Category: Knowledge article

Leading organizations distinguish themselves through their approach to digital opportunities and issues. The main difference from laggards is that leaders of truly digital organizations do not blindly pursue trends and developments, but approach them strategically and proactively. This is the difference between the so-called ‘Catch-up Enterprise’ and the Digital Enterprise. 

Catch-up: surviving instead of thriving 

Many companies still operate as Catch-up Enterprises. They wait until external pressure forces them to embrace new technologies. This race to catch up often leads to reactive decisions that sometimes help them keep their heads above water, without really making progress. The focus is more on survival than on growth and innovation. 

This type of company only adopts technologies when they have no other choice. Or they have no higher plan, which leads to thoughtless and reactive ‘snacking’ on often irrelevant digital solutions within the organizational context. Catch-up then means ‘add a little ketchup and eat!’ While a healthy basis for sustainable growth is lacking. 

Digital: vision and action hand in hand 

The leaders of a true Digital Enterprise have a completely different approach. Here, digital technology is not seen as a necessity for survival, but as a force for thriving. These organizations proactively integrate technology into their core strategy, with every step aimed at creating efficiency, increasing competitive advantage and exploiting opportunities. 

Their success rests on five building blocks: a strong customer experience, a robust operational backbone, a flexible digital infrastructure, shared data and so-called ‘digital smartness’. In combination, these core components ensure a culture of continuous improvement and agility, allowing the organization not only to respond to changes, but also to predict and capitalize on them. 

Anderson MacGyver developed the Digital Enterprise model to help companies develop (further) digitally. In addition to the five building blocks, it places the organization within the context of the digital ecosystem of customers, partners and other stakeholders. The idea is that companies and their leaders can work better and more deliberately on their digital development through insight and visualization. 

Dare to choose sustainable growth 

It is up to you as a leader to shape the future of your organization. Are you in the sometimes tempting, but reactive Catch-up mode? Or are you ready for the transition to sustainable digital growth and development? The path to proactive digital strategies starts with the right insights and tools. 

Learn how to transform your organization into a Digital Enterprise. Read our whitepaper ‘How to become a Digital Enterprise?’ or contact us. Take the first step towards a successful future. 

By Tim Beswick

We are often asked whether becoming an AI-driven enterprise requires something different than becoming a data-driven enterprise. In this series of blog posts, Anderson MacGyver shares her point of view on this topic. For those who want to start from the beginning, you can read part 1: How do we become an AI-driven enterprise?, part 2: Data-to-AI-to-Value journey, and part 3: theme 1: The generative / general-purpose AI model buzz. Now, let’s dive into part 4: the second underestimated theme. 

2. Business process redesign requiring even more attention to people change 

Theme two of the four underestimated themes

People change activation is a key success factor in any digital transformation journey. It is always important to understand where and how a more extensive use of data (insights) impacts people, and to put a deliberate effort into guiding the resulting people changes. However, in cases where AI is a major part of the journey, the people impact is typically even bigger. Let us look into why the implementation of AI has such a large people impact. 

Using more Business Intelligence (BI) better is often a part of becoming more data-driven. Data insights created through BI are typically an additional piece of information that people use in an existing business process. The people involved need to learn how to use these insights. Also, it is common that organizations need to step up their efforts in creating the right data as input for BI. This implies that people need to change, better understand, and pay more attention to creating good data as the key ingredient for accurate insights. 

On top of these types of people changes typically required for successful implementation of data analytics, implementation of AI results in a business process redesign. A process redesign that changes the role of the humans involved. In extreme cases, it makes the human redundant. More often, it leads to a shift of the role of the human. The individuals involved need to be supported and coached to make this shift of their role to accommodate the new business process. If the individuals involved do not make this shift, the AI is either redundant, or conflicts arise in the business process at hand, and the AI does not deliver value. 

Besides described people change on an individual level, there is a more group dynamics-related phenomenon that requires attention. It is not uncommon that AI is directly associated with redundancy, which can lead to group resistance to AI-driven change. Overcoming this resistance after it emerges is hard and time-consuming. You are better off avoiding this initial reaction. 

This already starts in the early planning stages. Abstract business strategies that include loose statements on AI result in hard-to-change preconceptions and resistance. Close business involvement in defining and communicating laser-sharp focused Data & AI Value Opportunities creates clarity for the people involved. This is instrumental in making a good start on working the transformation together with the people who will actually ensure the embedment of AI in your business.

