The components of the data-driven culture Modern companies are typically overloaded with large volumes of heterogeneous data, from multiple sources. They make significant investments on technologies and architectures such as data marts, data warehouses, data lakes and operational data stores with sophisticated on top. Business Intelligence, Analytics and Predictive systems Nevertheless, data & technology does not ensure better decisions unless an additional component is added to the mix: . the data-driven culture The “Data-driven” here implies much more than just technology and data. It means you need a special mentality. A leadership attitude. This makes it possible for your company to make informed decisions by using data systematically to improve business performance. The data-driven culture As the data engineering lead at , I’ve seen several companies struggle to establish this data-driven culture. datamine decision support systems Check also: how to establish an innovation culture These are companies with sophisticated data-warehouses, who are failing to add business professionals in the data analysis loop. In some cases they’re abandoning advanced analytical systems due to a lack of readiness by the company and lack of adoption by specific leaders or teams. To drive the , corporations need to first establish a stream of clean, accurate, reliable and active data feeds reflecting corporation’s business activities. All these qualities are critical for employees to - any limitation can prove to be the single point of failure in making the data-driven vision happen. data-driven culture trust the data, the technology and the tools Assuming the basics are there, employees need to start experiencing the benefits of being data driven. It’s important for them to understand that, by using data, they can improve their decisions**.** Their jobs can be more efficient with a . significant and measurable impact on the overall business Here are several ways to push employees in this direction: Share information & knowledge on data models, tools, reports and insights via quick-start sessions Systematically with possible follow ups in order to ask for interpretation and ideas/proposals on how to make them share insights and interesting data findings actionable on how the use of data allowed with real financial gains for the company Share real success stories smart decisions and data strategy of the company. Ask for ideas, proposals, requests Share the vision Capture needs and requests for new reports, additional data points, or new data analysis capabilities, data models, visualizations Ask for feedback on the overall data-driven experience. with known, hidden patterns in your data (artificial or real ones) and asks teams to discover them Run mini-hackathons Establish on data analysis and reporting tools formal training sessions Make data analysis a must for every business proposal, idea, business case The self-service insights store Every employee should be able to easily understand the performance of the company, the competitors, the corresponding local and global economic environment and the market dynamics. Employees need to build a habit of consuming well-defined (and designed) dashboards presenting and other insights, along with the outcomes of particular business decisions. business performance, trends, social signals As employees become data-aware and the complexity of data increases, a new class of systems is needed: a to empower business users perform their own information exploitation, analysis and visualization; to submit questions, consume dashboards, predefined reports; to dynamically generate reports via simple user interfaces; to easily define their what-if scenarios and get instant answers. self-service insights platform While the typical data warehouses and data lakes target particular classes of users (like data analysts and data scientists) the is open to any authorized employee - no special skills required. self-service insights store It’s an easy-to-use data analysis surface (powered by a range of data technologies, analytical and reporting engines) where and related reporting elements. insights and analytics are exposed in a modular form as active, reusable widgets This could take the form of a configurable and adaptive dashboard system which is based on an expandable range or predefined, active insight widgets. This could eventually become a repository of insights where new widgets get deployed and become usable by employees through custom dashboards and integration scenarios: Employees can easily reuse these insight widgets and combine them in customized, named dashboards which better serve their business needs. self-service insights store Personalized Insight experiences Interactive data insights stories provide a modern way to package and communicate data findings and interesting patterns, within the corporation. Either manually or automatically generated by AI, data stories can present a particular aspect of the business, the impact of certain decisions, and the results of large-scale online experiments. The focus is on the user experience so that . the story is delivered in a smooth, interactive way with multiple dimensions and entry points, taking into consideration the perspective of particular users according to their role, seniority and department within the organization Insights from smart buildings Smart buildings could be directly connected to the self-service insights store in order to access white-listed widgets (suitable for exposure within the building) and visualize business insights through the network of public screens within the corporate buildings. This experience could be (to the space, seasonality, team) delivering relevant insights to the right users at the right time. For instance public screens in the sales team area present sales performance metrics while in IT they experience network utilization KPIs. contextual Thanks for reading. Here are some other articles I’ve written that you might enjoy. 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