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TDWI Upside - Where Data Means Business

How to Build a Data Culture

Many organizations still struggle to successfully complete data projects. These tips for building a data culture should go a long way in overcoming the problem.

If we live in a more connected world than ever before, and most companies want to be data driven, why do so many data projects fail?

For Further Reading:

Five Characteristics of a Data-Driven Company

Building a Data-Driven Culture in Your Enterprise: Introducing the 4 Pillars

To Be a Data-Driven Enterprise, Become Data Literate First

Having worked with Fortune 500 organizations, not-for-profits, and everything in between, I have noticed one similarity across organizations of all types – they all struggle with building a data culture. Digitalization has resulted in our generating far more data than our predecessors did, and there is no shortage of data we collect or store. What we have, though, is a shortage of people able to understand the data.

Data by itself doesn’t have much meaning. It is the people who give it the meaning when they use it to achieve business outcomes. Without having a data culture in place, it’s like giving a person all the ingredients needed to bake a cake without considering their baking skills. Just creating dashboards or investing in technology solutions will not result in business value.

As the famous saying by management consultant Peter Drucker goes, “Culture eats strategy for breakfast.” Any data strategy will miserably fail without cultural change.

Step by Step Data-Culture Building

Culture cannot be built overnight and requires incremental steps. I break down building data culture to three major areas.

  • Data enablement is a top-down strategy that begins with leadership teams investing in the necessary technologies, ensuring that everyone has access to the data they need, and encouraging everyone to use the data to drive business advancement.

  • Data empowerment is a bottom-up approach to creating a culture where individuals believe they can use the data to their advantage. People who have this mentality are more willing to try new things and believe that failure is a safe opportunity to learn.

  • Data adoption can be defined as individuals confidently using available decision-making tools and dashboards. Have you put measures in place to monitor and measure user adoption? It takes time and attention to make everyone productive with data, thereby leading the way out of blind guessing or gut feeling.

To develop a data culture, data enablement, data empowerment, and data adoption must all be balanced and deployed in different ratios, based on the organization’s data maturity. This can be collectively called a “data literacy effort,” where data is used organically to address a business problem.

Meeting Data Challenges

Positioning a company as data-driven is easier said than accomplished. Before gaining value from data, there are a number of obstacles you must address and suggestions for doing so.

Obstacle #1: Anxiety around data

Increased expectations from teams to use data to make decisions creates stress and anxiety. Most of the working population did not get a formal data literacy education as part of their schooling; hopefully, future generations will. Everyone is not confident enough to step out of their comfort zone to experiment with data. Teams are not using the self-service analytics capability organizations establish due to concerns of failing in uncharted territory.

To overcome anxiety about data, make learning a fun exercise rather than a statement (“we have to be data driven”). For example, conduct internal data contests for teams to create solutions using simple data sets and tool of choice. Sponsor data games in which contestants have 15 seconds to look at a chart or visualization and answer a question. Events such as these help data to naturally become part of day-to-day operations.

Obstacle #2: Making large scale changes

Every organization's road toward building a data culture is distinct, and to be successful, it is crucial to begin small with a pilot project. Establish a trial user group and show how data can benefit their daily operations. Prior to making changes at the corporate level, start with feedback loops for improvement. When other team members see concrete results, they will be more motivated to participate. People pick things up at different rates and make mistakes along the way. To remove fear of failure and encourage others to join, create a defined safe learning atmosphere.

Obstacle #3: Regarding data as a purely technological subject

Don't think of data as just having the best tools and developing sophisticated tech solutions. There is no value if no one wants to use what you create. Every organization has people who mistrust the significance of data and prefer to rely on their accumulated experience over data. Recognizing that not everyone is curious about or eager to start using data is crucial to success in building a data culture. Be patient; complex cultural transformation necessitates tenacity to go on a new path. It takes work (and time) to shift people’s perspective, if data usage was not already standard practice inside the organization.

Obstacle #4: The shiny object syndrome

Choosing the most expensive tool on the market is not always the best option. Assess the team’s present abilities before selecting a tool that will work with a minimal learning curve. Numerous no-code solutions are available, enabling users to interact with data without prior coding experience. Additionally, whenever possible, attempt to keep with a single consistent tool, rather than confuse consumers with multiple tools.

Obstacle #5: Failing to define data success

Different organizations have different definitions of success. For some, it may be getting real-time data into the hands of the majority of team members to develop better solutions. It might mean making fewer mistakes, having fewer production problems, or improved customer service. For some, it could mean no data breaches.

Determining what success will mean to your organization in three, six, and 12 months -- as well as long term -- is a necessity. This will make it possible to continue on your path of progress.

A Final Word

Building a data culture is a long-term process, so enjoy the incremental victories. Thriving, not just surviving, in a data world is the objective.

About the Author

Sangeeta Krishnan is a BI and analytics leader who combines a blend of subject-matter expertise and practical experience from a variety of industries. Most recently, she joined Bayer as senior analytics lead for sales. She has worked with Fortune 500 organizations, not-for-profits, and everything in between, helping various organizations build their operations and monetizing data products from the ground up. Krishnan is a public speaker, content creator (with articles published in industry journals), and was recognized as a finalist of the Women in IT Awards 2018 (USA) in the Data Leader of the Year category. She is the author of Thriving in a Data World. You can contact the author via email or LinkedIn. More information is available here.

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