We discuss the other 2 themes separately in the next two blog posts. So, stay tuned! 

Follow us on LinkedIn to be notified when we publish a new blog: Anderson MacGyver LinkedIn. 

By Tim Beswick

We are often asked whether becoming an AI-driven enterprise requires something different than becoming a data-driven enterprise. In this series of blog posts, Anderson MacGyver shares her point of view on this topic. For those who want to start from the beginning, you can read part 1: How do we become an AI-driven enterprise? and part 2: Data-to-AI-to-Value journey. Now, let’s dive into part 3 in this blog: the first underestimated theme. 

1. The generative / general-purpose AI model buzz 

Theme one of the four underestimated themes

It is hard to escape the buzz around AI. To a large extent, this is caused by the tremendous advances in and uptake of freely and commercially available generative and/or general-purpose AI solutions, which kicked off in a big way with ChatGPT. On one side, this is helping data professionals to get attention from business and executives. The advancement and potential value are real. On the other, more negative side it is leading to short-sighted perspectives on what AI can be for an organization. 

Many technology and data teams are being pushed by business users into focussing their, often scarce, resources towards fulfilling business demand for the obvious generic and probably not most impactful use cases for generative- and / or general-purpose AI. Often without accurately assessing where the highest yield of efforts would be. Exactly in these situations, it is important to take a step back. Structurally engage to understand business priorities and create a common view on where and how data and AI contribute most to business value. Not in abstract terms, but in tangible wording, such as: The marketing manager uses a generative AI solution to reduce effort in redirecting marketing communications messages, reducing human effort and, with that, saving €200K per year. 

If this €200K per year efficiency gain is a strategic priority, go for it! If it is merely a nice to have and there are other more transformational opportunities to create value with data and AI, go for those! We are not aiming to debunk the value potential of generative- and / or general-purpose AI; we believe it can be real. We are cautioning for getting distracted by the buzz and becoming shortsighted. Engage with your businesses and flush out true business value beyond the buzz and prioritize.

We discuss the other 3 themes separately in the next three blog posts. So, stay tuned! 

Interested in further insights into this topic? Join our CIO Masterclass on becoming a scalable, AI driven enterprise on the 13^th of November. 

By Tim Beswick

We are often asked whether becoming an AI-driven enterprise requires something different than becoming a data-driven enterprise. In this series of blog posts Anderson MacGyver shares her point of view on this topic. For those who want to start with part 1, you can read it here: How do we become an AI-driven enterprise? Now, let’s dive into part 2 in this blog. 

Common good practices

You may recall an earlier series of blog posts: Did we just meet the modern Don Quixote?, where we discussed the following four common good practices: 

1. Vision, Goals and Strategy: Activate and focus the change effort by ensuring that all stakeholders have a clear and collective understanding of a relevant vision, goals and strategy. 

2. Data Value Opportunities: Build tangible bridges between business value and data capabilities by defining and prioritizing Data Value Opportunities. 

3. Data Value Delivery & Data Foundation: Use the prioritized Data Value Opportunities as the guiding stars for balancing Data & AI Value Delivery and Data Foundation efforts. 
   • If you focus your budget and efforts too much on Data Value Delivery, you may end up with solutions that do not meet requirements, fail to comply with regulations, cannot be integrated in your architecture, do not scale for production usage, do not provide sufficiently accurate insights et cetera. Great experiments in a dark and cold cellar, that will never see the light of day. 
   • If you put too much emphasis on the Data Foundation, you will end up doing lengthy and costly work without making an impact on the business. You spend a lot of time and budget without visible impact, you will lose momentum, business will lose their focus and limit their contribution, executives will become impatient and eventually the plug is pulled. 
4. Undercurrent of people change: Pay attention to the undercurrent of people change by deliberately working on addressing people related aspects such as motivations, emotions, beliefs, behaviors, symbols and rituals.

What is specific for Data-to-AI-to-Value journey?

AI requires specific capabilities in knowledge domains such as Machine Learning, Artificial Neural Networks, Natural Language Processing, Computer Vision, Cognitive Computing and Autonomous Systems. We will not delve into the details of these, but rather investigate the extent to which the earlier mentioned common good practices apply to a Data-to-AI-to-Value journey. 

