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Stage 2: Death by dashboards The data sharing challenge is often addressed by flooding each individual in the chain with even more data. BI (Business Intelligence) is one of the most mis-used tools in the enterprise. No wonder that a death cry of employee productivity is … “If we can justload thisinto BItoolsthatthe team can manipulate and dive into as deeply asthey want, everyone will have the same information!” Unfortunately, now everyone in the organization needs to be an analyst to understand what’s going on in their world. This often encourages the creation of shadow IT and data teams (local team members creating tools to answer the many specific questions their team has, outside the overall data and IT accountability chain). And then there is the magical ‘dashboard’. Originally conceived to provide a simplified view of what’s going on in the organization (think about the dashboard in your car with less than five data points on it), dashboards rapidly became the dumping ground for every item possible, often resembling the cockpit of the space shuttle with hundreds of dials, readouts and blinking lights. We’ve seen web site dashboards that users navigate or 100+ page PowerPoints created to cover every conceivable question and scenario for a discussion. Once again, large amounts of time are wasted in creating and navigating these tools in an attempt to reach answers. Both manual data processing and magical dashboards mask one of the core problems at this stage of maturity: multiple, inconsistent sources of truth. The same answers reside in different systems and each part of the organization looks in different places for their answers. Copies of data are spread around as it is crunched and report writers typically

2020/10/14 How to Measure Your Organization’s Data Maturity | byAlexBratton | Towards Data Science

https://towardsdatascience.com/how-to-measure-your-organizations-data-maturity-2352cbaf1896 4/8 grab whatever is easiest to access as only key individuals typically know where and how to get to the source of truth. This takes us right back to arguments about where the data in a report came from as the business logic applied to generate the answers isn’t readily apparent. Stage 3: Data tells a story As organizations evolve digitally, the data starts to tell stories to their teams. This typically starts when efforts focus around understanding what information employees in various roles actually need to do their jobs successfully. This focus simplifies the data discussion and brings business and IT into partnership to serve those employees. Focusing at the employee role level also uncovers that answering the most valuable questions (what is our sales performance — and why?) requires viewing and combining data from multiple systems currently being used across the organization. To prevent this from leading to yet more information overload for the team, aggregated data must be stripped down to what is specifically required to answer the questions at hand. This simplified, role-focused approach allows the data to be turned into glanceable answers for employees, giving them everything they need on a single screen to understand what’s going on and make better decisions accordingly. And, they didn’t waste hours clicking through a myriad of reports, tools and dashboards to surface needed data and manually mash it together.