top of page


Data driven culture is difficult establish and sustain - sumo analytics 6 steps to create data-driven culture - artificial intelligence and data science

Becoming a data-driven organisation is simply about replacing intuition with facts and insights derived from data - outcomes of sophisticated analytics models - for faster and more accurate decision making.

The whole idea of a data-driven culture is therefore about making data the go-to resource for leveraging actionable information. Obviously, organisations have always paid good attention to their numbers, but data-driven culture brings the whole concept to a higher level and fosters data-driven transformation within every department.

The goal will always be to inspire employees at all levels to use data in their daily activities and every-day decision-making for enhanced accuracy and efficiency for fully exploiting the organisation’s potential. And to build a data-oriented cultural framework that encourages employees to work together in making data the centrepoint of all decision making, and by continuously coming up with new and prioritised use-cases.

Obviously, many organisations are already getting all kinds of information and actionable insights from their data. Data-driven culture, on the other hand, fosters employee capabilities of data interpretation and critical thinking instead of blindly following numbers. But as most companies aspire to become data-driven, many seem to fail in their transformation efforts. Something that can often look simple enough on paper can sometimes become a daunting task during the actual implementation and the sustainability of it.

We have listed six factors that are critical for the success of any data-driven transformation that will help establish an organisational culture with data at its heart.

1. Data-driven transformation starts at the top

That means that the CEO and all senior management must adopt data-driven decision making to set an example for the rest of the organisation. They must show that decisions anchored in data are the norm, but not an exception. In communication between managers and subordinates, action should be backed by data.

This kind of practice spreads across the organisation and employees who want to be taken seriously when communicating with their superiors will have to communicate on their terms and language which is data-focused. Leading by an example by few at the top can create a major transformation in organisation-wide behaviour.

2. Adopt use-case prioritised approach

A large number of leading corporations have had to deal with repeated failures on the journey to data-driven transformation. Alarming results detailed by R. Bean and T. Davenport in Harvard Business Review claim that 72% have not yet managed to create a data culture; 69% say that they still haven’t created a data-driven organisation; 53% state that data is not treated as an asset; and 52% admit that they’re not competing on analytics.

The main underlying problem is that many organisations try to do everything at the same time and bite more than they can chew. Employees won’t be able to gradually adapt and will be more reluctant to use the implemented data solutions. Remember, people are busy with their daily tasks and adding multiple data-driven solutions will take time to learn and adapt to; time that many don’t have.

A tried and tested best-practices approach is agile use-case-based prioritisation. Data-driven transformation done use-case by use-case will shorten the turnaround time and allow employees to start using some solutions immediately. The ROI will be much quicker, the delivery of actual results will be immediate and allow for constant alignments and improvements in the data-transformation process.

3. Don’t box-in your Data Scientists in the IT basement

It is sadly common that organisations keep data scientists somehow isolated within the workplace where often they and business leaders have little knowledge about each other. Data science and analytics will never provide value and won’t survive if it’s operating separately from the rest of the organisation. A successful strategy has been to rotate staff from centers into operational roles where the proof of concept is scaled up and then returning to centers for production.

Data scientists should be brought closer to the business, i.e. understand the daily challenges the organisation is dealing with in different areas. This will ultimately allow them to come up with solutions without being specifically asked to do so, as their operational understanding allows for direct problem solving. Likewise, the business should be pulled towards data science; i.e. that employees are data literate and understand the fundamentals of data science and even being able to perform basic coding.

4. Create easy data access for employees

One of the main issues out there is that people seem to have difficulty accessing even the most basic data for simple analyses. When analysts and other employees struggle to find the right data, very little analysis will take place and efforts of creating data driven culture will fade out. It is critical that companies create a robust analytics platform with Central Analytics Database that guarantees access to the right data, at the right time and in the right format for the whole analytical structure to function perfectly and encourage use.

5. Follow through with training and constant adaptations

Analytics is more for employees than it will ever be for clients. Inspire them to learn skills related to data science and analytics which will allow them to constantly stay up to date with the newest and the latest. Remember, data-driven transformation is a never-ending journey and the employees are the most critical factor to make it a success.

At the same time, measure the use of data and analytics solutions within the company. What is being used and how can it be made better, more accessible and more insightful. What is not being used and why. Speak to employees and create a system for them to propose changes and modifications. For a data-driven culture to take root, the employees need to have a say in what use-cases are important, the design, how it’s accessed and what the desired outcomes should be.

6. Establish data ambassadors for organisation-wide buy-in

Data-driven transformation is truly a major change for companies and proper change management practices are necessary. Every layer of the organisation should be included and the identification of change/culture ambassadors should take place at every level in every area. They will serve as communicators and exemplars, educating their colleagues about why transformation to data-driven organisations is important.

Obviously, those informal leaders must be identified before they are engaged. They need to have certain characteristics and be respected and influential among their colleagues as they’ll serve as role models, educators and promoters of the change. But most importantly, they need to buy into the change themselves.

Organisations who spend time establishing ambassadors at every level in the beginning of the process will experience far more success on their data-transformation journey.


Most businesses have realised the importance of data-driven transformation for competitive advantage, operational excellence, and the future success of the organisation. But simply desire to become data-driven is not sufficient. Even so, adopting the technologies necessary is not sufficient either. That’s the easy part. To truly succeed, organisations need to create a culture where the data-oriented mindset can thrive.

We understand that data-driven transformation can be hard to achieve, but we're here to help - Sumo Analytics delivers clarity from complexity


bottom of page