Putting it very straightforward: the stated good practices are equally applicable to situations where AI is a major focus in your transformation journey. We have found that there are four additional themes that are often underestimated and will prove to be pivotal in your journey towards being an AI-driven enterprise: 

1. The generative- / general-purpose AI model buzz. 

2. Business process redesign requiring even more attention for people change. 

3. Additional risks and different measures. 

4. Even greater challenges in accessing AI talent.

We discuss each of these themes separately in the next four blog posts. So, stay tuned! 

Interested in further insights into this topic? Join our CIO Masterclass on becoming a scalable, AI driven enterprise on the 13^th of November. 

By Tim Beswick

We are often asked whether becoming an AI-driven enterprise requires something different than becoming a data-driven enterprise. In this series of blog posts Anderson MacGyver shares her point of view on this topic. 

Data-driven enterprise

It is good to start with looking at what a Data-driven or an AI-driven enterprise is. Anderson MacGyver takes a business value centric view and defines a Data-driven enterprise as an organization that unlocks the business value of data in any of the following three ways: 

1. Enabling digital systems with the exchange of data: Today’s society relies on systems exchanging data. The importance of the exchange of data between digital systems has superseded the importance of spoken and written words between humans. Without the timely exchange of the right data between digital systems, an organization and our society come to a standstill. Data-driven enterprises ensure that the digital systems in their organization and eco-system have the right data, of the right quality, at the right time. 

2. Deriving commercial value from data: Data on itself can have commercial value. Data-driven enterprises derive monetary value from their data by trading data either for a direct monetary reward or in exchange for other goods or services. 

3. Creating insights through analysis of data: Data analysis provides insights that can be used for improving customer intimacy, strengthening business control, driving operational efficiency and for the development or as part of new products & services. Data-driven enterprises unlock value from data by using data-driven insights to optimize their business processes and offerings. 

AI-driven enterprise 

So how does an AI-driven enterprise differ from a Data-driven enterprise? Let us first look at what an Artificial Intelligence is. Anderson MacGyver uses the following terms to define an AI. An AI is a digital system that: 

• Has the ability to learn and adapt. 

• Can generate output in the form of new data and content (video, image, text, sound, code). 

• Triggers or autonomously take actions. 

What does this mean in relation to the three types of value from data? 

1. Exchange of data between digital systems: An AI can be used to optimize the processes that drive the timely availability of data for digital systems. This does not result in new types of value in this domain. 

2. Commercial value: An AI can be used to generate new data and content that has commercial value. AI becomes the core production engine of your commercial goods that contain data, video, image, text, sound or code. 

3. Insights: AI can provide dynamic data-driven insights and if desired autonomously act. By autonomously acting an AI often improves process efficiency. The AI generated insights are used for improving customer intimacy, strengthening business control, driving operational efficiency and for the development or as part of new products & services. In these kinds of applications, AI is an extension of using data driven insights to optimize your business processes and offerings. 

An analogy to a smart car can help to make the point on dynamic insights and ability to act autonomously more tangible: 

• A route planner that calculates the best route to your destination based on a static map and ignoring current traffic situation is not an AI. 

• A route planner that dynamically recommends the best route based on the current and predicted traffic situation is not an AI if the recommendations are based on predefined rules (Note: rules can be defined using historic data-driven insights). 

• A route planner that dynamically recommends the best route based on the current and predicted traffic situation is an AI if the recommendations are based on predicted future traffic patterns, the system continually learns from historical data and optimizes the route based on multiple, complex factors. 

• A system that controls your autonomous vehicle taking the most advantageous route into account, must be an AI if you would want to safely make it past even the first junction. It would have to respond to the ever-changing circumstances on your route. 

Autonomously acting is not an absolute phenomenon. It comes in many forms. From a simple trigger in the form of a recommendation to operating and controlling systems without any human intervention. 

Summarizing the above, an AI-driven enterprise is an organization that leverages Artificial Intelligence to unlock business value by using digital systems that, based on data, learn and adapt and: 

• Generate new video, image, text, sound and code. 

• Trigger actions or autonomously act. 

Data analytics is typically used as part of an AI system. This implies that an AI-driven enterprise is an extension of a Data-driven enterprise. 

Data-to-AI-to-Value 

Becoming a Data-driven or and AI-driven enterprise is a journey. Anderson MacGyver uses the terms Data-to-Value and Data-to-AI-to-Value for an organization’s journey to become a Data-driven or an AI-driven enterprise. 

