Building a data-driven organization

No matter which industry you belong to, becoming data-driven is crucial in how you run your organization going forward. Although the use of data in everyday business is nothing new, becoming truly data-driven is imperative for success in a rapidly changing world. What does it take for an organization to move from assumptions and gut-feel to become truly data-driven?

For a while now we have all been told that the world moves at an accelerated pace due to digital, and the current pandemic has shown us how fast things can change when an unforeseen event occurs. History has shown us that every crisis has resulted in opportunities, and fortune favors the prepared mind.

Right now, it seems like someone has hit the reset-button and this challenges us to evaluate much of our ingrained practices when it comes to strategic decision-making, budgeting, understanding customer segments and behavior, operational processes as well as product and service development.

It is not necessarily the recipe for past success that will drive future returns, and decisions must therefore be made from facts and insight rather than gut feel, instincts and HIPPO (highest paid person’s opinion). Regardless of your data strategy, the cultural change towards a data-driven culture is a major success factor.

According to a survey of more than 300 executives by Bain and company, a two-thirds of companies surveyed stated that they were investing heavily in becoming data driven, and 40 percent the respondents expected significantly positive outcomes. Despite high expectations, 30 percent of the same executives had no a clear strategy for embedding data and analytics in their companies.

While we often recite the stories success stories of how the best in class leverage data to drive profitability and user experience, it is easy to overlook all those who struggle to meet positive returns on their data investments.

A Mckinsey study states that the gap between leaders and laggards when it comes to data utilization is increasing, and embracing a data culture is playing an increasingly important role for succeeding. According to the study, you can’t import data culture and you can’t impose it, and most of all, you can’t segregate it. A data culture is company-wide, not something hidden away in the analytics department.

Whit this in mind how should one approach the subject of nurturing a data-driven culture.

Set clear expectations on the purpose of becoming data-driven. The fundamental objective in becoming data-driven should be to take better decision. According to Harvard Business Review, best performers have top managers that demand who expect that decisions must be anchored in data – not occasionally, but every single time, and often lead through example.

Set business goals before technology and data science goals. A company’s advanced analytics goals should reflect the company’s broader aims, allowing it to amplify its most profitable products, services and processes. Coca-Cola, has been using social listening tools to spot influencers who could help the company promote its brand to key customer groups. In the banking and insurance industry data is often used to assess risk and determine credit worthiness, thus pricing products more accurately.

Go from description to insight. A common pitfall is tospend too many resources in accurately describing the present as well as a potential future, thus hoarding vast amounts of data to verify ones predictions. More data may lead to increased complexity and uncertainty rather than to drive better decisions. Rather than collecting more data, most companies would gain more from maximizing the yield on their existing data and becoming nimble enough to act on insights quickly. The value of becoming data-driven comes from the action and not he input. An accurate prediction of the future is of little value if all it does is to describe a potential future. Make sure to seek the shortest path from insight to action.

Data quality is everything. Poor decisions based on unverified assumptions will lead to unfavorable outcomes; poor decisions based on poor data quality may lead to systemic consequences, and thus decimating the organization’s trust in data-driven decisions. There will always be parts of your data collection that is inferior and downright faulty. Use the data you know are correct rather than waiting for 100 percent of your data is going to be 100 percent reliable.  As absolute uncertainty is near impossible, make sure to quantify uncertainty. Be explicit of uncertainty and include levels of confidence where uncertainty is present. Seek out drivers of uncertainty and attempt to run experiments that may shine a light on the root cause(s) of uncertainties.

Measure, learn and adjust. Frequency and speed of feedback have changed with digital, and the ability to test features and do adjustments based on real time feedback from customer behavior is a valuable competitive advantage. However, make sure to choose your metrics with care. Choose the data points that provide accurate and actionable insights.

Keep it simple. It is easy to get lost in complexity and realize that everything is connected to everything when going down the data analytics rabbit hole. Like previously mentioned, more data will in many cases lead to more complexity rather than precise results. When starting you rjourney towards becoming data-driven, be obsessed with the business problem you are attempting to solve, and seek the simplest solution for the problem at hand. Advance as you progress.

Stay true to a single source of truth. Usually, you can find the answer to the same question from several data sources as most companies have siloed information scattered across departments. Many companies that depend on data have different “data tribes”, where have its own preferred source of information. As a result the organization risk spending valuable time in arguing which variant of the same data point is more correct and/or attempt to reconcile subtly different versions of a metric that should be universal. Decide upon what should be considered the universal source of truth within the organization and stay true to it. Apply the same methods for data processing and modelling throughout the organization to avoid inconstancy.

There is a reason the term culture eats strategy for breakfast has become so popular, and when it comes to becoming data-driven one should not underestimate the cultural change required to embrace a shift in how data should play I vital role in decision making across all levels of the organization.