How To Enable Self-Service Analytics Using Data Literacy

As a data and analytics leader with hands-on experience, I am often asked, “What are the key enablers for data and analytics?” Many organizations face challenges in analyzing their data and getting insights from it. The global pandemic has dramatically increased the speed of digitization across multiple industries. The speed of data creation has increased even further. In this global backdrop, any organization looking to leverage data to drive decisions must embrace self-service analytics. There is simply no other option to scale your analytics capabilities, no matter the size of your organization.

However, self-service is easier said than done. Most organizations need more analytics skills that can be leveraged in a self-service fashion. In this context, it is important to incorporate data literacy into your day-to-day business processes. Data literacy means ensuring your teams know how to use data for their daily jobs. They need to know how the data is organized in your systems. For any business domain — whether it's marketing, supply chain, finance, etc. — there are sets of metrics or KPIs that business teams use to track if they are executing as per the goals and objectives. These metrics are reported daily, weekly, and monthly at different frequencies. The data for calculating these metrics is key to all your insights.

Some business domain experts know their metric numbers well. They see what metrics each business domain should focus on and how to execute business activities to achieve the right key results. All businesses should invest in molding these experts into the role of Data Champions. These champions should be able to pinpoint why specific metrics are falling short.

Data Champions also have a crucial role in translating business metrics into technical requirements for data and analytics teams. In that regard, Data Champions need to educate data analysts on how to calculate different metrics and what data elements they will need to calculate those metrics. Once the data elements are identified, data analysts need to know where to get the data for those elements. For this purpose, data and analytics teams must provide self-service enablement tools like metadata/data dictionary catalog. Anyone in the company should be able to search a metric definition, what data elements go into that metric, and then which data tables to get data for those elements using a data catalog.

After data analysts find the data, self-service visualization tools can help them build visualizations that can help them find insights. Citizen data scientists can use this information to perform what-if analysis. More mature self-service features ensure data is organized in flat, easy-to-find, one-stop-shop tables. More traditional data modeling techniques focused on breaking data into normalized facts and dimension tables. It made sense to do that 10-15 years ago when compute technology was not advanced and efficiently organizing data meant data could be retrieved fast. Now, with the separation of compute and storage, it is possible to retrieve data quickly, even from very flat tables with many columns.

The advantage that a number of normalized tables provided is actually a hindrance to self-service. Skills for doing complex table joins to perform analysis exists in technical teams. However, this function must reside in business teams for faster insights and actions. Often business teams need to gain high technical skills. The job of a business data analyst can be made easier by lowering the bar for self-service and letting a lot more resources across departments participate rather than just the IT team.  This true-north data and analytics self-service is powered by data literacy and enabled by Data Champions. 

In this fourth industrial revolution, driven by digital technologies, every organization must find its Data Champions and use them as change agents to enable a data-literate organization. This type of data-literate organization can find insights quickly using self-service. In analytics, speed and agility are crucial activities that provide a competitive advantage. Most organizations need it, but only a few organizations have it now. Those who can build this capability will be positioned to out-compete and grow their business in this digital world. 

About the Author

Kiran Kanetkar is Vice President - Data and Analytics at Pendulum Therapeutics. He is also a member of the CDO Magazine Editorial Board.

Kanetkar is a seasoned technology executive leader with over two decades of experience in the field. He is adept at leading teams and establishing a data-driven culture. Previously, he was Senior Vice President of Data Engineering, Business Intelligence, and Analytics at Petco, where he led the company's digital transformation and improved analytics capabilities. Before Petco,  Kanetkar worked as Enterprise Program Manager at Warner Bros. Entertainment Group of Companies and as Enterprise Integration Architect at Abbott Laboratories.

He is passionate about using modern agile technology architecture to solve business problems, deliver business value, and remove technical debt.