In the next blog post we share the good practices that we learnt to apply in these journeys: Data-to-AI-to-Value journey. 

Interested in further insights into this topic? Join our CIO Masterclass on becoming a scalable, AI driven enterprise on the 13^th of November. 

By Gerard Wijers  

What exactly constitutes the essence of a Digital Enterprise? And what can you do as a leader or board of directors to become such a tech- and data-driven organization? Anderson MacGyver co-founder and Nyenrode lecturer Gerard Wijers explains what is needed for distinctive customer experiences, new business models and an optimal operational backbone. All to claim a unique position within the market and business ecosystem.  

My vision of the Digital Enterprise is based on my academic work at Nyenrode, combined with the in-depth knowledge and experiences I have gained since 1991 after my PhD in IT Strategy, including  as a strategic consultant in the field of technology and data. Since our founding in 2013, as Anderson MacGyver, we have helped many of our clients become a Digital Enterprise. 

When I started as a consultant, tech and data were still by no means a boardroom topic. Over the years, that changed and our profession became more and more interesting. We now see that with a dedicated officer, the subject is increasingly gaining a permanent position at the boardroom table. Together with people from operations, commerce and finance, the decisions are made. 

Partly because of this broader engagement of leaders, organizations are becoming increasingly digital. In this article, I not only talk about the specifics of modern digital organizations, I also offer tools for becoming such an organization. 

Although the importance of digitalization varies enormously between companies and sectors, sooner or later virtually all organizations will have to deal with digitalization. Technology and data are used to differentiate, to operate more efficiently, to cooperate better within chains or ecosystems, and to respond flexibly to the needs and demands in social and economic dynamics. 

Three Pillars 
Digitalization requires a holistic view of multiple, interconnected aspects. Starting with the so-called “digitization” of the operational backbone – ranging from an ERP implementation to full automation of the production environment. Based on all my experiences with clients, I dare say that for many organizations, this domain accounts for about 40 percent of IT investments. 

Around 2014, when the term “digital” began its rise, this was still mostly about the outside of the organization. This included digital marketing, use of social media and the right digital channels such as apps, websites and so on. This ‘digitalization’ of the customer – and employee – experience in the front office is seen mostly in B2C companies. 

In addition, modern digital leaders create space and capabilities for innovation and the realization of new digitally based business models. Often, organizations start by turning their existing products, solutions into ‘digital’ services. Deployment of technology and data causes people to start thinking about issues such as long-term customer value and thus the introduction of, for example, subscription models and other forms of value creation. 

The three pillars – operational backbone, digital customer experience and value-based innovation – have so-called ‘enabling technology’ as their common basis . This is the digital version of the ‘firm infrastructure’ coined by Michael Porter, which consists of support items such as buildings, communications, administration, energy supply and physical security. 

This preconditional technology does not provide direct business functionality, but without solutions for areas such as integration, data platforms, identification, information security, you can do nothing at all as an organization. Enabling technology is growing rapidly and accounts for 30 to 40 percent of annual IT costs. 

Digital Evolution 
All of this does not make you a digital enterprise, however. A clear evolution of technology and data can be seen over the years. This started in the 1990s as IT management, which in a decade later gave way to the dynamics of demand and supply – where IT started to have its own processes and SLAs. As tech and data became very distant from the business, the step to outsourcing became smaller and smaller. 

As a result, well defined ownership became important. Agile-IT led to the rise of product owners, process owners and other owners some 15 years ago – even at the board level. By now, the more tech-savvy a board is, the more digital the organization. Sectors such as media, travel and retail are really leading the way right now. Some 50 to 70 percent of their revenue is based on digital activities. 

Since 2015 we have been in the era of digital business, with a greater importance of ecosystem dynamics in the last five years, where, for example, within a specific sector you use specialized and often powerful digital platforms. Think of Independer in the world of insurance, or Bol.com in retail and sites where you can book cheap airline tickets and vacations. 

It is important to know one’s position and added value within the network or ecosystem in which one operates. 

Top 5 pains & gains 
With our annual Digital Business Monitor, as Anderson MacGyver, we measure the state of affairs within European organizations. From this we distill a top 5 of pains & gains for board members. One of the most important gains is scalability. The idea behind it is that software is more scalable than people. Organizations are in a better position to grow through digitalization. 

