What is data-driven decision making? 8 best practices

SaaS and the cloud make corporate data accessible to nearly everyone. But to make the most of it, your organisation needs to mentor and coach employees at all levels 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 makes data-driven decision making open to the whole team.

Just as important, you must celebrate your team’s victories with data-driven decision making, encouraging your workforce’s transition to a knowledge worker haven.

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, its storage location, and the collection process. 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.

Discover how to fuel your decision making with data and analytics features on platforms such as Confluence, Jira, and monday.com. Then, encourage your power users to gradually include data in their internal reporting, from status reports to management.

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. It could be as simple as using Confluence Analytics to track how your internal content is performing.

Expanding beyond your Atlassian stack requires a cloud-based data warehouse to serve as a central repository. Amazon Redshift and Snowflake are two popular options. There’s also a requirement for Extract, Transform, Load (ETL) tools to extract data from different sources, transform the data into a usable format, and then load it into your data warehouse. Talend is a well-regarded option for ETL. Tableau, Looker, or Power BI come into play to analyse the data you store in the data warehouse and run reports. As your data infrastructure grows, data pipeline management tools become necessary to monitor the pipelines moving data between your systems.

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.