{"id":1809,"date":"2024-08-20T17:32:28","date_gmt":"2024-08-20T17:32:28","guid":{"rendered":"https:\/\/solutionsreview.com\/thought-leaders\/?p=1809"},"modified":"2024-08-20T17:32:28","modified_gmt":"2024-08-20T17:32:28","slug":"self-service-is-the-outcome-not-the-driver-of-a-data-driven-organization","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/self-service-is-the-outcome-not-the-driver-of-a-data-driven-organization\/","title":{"rendered":"Self-Service is the Outcome, Not the Driver of a Data-Driven Organization"},"content":{"rendered":"

Originally published at\u00a0www.eckerson.com<\/a>.<\/p>\n

When I ask data leaders what their strategy is for creating a data-driven organization, most say that self-service is a key driver. They believe that if business users can service their own data needs, they will make timely, data-driven decisions. Furthermore, they believe that the way to empower business users is to give them a self-service analytics tool and appropriate training (i.e., data literacy).<\/p>\n

I wish implementing self-service was this straightforward. But it is not. You can\u2019t implement self-service with a tool, and no amount of data literacy can overcome other issues that business users face when using data. That\u2019s because self-service is not a tool; it\u2019s the outcome of doing everything else right in your data environment.<\/p>\n

Self-service is not a tool; it\u2019s the outcome of doing everything else right in your data environment.<\/em><\/p>\n

Self-Service Done Wrong<\/strong><\/p>\n

Self-service-as-a-tool often backfires. When data leaders equip business users with visual analytics tools, there are two unfortunate consequences.<\/p>\n

First, power users flood the organization with reports and dashboards, which often contain conflicting data and metrics. This creates confusion among business users, who don\u2019t know which report or dashboard to trust. As a result, they ask the data team to create new ones for them.<\/p>\n

Second, business users forget how to use the new tool or find it too complicated to get the answers they want in the format they desire. Once again, they ask the data team to create a custom report or dashboard for them. They also become territorial about their custom reports and fight anyone who attempts to standardize their metrics and calculations.<\/p>\n

So, the downside of self-service is chaos. There are more reports than ever, but people trust the data less; more people are empowered, but the data team still creates custom reports. Moreover, top executives are mad as hell because they can\u2019t get answers to simple questions about their business because the data never adds up.<\/p>\n

Self-Service Done Right\u00a0<\/strong><\/p>\n

Self-service is hard to implement because it comes at the end of a long line of improvements to the data program. Starting with self-service is often a recipe for failure.<\/p>\n

Before implementing self-service, data leaders need to focus on designing the right data architecture, the right data governance program, the right operating model, and the right project delivery methodology. Once those things are in place, business users can have a successful experience with a self-service analytics tool and their training might bear fruit.<\/p>\n