Another gain is achieving operational excellence in the backbone – accounting for roughly 40 percent of all tech investments last year. Also important are an optimal customer and employee experience and realizing a responsive value-oriented organization in which Agile principles are applied at scale. 

Most of the pain is in an overly complex and outdated IT landscape. Also at play are too high costs due to inefficient operations. A word of warning: be well aware of where you are going to cut! Anyone who wants to improve margins or achieve higher sales will really have to invest more in IT. The high-performing organizations prove this. 

Other pain points include compartmentalized management rather than an integrated, holistic view of the organization. Attracting, developing and retaining digital talent also requires attention. Outsourcing driven by digital ambitions often has scarce talent as its trigger.  

Last but not least, poor data quality and the lack of a good data foundation. After all, data is increasingly at the core of the digital enterprise. 

Digital Enterprise 
But what is the core of a Digital Enterprise and what can you do as a leader or board of directors to make it happen? Before that, a word about the dynamics on the outside and inside… 

A unified and distinctive customer experience requires an integrated mix of digital propositions, products, channels and services. Organizations that do this well include Albert Heijn, Rituals and DPG Media – roughly half of their IT investments go toward optimal customer contact. Here it is important to be able to change quickly and energetically in order to respond optimally to current needs. 

There is also the extensive and relatively slow operational backbone, which needs to be automated, standardized and simplified. This stable and well-integrated IT supports the primary operational business processes and staff functions such as finance, HR and procurement. The pain of technical debt and legacy is especially felt here. Companies that focus heavily on having a good backbone include LeasePlan, CZ and Spie. 

Digital infrastructure was also already discussed under the heading of enabling technology. As mentioned, 30 to 40 percent of total IT spending goes to integration and data platforms, IoT and IT4IT. Organizations with a major stake in their digital infrastructure are Tennet and Schiphol Airport. 

Central elements 
Many organizations and their leaders are aware they are not getting the most out of their software and data. To change this, they can make the right tools and data widely available within the organization so that different functions and departments can run artificial intelligence and algorithms on them. In this way, they can become digitally smarter. Efforts in this area tend to lead to more and better insight into customers, products, processes and operational exploitation of these insights into direct action. 

Characteristic of the digital enterprise are this digital smartness and organization-wide shared data. As with the digital outside, inside and infrastructure, these are not things you approach in isolation. For maximum results, you address everything as much as possible in interconnection. with the digital front end, back end and infrastructure. Frontrunners in this area include PostNL and TVH. 

Recommendations 

My recommendation is to start thinking like a digital enterprise: holistically and end-to-end. Based on governance and personal leadership, create business strategies with explicit digital ambitions, leveraging technology and data to differentiate yourself from the competition. 

Moreover, build strategies that are scalable. Meanwhile, keep the overall IT landscape as simple and fit for purpose as possible. Place ownership and responsibility within multidisciplinary teams. Ensure tech and data capabilities within management teams and the board. Also recognize that there is no one type of IT, but embrace a multimodal approach. 

That multimodality means that different aspects of business operations require different kinds of IT. The Operating Model Canvas (OMC) helps determine which IT best fits which business activity. Across the axes of dynamics (from stable to responsive) and differentiation (generic versus specific), our multimodal model has four modes: distinctive versus common, and adaptive versus specialized. 

Conclusion: digital is everybody’s job. Everything you do with technology and data must be scalable and fit for purpose. That requires ownership and leadership – especially within boards of directors – in addition to a multidisciplinary approach. 

What’s next?

Want to find out more about the Digital Enterprise? Take a look at our other page: Stop playing catch-up, start becoming a Digital Enterprise
Want to get more insights? Jump to our pain: a complex legacy IT landscape and technical debt
Over the coming months, we will be sharing fundamental insights and experiences. Don’t wait to dive into this exciting theory! You can also contact Gerard via email or text to spar.

Last year, our Digital Business Monitor surveyed digital leaders to understand their strategic decisions on technology and data. We found varying levels of digital integration in organizations: some use technology and data to support the business, while others have digital ingrained in their operations and is thus an integral part of doing business. In the latter the business is digital minded and collaborate with tech and data experts in multidisciplinary teams to drive value. 

