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Seven guiding principles for becoming a data-driven organization

(Image credit: Shutterstock / carlos castilla)

A global pandemic has played a big part in elevating the data literacy of the ‘everyday man’, essentially demystifying data science for many of us. There now seems to be a much clearer connection between ‘what the data says’ and ‘what action we’ll take’. It’s a good example of data-led decision-making to drive the best possible outcomes.

In the same way, businesses can also drive better outcomes if they understand this connection and use it to transform their business model to one that is driven by data. Building a data-driven future is what will give businesses their competitive edge at a time when ’survival of the fittest’ counts the most.

With this in mind, many organizations are currently investing in data projects of one kind or another. Whether it’s data analytics, big data, AI, machine learning, data science or any other area of focus, the interest and investment in efforts to become ‘data-driven’ has been given significant extra impetus by the experiences of the last 12 months.

Whether the objective is to drive efficiencies, create competitive advantage or improve decision-making processes, it’s important to remember that isolated or independent data projects do not make you data-driven. Instead, the race to create a data-driven business infrastructure should be seen as a strategic journey, where organizations position data so that it empowers and delivers on business objectives. Success depends on transforming business models so that the whole is greater than the sum of its parts.

So, what does it take to become a data-driven organization? Here are a series of core principles that together, can help build a solid foundation, focus and measurable progress for using data as a strategic asset:


The entire process rests on effective leadership, where a top-down perspective aligns the business with data strategy. Without this approach, it can prove impossible to instigate the culture shift required to truly become data-driven and ensure that initiatives are given the right emphasis, support and representation, as well as driving the education required at a leadership level.


Next, it’s important to evaluate the relevant skills - and the gaps - within existing teams. For instance, it’s not unusual for analytics skills to be spread across departments, but in creating the right focus, business leaders need to transition to a core, centralized practice to ensure consistency. This does not necessarily mean that teams have to be changed, but organizations must create best practice processes to focus their efforts. Ultimately, building a community of data professionals who share knowledge and work together can be hugely beneficial, even if they don’t work in the same teams on a daily basis.

Best practice

Looking more closely at best practice, the objective should be to move from sporadic and isolated data driven initiatives siloed in each department to an approach which ensures consistency of approach across the organization. This should always be based on a common understanding of how to deliver value from data effectively.


As skills and best practice processes become integrated into a data driven culture, it becomes more important to ensure governance increases. Indeed, establishing best-in-class governance and frameworks is essential to ongoing data-driven transformation, because it enables leadership to track progress against goals. In practical terms, leaders need to work with data practitioners to ensure that initiatives meet business objectives, that there is consistency in delivery and prioritization, as well as in the platforms and technologies used. At the same time, every organization must meet their data compliance obligations, especially relating to sensitive or personal information.


Increasing the impact of a data-driven strategy is not just a matter of bringing the specialists together. Educating the business at large about the possibilities of analytics is an important part of the process so the whole business can share a common language around analytics and dispel preconceptions of what analytics can and can’t achieve.


As the impact of education efforts take effect, and business interest and knowledge of the potential of data driven decisions grows, many organizations find they are presented with a wide range of potential initiatives. Clearly, prioritization then becomes important, and key questions about each idea and option should include: will an initiative add significant, measurable value? Is the organization ready to implement data driven initiatives that may deliver meaningful results? Is the right data and platform available to make it work, and is the organization in a position to adopt the new practices each initiative will require?


With priorities determined and actively being implemented, the process requires a structure to measure success in a consistent way so that all stakeholders can see the data driven program at work, rather than isolated instances of innovation. This is often pivotal for organizations in their efforts to move away from a series of data science projects to being a truly data-driven company.

There’s no doubt that investing time and resources in developing a data-driven culture can radically improve insight and decision making. In today’s rapidly changing business environment, spotting new opportunities and challenges, improving processes and working with greater insight into the variables that affect business success is vital. By adopting a rounded process that addresses these critical areas, businesses have the best chance of succeeding in their mission not just to become data-driven, but in their wider digital transformation strategy.

Rich Pugh, Chief Data Scientist, Mango Solutions (opens in new tab) (an Ascent company)

Rich Pugh is the co-founder and chief data scientist at Mango Solutions.