Abraham Lincoln was quoted as saying “Give me six hours to chop down a tree and I will spend the first four sharpening the ax.”
“You want to bear that quote (and process) in mind when defining your data strategy. » That’s how Bernard and Philippe summarised the matter when we first discussed writing this article.
Bernard and Philippe have been working in the data field for years now. They have seen the rise and decrease of the Big Data enthusiasm, and we’re observing that artificial intelligence and blockchain might be following a similar path (and hype). Along the way, they have experienced the many challenges, gaps and unmet expectations that data-related technologies brought with them.
While some of those technologies are stunning and will be, arguably, more and more part of our daily lives in the next few years, a data-driven and automated world doesn’t come so easily. As long as we don’t have clean and quality data, sound data governance, efficient data management and a well-thought-out data strategy, pitfalls will be hard to overcome.
Years of discussion with numerous leaders addressing matters such as data strategy, data management, data architecture and digital upskilling, have helped them to identify patterns. For instance, numerous leaders have had difficulties in defining their organisations’ data strategy. Sometimes the basics—determining what a datum is—isn’t that obvious, and there is confusion about what data, information and digitalization are.
Business leaders know that, nowadays, tapping into the power of data isn’t a choice but a necessary requirement. Asset, resource, magic wand or the Nostradamus of the XXI century, whichever you prefer, the gap between acknowledging its importance and actually taking advantage of it is, often, too large, due to data management complexity and the necessary organisational structure to handle it.
In this article, we go through some recurring misconceptions of what data and a data strategy are, and how they, when purposely used, make businesses smarter, well-informed and more resilient.
What is data and why you should care about it
This subject has been discussed all over the world for many years. What is data, after all?
The International Standards Organisation (ISO) defines data as “re-interpretable representation of information in a formalised manner suitable for communication, interpretation, or processing”.
Ergo, data is both an interpretation of the objects it represents and an object that must be interpreted. Although that rationale is helpful, we still need to clarify the concept a bit more.
The thing is that pretty much everything can be data. The real—and brainy—trick here is to define which data has value for you. The key is to treat data as an asset.
Because an asset will be specific to each person or organisation, that’s why. For instance, e-commerce companies want to know what you buy and where you look, social networks want to know how much time you spent on each video, article or ad to improve their algorithm recommendation system, insurance companies want to understand your future behavior and… you name it.
Almost an axiom, the sentence “the world is changing” is always useful to remind us we have to keep up with the pace of, well, change. You might have seen headlines affirming that data is the new oil. Quite frankly, we don’t agree with this comparison as you can re-use data as long as you need it, providing that you have assumed that data is something highly valuable.
This translates into using data in a way that benefits your business and delivers insights to make better decisions. Data is essential for any business and, when used correctly, it can be a powerful tool that will help you to excel and take your organisation further.
A strategy for data
In 1979, General Motors employed more than 800,000 workers and made about US$11bn. In 2020, Google made about US$182bn while employing 135,301 people. In 2004, Blockbuster had 84,000 employees and made US$6bn in revenue. In 2020, Netflix had 9,400 employees and made US$25bn in revenue.
Source: Based on The Rise of the Machines – Why Automation is Different this Time (Kurzgesagt – In a Nutshell)
You could say that these comparisons don’t make sense. And that’s exactly our point. The world has changed and we have access to new information, very diverse, like never before.
In recent years, the world has started to gather data about everything. Customers, products, behavior, weather patterns, medical records, communication systems, demographic data, geolocation data, data about what we do at work… and that is creating new industries. “Data-heavy” industries, Bernard likes to call them, by the way.
It’s estimated that, by the end of 2021, the volume of data and information created, captured, and consumed worldwide will be 80bn terabytes, and that number is expected to double by 2025. According to Stanford University, the world produces around 1200 Exabytes of data every year. That’s 629 million of 2TB external hard drives (what most of us have at home). The total volume of these drives could fill more than 292 Great Pyramids!
Source: Based on Infographic: The Physical Size of Big Data
So, data can help you innovate and reach strategic goals. But, despite that recognition, we continue detecting important gaps and a lack of data management.
To understand the value of your data, we invite you to put all the elements of a strategy in place —vision, planning, coordination and governance, commitment—having the organisation’s leaders and decision makers greatly involved. Knowing how to identify valuable data, how to collect it, store it, govern it and take advantage of it isn’t that straightforward.
Having a data strategy is all-important, then, and taking the few steps we propose to you, right below, will lead you in the right direction.
How to define a data strategy and why it is a challenging endeavor
Any business strategy is a set of guiding principles that, when communicated and broadly adopted in the organisation, generates a desired decision making pattern.
A data strategy falls into the same definition. It’s a highly dynamic process employed to support the acquisition, organisation, analysis, and delivery of data in support of business objectives. This equals how the organisation will get the data, how it will manage it and ensure its reliability over time, and how it will use it.
A good data strategy treats data as a vital company asset and provides a clear roadmap—a set of guiding principles or rules—that defines the actions that people in the business should take (and not take), and the aspects they should prioritise (and not prioritise) to achieve any desired goal.
Kindly note that, for the sake of this article to be more compelling and effective, we assume that you have already gained the key stakeholders’ approval. Here below, get some valuable tips to develop your own data strategy.
1. Start from a global perspective
What is the vision of your company? Vision is the articulation of your organisation’s purpose and contribution towards a shared goal which is bigger than each individual’s.
In the same line, what is the strategy of your company? Have it on hand because, in the next step, you should align your data strategy with it. Using data just because it’s modern and fancy will not lead you anywhere.
We recommend not to focus on data as long as you don’t have a clear answer to the above-mentioned two questions. One day or another, you will have to create a compelling story for your data and how it aligns with business objectives. And the sooner, the better.