Challenges of digital leaders
Top digital challenges cited by all leaders were complex legacy systems and business-technology collaboration. But differences between our groups also emerged: “supporting the business” organizations faced digital talent shortages and lack of digital savvy leadership, possibly due to a preference for digitally-driven companies. Meanwhile, “doing digital business” leaders encountered issues with poor data quality, suggesting a heightened need for effective data management amidst intensified collaboration with business (and even partners) to deliver value. 

Do you want to know more about the Digital Enterprise? Check out our page: Stop playing catch-up, start becoming a Digital Enterprise

In the journey to a digital enterprise, CZ took three crucial steps in two years: transformation of IT, reassessment of business strategy and the decision to go through major standardization. CIO Peter Slager explains how technology and data are closely intertwined with CZ’s strategic direction. 

As a member of the executive board, the CZ CIO is eminently motivated to help improve healthcare through digital means. “My wife is a general practitioner, and we regularly talk at the kitchen table about using technology to keep healthcare affordable and accessible in the face of a tight labor market and rising prices. Business and IT will increasingly become one, with tech and data as enablers of innovation.” 

CZ has more than four million customers and the health insurer is in great financial health. According to Peter Slager, as one of the larger players, the organization can help guide digital developments in the sector. In this regard, the former top handball player brings vision, team focus and leadership experience – gained at LogicaCMG and Ahold Delhaize, among others. “I really believe in doing things together, multidisciplinary. It makes you much more effective as an organization,” he emphasizes. 

Value chains
When Peter Slager took office as CZ’s CIO two years ago, he had been on board with the company for some time. “That made my first hundred days a lot easier, because I was able to do an analysis on the entire division in the without having to manage the department. I wanted to transform the IT organization from an internal supplier to an enabler of business objectives.” 

The initial IT transformation program was based on four values: customer focus, simplicity, well-oiled processes and professionalism. Next to a more value-chain and customer journeys-orientedIT organization, CZ is working to modernize its technology and data landscape. 

“Soon we also decided to review our business strategy. Our services so far have had various digital elements, such as using technology and data to improve and enrich the CZ app. But rising healthcare costs and scarce talent required a much broader focus. After all, if we as a sector do nothing now, we are heading for a shortage of 80,000 healthcare professionals by 2030.” 

Pivotal
“In the summer of 2023, we saw that there was suddenly a lot going on,” Peter Slager continued. “The BizzTech program bringing IT closer to the business had led to improvement in the delivery process, but it was still not moving fast enough. Moreover, IT costs continued to rise. Partly because of our pivotal role in the CZ 2030 strategy, I decided to contact Anderson MacGyver.” 

The CIO had three questions: how can CZ IT accelerate, to what extent does the current architecture fit the ambitions and are IT costs keeping pace with other health insurers, and should we continue to want to do everything ourselves in terms of IT? “Using the Operating Model Canvas (OMC), it became clear which IT best fits our processes, principles and business activities.” 

OMC color palette
The colors assigned to the OMC showed that the many green-labeled generic activities within CZ demand stable and efficient IT support. In addition, on the customer side the color blue (adaptivity) deserves particular attention, while on the product side, among others, purple predominates: the need for distinctive (specialized) solutions. Orange is for the specific but relatively stable business processes. 

It did take a while before the OMC found its place in the organization. By now, both management and employees recognize and acknowledge the value of such an overview. “The visualization helps enormously to get people on board with the changes. The most important conclusion was that with the existing IT landscape we would not be able to realize the CZ 2030 strategy.” 

Compared to similar organizations, CZ’s change capacity turned out to be rather limited. “The conclusion was that we had too many in-house developed customizations. Together with Anderson MacGyver, we are now defining a new target architecture. Parallel to the initiated standardization is the transition to a scalable, modular and secure IT landscape. This includes sound data management. We are also reassessing the sourcing strategy.” 

Organizational principles
But CZ is looking ahead even further. The principles embraced within the IT division, such as well invested responsibilities and the pursuit of efficiency and simplicity, inspire a future vision for the overall organization, business processes and culture. 

“All in all, in two years we as an IT function have made the move from internal supplier to more strategic enabler based on technology and data,” concludes Peter Slager. “Business and IT can no longer be separated in this regard.” 

