How to implement data-driven decision making in 7 steps

To make the most of the data you have available, your organisation needs to coach employees on how to pull data from business-critical applications.

It starts by providing training and resources to enhance data literacy across the workforce, including educating employees on how to interpret and use data effectively. Equipping your users with self-service reporting tools opens up data-driven decision making for the whole team.

It’s just as important to celebrate your team’s successes when they’re achieved through thoughtful, data-based decisions. This helps encourage knowledge workers to use information and knowledge to make smart choices.

Identify the specific business objectives and goals you want to achieve through data-driven decision making. These would usually involve:

• Improving operational efficiency

• Increasing customer satisfaction

• Optimising resource allocation

Next, assess your organisation's current data landscape. Determine what data you have, where it’s stored, and how your company gathers data. Then create a data acquisition strategy from that assessment, including data sources, collection methods, and frequency. You also need to define a data governance strategy to ensure data quality, security, and compliance with relevant regulations.

The quality and accuracy of your data are integral to the process, requiring your organisation to invest in implementing data governance practices to maintain data integrity, consistency, and reliability. Establish clear data standards, documentation and data collection, storage, and maintenance processes. Also, regularly audit and monitor data sources to identify and rectify issues promptly.

Even if you're starting small, data infrastructure is key. If you’re a Confluence user, it could be as simple as using Confluence Analytics to track how your internal content is performing.

To make data easy to access, you need a cloud-based data warehouse, like Amazon Redshift or Snowflake, to keep everything in one place. You’ll also need ETL (Extract, Transform, Load) tools to collect data from different sources, clean it up, and format it so it’s ready for use. One popular option is Talend. Once your data is stored, programs like Tableau, Looker, or Power BI help you analyse it and create reports. As your data system grows, you’ll also need tools to keep track of how data is moved around, ensuring it all flows smoothly.

Process and analyse your data to derive meaningful insights. Spend time understanding the patterns and trends to guide your team’s decision-making process.

With your data tools, infrastructure, and strategies in place, your organisation is now fully equipped for data-driven decision making. You should expect the first attempts to take longer as people learn the process and tools, and adjust to how data changes the conversation around business decisions.

Regularly revisiting the outcomes of your decisions and iterating based on new data or changes in the team’s goals or priorities is vital. This ensures a continuous improvement cycle, at the heart of any successful data-driven decision making process.

Data-driven decision making can offer your organisation a competitive edge. The key is to start small and scale up, including your culture, strategy, and tools.