The idea of being a \u201cdata-driven\u201d organization has been around for several years. The pursuit of making better decisions armed with facts is a good one.<\/p>\n
As part of achieving this goal, a lot of companies have invested in data scientists, data lakes, new open-source storage capabilities, and a whole array of new and changing tools. These are not necessarily bad things or wrong decisions. However, this focus on being data driven sometimes has its own set of challenges.<\/p>\n
In this blog, we walk through five key points to consider when pursuing your initiatives to be (more) data driven.<\/p>\n
Initially, becoming a data-driven organization<\/a> tends to be about identifying and centralizing data. Companies pursue creating data lakes, data ponds, data warehouses or a myriad of other data repositories. They pull all the data found together into one source. This takes a lot of time and energy. While centralizing the data to enable insight isn\u2019t necessarily bad, where do you start? How much data is enough before the business side can see the value?<\/strong><\/p>\n Organizations often get so involved in the architecture and technology efforts that they lose sight of the intended value. The timeline gets increasingly longer, and the business gets frustrated. Rather than trying to get all data into a centralized source, consider working with the business to identify core needs and related metrics. Identify the key questions you need to ask and the data that would enable the answers. Then you can focus on sets of related data and iterative insights to help drive business answers over time. You\u2019ll find as you uncover these answers the business needs may change.<\/p>\n Seek to understand business priorities and metrics, along with the impact they can have. A business-first mindset will energize your team as solutions drive insight and productivity.<\/strong><\/p>\n Over the past several years, I have seen several companies relinquish their pursuit of being data driven to their IT department. They ask IT to produce the best path forward. From a pure technology perspective, many options, tools and potential skills are required. Machine learning and artificial intelligence<\/a> can drive a lot of potentially interesting insights that excite most technologists.<\/p>\n However, basic business intelligence and reporting can also provide the right insight for many businesses. There is value to almost all solutions, but the value depends on the organization’s need and maturity. So, where do you start? It is important to understand what the business needs to improve its decision making and customer experience.<\/strong> Ask yourself, what skills, data and capabilities are already in place?<\/p>\n Understanding the basics is often critical to making the right decisions with more complex insights. Let the business side define the targeted goals, domains and expected potential impacts, so IT can apply the right technology. Don\u2019t forget to invest in training for both technologists and data consumers.<\/p>\n I have worked with a lot of data people. Whether described as business intelligence experts or data scientists, they have great insight and understanding of their data. Many also have the ability to produce interesting and (often) complex reports and dashboards. The thing that often differentiates good data people from great ones is the ability to provide context.<\/p>\n The numbers are great, but how do they fit within the expectations of good and bad? The most valuable reports and dashboards explore and answer “Why.”<\/strong>\u00a0If your reports only help provide awareness around what happened but cannot explore causes or potential actions, are they driving the intended value?<\/p>\n2. Technology shouldn\u2019t drive the business. The business should drive technology.<\/h2>\n
3. Data is good. Data with context is even better.<\/h2>\n