Curious about the general review of our Masterclass with Peter Slager, CIO of CZ? Read it here.
Want to find out more about the Digital Enterprise? Take a look at our other page: Stop playing catch-up, start becoming a Digital Enterprise

Technology and data have plenty of attention in the boardroom. Organizations are massively seeing the benefits of digitization and also experiencing the pain when they are lagging behind. A record turnout of over sixty senior level executives in the digital business domain underlined the need for knowledge and insight during the recent CIO Masterclass. CZ-CIO Peter Slager acted as guest speaker. 

Chair Fiep Warmerdam introduced the day’s theme Tech and Data in the Boardroom not from her role as a young consultant, but rather as a consumer who is increasingly making digital choices. She was referring in particular to her own and future generations. “When choosing health insurance, a digital card for my phone is an important requirement,” she warned. “Boards must align their decisions of today with the customer needs of today and tomorrow.” 

Digital enterprises
Before Peter Slager explained how, as CIO of health insurer CZ, he puts technology and data on the agenda, Gerard Wijers first outlined the playing field. “In 1991 I did my PhD in IT Strategy and back then it was definitely not a boardroom topic,” said the co-founder of Anderson MacGyver and Nyenrode professor. “By now it is. It’s great to work with our clients to create digital businesses.” 

All organizations, he says, are becoming increasingly digital, regardless of the sector. Although, companies within media, travel and, to a lesser extent, retail are generally further ahead than, say, manufacturing companies and infrastructure builders. Nevertheless, there are similar challenges everywhere, according to Anderson MacGyver’s own Digital Business Monitor. Take the need for scalability, responsiveness and customer focus. Or the pain of excessive costs, too much complexity, scarce talent and poor data quality. 

A holistic view of digitalization addresses multiple aspects, according to the Anderson MacGyver co-founder: “Digitization of the operational backbone, digitalization of the customer and employee experience and being able to innovate and create digital business models. All of this is made possible by underlying enabling technology.” 

Central elements
Other central elements of the digital enterprise are digital smartness and shared data. According to Wijers, these are not things you approach as standalone, but as much as possible in connection with the digital frontend, backend and infrastructure. “Digital is everybody’s job. Everything you do has to be scalable, fit for purpose – that requires ownership and multidisciplinary teams and responsibilities.” 

He also introduced the concept of multimodality. This means that different aspects of business operations require different kinds of IT. Sometimes focused on distinctive service or market position, and sometimes focused on operational efficiency. The so-called Operating Model Canvas (OMC) helps determine which IT optimally fits which business activity. 

The OMC of CZ shows with the use of colors that at the health insurer the many generic processes labeled green, partly in view of the highly regulated business domain, require stable and efficient IT support. In addition, on the customer side blue (adaptive) deserves attention, while on the product side purple predominates: the need for distinctive (specialized) solutions. 

Personal drivers
The easy-talking CZ-CIO Peter Slager would return briefly to OMC after his presentation. First, he talked about his own motivations to help improve health care. “My wife is a general practitioner, so we have regular conversations at the kitchen table about the possibilities. I also believe that business and IT are one in the same, with tech and data as enablers of all kinds of new possibilities.” 

That power of innovation is badly needed, because the healthcare industry is facing major challenges: keeping healthcare affordable and dealing with labor shortages. As one of the larger companies, the board member says CZ can help guide the sector toward tech- and data-based solutions. The former top handball player brings vision, extensive digital leadership experience and team spirit. 

To best address the spearheads for the so-called CZ2030 strategy, he is working on, among other things, customer focus, simplification, streamlined methods and processes and, finally, a more results-driven culture. A more focused organization and a scalable, modular and secure IT landscape are important tools. 

Strategic enabler
Peter Slager has now been on the way for about two years. “In the summer of 2023, we saw that as part of the IT change program, costs were rising dramatically. In addition, we needed to accelerate. Together with Anderson Macgyver, we then determined a new target architecture. With the OMC, we saw which IT best fit our processes, principles and business activities. The conclusion was that we had too much customization. In addition to standardization, we also recalibrated the sourcing strategy.” 

At the end of his presentation, the CZ-CIO again emphasized the strategic importance of technology and data, striking a good balance between business value-oriented innovativeness and limiting complexity. “After two years, we as IT have made the shift from internal supplier to a more strategic enabler,” he said. 

It was striking that the questions from the 60-member audience were mostly about the strategic position of technology and data, with Peter Slager emphasizing that digital tools can help pre-eminently in being able to provide people with the right care. AI can also play a role in this – supporting the human factor and especially as an enabler for operational excellence and serving customers better. 

