Data Strategy made simple – Episode 1

How come not all companies have a data strategy? Is it possible that a simple one liner statement could be sufficient to define a company’s data strategy?

This article is the first of a series where I will give my views on what I believe data strategy to be. As a consultant in that field, I’m aware that data strategy is a topic with multiple dimensions and complexities. I do not pretend knowing them all. Humbly, I will share the experience acquired in that field over the past 20 years (feeling old saying that by the way) and try to provide you with some insights, which I hope, will help you better apprehend this topic. I believe in the power of a simple and pragmatic communication style. For that reason, I will try not to use the too technical or specialist terms usually used by data practitioners.

We all use data, one way or another. Understanding data should be accessible to all. The same is true for data strategy, we should all understand its usefulness and purpose.

Back in 2001, I started working as a budget controller in a financial company. I was in charge of 4 divisions and was in direct contact with business units managers and managing directors. My main concern at the time was to be able to connect to our financial systems on T+3, query the accounting views to extract the data that would feed the reports I had developed for my clients and analyze the actual spend versus plan. Based on these reports and analysis, every months I would meet with my clients and discuss my findings. Back then, I was not concerned with Data Strategy. I didn’t even know if we had one. What I knew is that I needed to have access to specific sets of data, at the moment I needed them and that this data had to be correct.

So, why, 20 years later, am I saying that all companies should have a Data Strategy ? Well, the world of data has changed drastically over the past 2 decades. The evolution of technologies required to access, store, process, exploit and consume data has opened the door to new ways of using and managing data. The unstopped growth of internet, online shopping, mobile applications, enterprise solutions, Internet of Things, etc, is generating a volume and variety of data at a speed only few people had anticipated early 2000s. The world is now ruled by data, for better or for worse, and every week we can read articles praising data as being the « new gold », the « new oil », the « new black », etc…. This evolution alone doesn’t justify to define and implement a data strategy in a company.

 What truly matters is to understand that all companies possess data (e.g. financial, transactional, commercial, operational, organizational, etc) and need to make the best use of it to run their business efficiently.

The use of data for companies has evolved from an ability to produce reports and analyses supporting the decision making process to the ability to leverage the value of data through predictive analytics, embedded data driven decisions, empowerment of business users and revenue generating data products. Over the past 20 years, companies have witnessed a shift in the position of data in their organizations from being a cost center to a profit center. Companies have now understood that data is effectively an asset and not a liability. As such, it needs to be managed properly to ensure it can be valued in a fair manner. And, this is where there is an issue. Not all companies have yet understood that this asset needs to be properly managed. They know that this asset has a value but… which one ? Not all companies are clear on that matter. Not all companies have grasped the concept of data value creation and what is required to support this value creation, what is required to manage data assets.

This is where data strategy kicks in. Why ?

Because a data strategy’s main objective is to define the purpose of data within a company by setting a comprehensive framework to manage data assets and leveraging the value of these assets. (But, be careful, this is not a stand-alone thing. A data strategy has to be linked to the strategic objectives of the company.)

Some few years ago, part of an assignment, I was asked to come up with a formal definition of data strategy, this is what I came up with :

« A data strategy supports the realization of the organisation’s vision and objectives by providing the necessary means to leverage the value of data and efficiently manage the data life cycle. It balances the needs of the organisation to both control its data and use it in a flexible manner. It is dynamic and evolves over time depending on the organisation’s needs, strategy, culture and market »

 The key in this definition is the link to the organisation’s vision and objectives. But in retrospect, I should also have made a specific mention to business users’ needs. A data strategy that can’t be linked to corporate objectives and/ or can’t address business users needs is limited in value and will generate significant inefficiencies. In this case, data strategy is driven mostly by technical or regulatory considerations which, even if efficient and useful, is not the type of strategy that will generate the most value to a company.

Data strategy needs to support the corporate strategy. It seems quite trivial and simple. And yet, too often times, I meet with companies where there is either no data strategy or data strategy is not defined to benefit the objectives of the company or its business users. How come, in 2021, in a world where data has become a commodity, not all companies have understood that with a data strategy adapted to the needs of their organization, they can increase significantly their capacity to create more business value ? There is no simple answer to this question and one would be very ill-advised to blame these companies. Why ? Because data strategy is a very special animal both multidimensional and ectomorph and, even in 2021, it is not a well-known topic.

So, even if most companies have invested in Business Intelligence solutions and set-up dedicated data teams over the past 40 years, both on the business and IT sides, have invested in Big Data technologies and analytics capabilities in the past 10 years, a vast majority of them have done so in an opportunistic manner. The resulting organization, governance and architecture form a sub-optimal environment not able to  ensure an efficient management of data assets or the leveraging of the full value of data. For most of these companies, if you ask whether they have a data strategy, they will answer positively. They work with data, have data tools and solutions, have dedicated data functions so, they must have a data strategy. No ? Well…if you ask them to explain the data strategy, that’s a complete different story.

So, let’s be clear, if you’re not able to explain the data strategy of your company with a simple sentence or some key words, either you don’t know it or there is most probably none.

I’m not going to lie and say that coming up with a one liner statement summing up your complete data strategy is easy. It requires a clear understanding of the company’s objectives, a detailed knowledge of the data ecosystem of the company and expertise on data matters. Put together, all these elements will drive your thinking process and help come up with a clear vision of your data strategy. The words to be used will need to be chosen carefully. For instance, writing in your statement that « data is a strategic asset » will imply the set-up of a framework/ organization in charge of managing this asset, the same way you would with any other financial or capital asset. Stating that « data supports commercial development » will imply a direct link between the way data is consumed and the commercial organization. These basic examples show the importance of choosing wisely the wording of your data strategy as it will translate into a series of actions, activities, programs, projects and initiatives aimed at setting up a data ecosystem addressing all the challenges of the data strategy.

Obviously, this one liner is only the beginning of the story. All the dimensions of the data strategy need to be addressed specifically and detailed. The data strategy itself will need to be approved at executive level and receive the right level of sponsorship to enable its execution. I will come back on all these aspects in upcoming articles. For now, one thing I’d like to stress is the need to ensure that this one liner is communicated and shared across the company. Not only at executive level but at all levels of the organization including business line managers and operational staff. Make it known, make it widely known in your organization and communicate. Data is everywhere in your organization so why should you keep data strategy a secret. It’s one of the most simple thing to do to help nurture the data culture of your company and increase its data maturity. By the way, don’t use technical terms, make it simple, pragmatic and easily understandable by non-data specialists.

So, let’s sum it up :

  1. all companies should have a data strategy to ensure proper management of their data assets and leverage the value of these assets
  2. a data strategy needs to be linked to the corporate objectives of the company and its users’ needs
  3. data strategy is not a topic widely known and this can explain why many companies don’t have one although they invest in data activities
  4. a one-liner statement defining your data strategy can be used as a start-up point to the design of all the components of the data strategy
  5. communicate and share the data strategy across the organization to ensure it is well known

There is way more to this and I will try to address the challenges of designing and deploying a data strategy in an organization in upcoming articles.

If you have any question or comment, please don’t hesitate to contact me directly and I’ll do my best to answer them.

Oh, one last thing, keep it simple

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