Begin with a purpose that is about serving others: Have a clear purpose to serve others. Take a future-back and outside-in approach. Having a clear purpose can help inspire the team with new ideas and develop a sense of urgency that leads to tangible results.
2. Create a diverse team: Establish cross-functional and multidisciplinary teams to bring in a wide spectrum of perspectives and experiences to solve complex problems. Customer experiences can be enhanced when diverse mindsets work together.
3. Take a customer-centric approach: It is important to understand not just how people experience your product or service, but also how they live their lives and how your product can serve them. Think about how you can enhance their experiences.
4. Establish a practice of exploring the art of the possible: Have brainstorming sessions that give every team member a chance to ask relevant questions, provide input and get a fair hearing, regardless of seniority. These ideation sessions can help teams reframe strategic questions to form people-focused breakthrough solutions.
5. Co-create solutions: To understand what customers need, host co-creation workshops to generate new concepts and solutions together. By working on a problem along with all the stakeholders involved, you are more likely to come up with results that are driven by real, on-the-ground insights on customer demands.
6. Embrace agile innovation: Engage in rapid prototyping and focus resources on achievable experiments and concepts that will have the greatest impact.
7. Establish leadership commitment: Design thinking can deliver long-term benefit only if the leadership is strongly committed. It can significantly improve an organization’s operating model, and a top-down executive commitment will be required to materialize success.

To get started, create a culture where inside-out business or technology development is blended with empathy, creativity and a holistic approach to solutions. Encourage employees to innovate to improve customer experiences, and the organization to reframe problems from a customer perspective.

Design thinking blends design, business and technology, and it can lead to more innovation and faster development of new products, services and experiences. Among the talent, it can create a culture driven by creative, empathetic and holistic thinking that opens up new levels of motivation, leadership and employee engagement.

Legal disclaimer: The views expressed are those of the author only and do not represent the views of any of the member firms of Ernst & Young Global Limited.