Moreover, it is important to involve concerned employees, suppliers and other parties in the ecosystem. Slager: “We really have to do it together.” 

Curious about the recap of our previous Masterclass with Jon Månson of Scania? Read it here.
Want to find out more about the Digital Enterprise? Take a look at our other page: Stop playing catch-up, start becoming a Digital Enterprise
Or check out the core components of a Digital Enterprise.

Don’t forget to continually address the appetite for business results
This is the third and last part of our blog series on key approaches for designing successful Data to Value journeys. In part 2 we talked about the importance of data and business people spending time together to explicitly define and prioritize data value opportunities. By doing this abstract data value terms are made tangible and bridges are built from required business capabilities to data capabilities. A joint understanding is achieved of what data capabilities are required; including the typical and for business people sometimes uncomfortable truth that also nasty foundational capabilities require company-wide, thus also business, attention. 

In this blog we discuss the importance on continually addressing the appetite for business results by delivering the data products that the business needs whilst also working on an improved Data Foundation. 

The effects of not finding the right balance 
It is probably easiest to define right balance from a perspective of imbalance. 

• If you focus your budget and efforts too much on Data Value Delivery, you will be end up with data products that do not meet requirements; do not comply to regulations, cannot be integrated in your architecture, do not scale for production usage, do not provide sufficiently accurate insights etcetera. Great experiments in a dark and cold cellar that will never see the light of day. 
• If you put too much emphasis on Data Foundation, you will end up doing lengthy and costly work without making an impact on the business. You spend a lot time and budget without visible impact, you will loose momentum, business will loose their focus and limit their contribution, executives will become impatient and eventually the plug will be pulled. 

Based on our experiences of working with many organizations we can confidently state that already creating this awareness is already a major step forward. The next step is to structure your data activities and expenditure along these lines. This facilitates constructive dialogue and conscious decisions were points of view are understood and considered. 

Data value opportunities are your guiding star 
Answering the question where exactly the right balance lies for an organization is a much harder one to answer. You can build on your detailed and prioritized definition of data value opportunities; these should drive your decisions. Below we have listed several examples of recent considerations with our clients 

• Do we need a machine learning capability if we are focused on using market standard systems for all our business activities expect for product engineering? Should we limit to a small pocket of advanced analytics capabilities in this business domain? 
• Do we need a organization wide self service capability if only the engineering and finances departments are expected to use this? Should we then focus on cataloging only the data domains that engineering and finance intensively use? 

• Giving the fact that we work with partners in sales; should we focus on data interoperability in that data domain first of all? 
• Based on our business risks, what are our Critical Data Elements and should we focus on these for data quality measures first? Should we first of all tackle the data domain to which the majority of CDE’s belong to? 
• Should we really focus on continuous data quality monitoring if we merely use data for descriptive reports that contain pointers for our sales people? 

Create an overview of required capabilities per opportunities and consolidate this. Include the data domain dimension in analyses to allow for focused and / or staged implementation. Ask yourself the question whether staged data product releases make sense; can we deliver an 80% solution short term to later deliver enhanced solution? And were does this not make sense, e.g. due to risk of presenting misleading or incorrect insights. 

Below you will find an illustrative example using a DAMA DMBok based data management capability framework. 

Once you have a clear picture of your target capabilities you can start measuring the delta between your current actual and target and based on this identify what effort is required where. Where do I need to fix the plumbing and which business thirst should I focus on quenching first of all? Share considerations and options with users and jointly create the implementation roadmap. 

Do not become the modern day Don Quichot 
To summarize, Anderson MacGyver suggest adopting the following approaches for designing successful Data to Value journeys 

1. Take a business value centric approach 

2. Use data value opportunities as the bridge between business goals and data capabilities 

3. Spend time making data value opportunities explicit and always prioritize 

4. Speak up about the need for foundational improvements; but always relate this to the data value opportunities  

5. Data value opportunity priorities drive your data capabilities prioritization 

6. Carefully assess which data capabilities you truly require, and which you do not require, to be able to address the prioritized data value opportunities 

7. Consider interim releases and selective per data domain implementations to be able to deliver shorter term results 

Missed the first two blogs? Jump here to the right pages:
Part 1: Did we just meet the modern day Don Quixote?
Part 2: Throwing a clump of earth into the reed