2. Outline your data strategy components and match them with your business strategy
Clearly, a data strategy must support the organisation’s strategy. On the same level that a marketing strategy has its own objectives aligned with the ones of the organisation, a data strategy should be done with a holistic approach where many company departments and functions get involved.
Outline what the future data strategy will look like and think of the key components of it. Also, reflect the goals you want to achieve with it and compare them with the ones of your business strategy. Only then a total alignment across the organisation to realise their data-related vision of success can happen.
Bear in mind that a wrong business strategy will probably lead to a wrong data strategy. As a result, you will gather inaccurate information about your business environment.
3. Perform a Data Maturity Assessment
A data maturity assessment helps organisations benchmark their capabilities, identify strengths and gaps, and leverage their data assets to improve business performance. It is a business-orientated analysis. It will allow you to know where you are and help identify where you want to go. With it, you define the change management effort to put in place to go from the “as-is” to the “to-be”.
This exercise will help you to have a better understanding of what your sets of data are, how they are collected, where they are stored and how it’s consumed within the company.
According to the Data Maturity Model of the CMMI Institute, data management can be addressed across five disciplines: data strategy, data governance, data quality, platform & architecture, and data operations. These disciplines work together to create an effective data framework, which is your next point of focus.
4. Data Management and Data Office
This is the more complex part of your data strategy definition because it is the step where you will work on the framework that will support it.
As you’re transforming your company, you want to put in place a data programme—a temporary organism of transformation, and a data office—centralised or federated according to your needs.
The role of your data programme will aim to transform strategic decisions into operational processes to achieve the expected benefits and the defined data maturity objectives. Ideally, your data programme will have to comply with these requirements: remain aligned with corporate strategy, lead change, focus on the benefits, deliver a coherent capability, and learn from experience while continuously add value.
On the other hand, the role of the data office is to deploy your data strategy while owning and maintaining your data programme. Ensuring good data quality, data governance, data operations, data architecture, among others, are day-to-day responsibilities of the data office.
You can use your data office as a change champion—cultural or not—by helping business stakeholders to join and integrate the transformation that you are putting in place. You can also use your data office to deal with reglementary issues linked to data such as identifying constraints and regulations, differentiating internal and client data or ensuring clients’ consent to use their data (e.g. primary or secondary use).
Having a data office and a data framework is a good start, but neither can be fully effective in the absence of a coherent strategy for organising, governing, analysing, and deploying the organisation’s information assets.
5. Offensive or defensive data strategy
At this point, you know where you are, where you want to go and who’s involved in the journey. Now let’s have a deeper look at the primary purpose of your data. What are you going to do with it? Are you going to use it defensively or offensively?
Leandro DalleMule and Thomas H. Davenport identified two types of data strategic orientations in the 2017’s article “What’s your data strategy”.
- Data defense is about minimising downside risks: ensuring compliance with regulations, using analytics to detect and limit fraud, and building systems to prevent theft. Defensive data is ideal for a highly regulated environment requiring protection like hospitals. Controlling your data is key.
- Data offense focuses on supporting business objectives such as increasing revenue, profitability and customer satisfaction. GAFAM are great examples of offensive data. Flexibility is key.
A company’s position on the offense-defense spectrum is rarely static. They usually strive for the best balance between defense and offense and between control and flexibility. It can explain the difference in speed related to how data is used in different activity sectors.
Commonly, an organisation’s data strategy will require a certain amount of both, the offense and defense mindsets. Either way, deciding which data strategy to choose will ultimately influence:
- The data foundation, namely, how your organisation is going to deal with data collection, storage, processing and management to generate value, and
- Data monetisation, i.e. how your organisation is going to create value from data (e.g. data business model, data analytics, data driven insights, data driven operations).
6. People and skills (Please do not forget them!
To fully deploy your data strategy, you will need, obviously, resources and, among them, people’s skills to use new tools and understand and handle data effectively. But, more importantly, and acting as an underlying pillar, there is a need for new ways of working.
We have seen so many challenging situations that the organisation could have avoided if more skills—both hard and soft skills—were involved.
Think of educating your collaborators by teaching data literacy, meaning the ability to read, understand, create, and communicate data as information.
It’s also recommendable to upskill them by foreseeing and identifying the needs that the data-driven transformation will create. Then, develop customised training while developing pathways that will allow your collaborators to go from beginner to expert in pre-designed domains.
But think broader, more holistically too. The challenge isn’t only to acculturate teams to data-related problems, but also to train the people in charge of data to deal with business issues as you will need proactivity throughout the transformation.
Finally, bet on creating a data culture at organisational level, especially the more your collaborators get excited to take part in this journey. Understanding the power of data and how to use it will become a life skill and you must put everything in place to support the change.
Wrapping things up (or the conclusion)
As you can see, defining a data strategy will involve many stakeholders and will require investment. Executing and implementing the data strategy is another challenge and will require additional resources.
We just want to emphasise, once again, that data should be treated as an asset and will bring little or no value as long as you aren’t actively using it. Data serves the business and goals’ achievement, that’s the mindset to keep in mind.
Heraclitus once said no man ever steps in the same river twice because the river is different (and the man too). It’s the exact opposite with data. You always want to know where you’re stepping into and, what’s better, whose foot it is.
That’s why you will need a data strategy.
What we think
Data can be both your best friend and your worst enemy. Change being the only constant, be sure to get the best of it by defining a data strategy that reflects your business needs.
Having a data strategy means making choices and renouncing certain ways and practices so you aren’t flooded by data but focus on the most valuable part of it.
Discover our Data Analytics and Artificial Intelligence services!