5 steps for building a data-driven culture

May 30, 2021

To be successful, companies need to be data-driven. Data-driven is an adjective used to describe a process or action that is based on data analysis and interpretation, as opposed to personal experience or intuition. This means that all business decisions are made based on analytical insights or patterns that are derived from the underlying data and metrics that best describe a product or how users interact with it.

Having said that, this brings us the following question: how do you build a data-driven approach and more importantly, how do you build a data-driven culture in your company?

Here are the top 5 steps to follow:

 1. Data-driven culture starts from the (very) top

C-level managers in data-driven companies rely on data analysis and real-time dashboards to take evidence-based decisions. They lead through example. The example set by a few at the top can catalyze substantial shifts in company-wide norms. Consequently, these practices propagate from top to bottom, as employees acknowledge the power of data in communicating with senior management. This way of working only happens when deep expertise exists at the top of the org charts, it’s too common to devolve responsibility for analytics modeling to junior positions, which prevents analytical mindsets from really taking hold.

 2. Data collection

This is a prerequisite for becoming data-driven: an organization must collect data. By design, this process comes with a lot of challenges:

  • You need to collect the right data
  • You need to aggregate data from different sources
  • You need to clean the data

The last step is the most important as clean, trustworthy data is the basis for meaningful insights.

3. Data access

Data must be accessible and easy to query for all employees. A lot of companies struggle on this because, by far the most common problem is: people in different departments don’t have access to even the most basic data.

Top firms use a simple strategy to break this.  Instead of grand — but slow — programs to reorganize all their data, they grant universal access to just a few key measures at a time.

Data must be:

  • Joinable
  • Shareable
  • Queryable

4. Reporting and analytics

After making sure that data is collected and people have access to it, it’s extremely important to generate meaningful insights. This translates in not only observing what happened but also trying to explain and anticipate future behaviour and patterns, which brings us to a more extended discussion on the difference between reporting and analysis:

5. Specialized data training

Corporate training often refers to facilitating employees access to different e-learning platforms or internal courses in which general purpose concepts and technologies are used and afterwards. They rapidly forget these concepts if they don’t apply them in their day-to-day job. As a general rule of thumb, specialized training should be offered in relationship with day-to-day job activities and directly linked to business concepts. When it comes to data, this translates into building analytical capabilities directly related to understanding, querying and analyzing metrics that best describe business flows.

Embedding data culture within your organization should definitely be part of the digital transformation strategy. This journey is tailored around people, process and technology too. In the future, there will be no single company who could claim success without having a data-centric culture.