Product Architect | Tech Inventor | Entrepreneur
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 Business Intelligence, Analytics and Predictive systems on top.
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.
As the data engineering lead at datamine decision support systems, I’ve seen several companies struggle to establish this data-driven culture.
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 data-driven culture, 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 trust the data, the technology and the tools - any limitation can prove to be the single point of failure in making the data-driven vision happen.
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:
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 business performance, trends, social signals and other insights, along with the outcomes of particular business decisions.
As employees become data-aware and the complexity of data increases, a new class of systems is needed: a self-service insights platform 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.
While the typical data warehouses and data lakes target particular classes of users (like data analysts and data scientists) the self-service insights store is open to any authorized employee - no special skills required.
It’s an easy-to-use data analysis surface (powered by a range of data technologies, analytical and reporting engines) where insights and analytics are exposed in a modular form as active, reusable widgets and related reporting elements.
This self-service insights store 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.
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.
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 contextual (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.
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