Six Stages of Transforming into a More Data-Driven Organization
In This Article
Adopting a more data-driven culture and harnessing the power of data analytics can be a game changer for companies looking to begin or speed up their digital transformation journey.
The more seamless data can be analyzed, models built and insights integrated into the day-to-day workflows, the more rapidly organizations can innovate and make decisions on the fly.
Steps on the Data Maturity Curve
WWT's Data Maturity Curve is a framework to illustrate the typical stages organizations go through to achieve a data-driven culture. The first step in any data-focused transformation is to understand where your organization presently sits so you can chart a path to move up the curve.
Zero: Lack Data for Analytics Projects -- Key data sources are never or infrequently collected and stored for analysis; manually captured with significant errors.
One: Isolated Data Projects -- Business units work with data throughout the enterprise in an uncoordinated fashion with no shared definitions and process. But the beginnings of a data-driven culture are present.
Two: Secure, Reliable Data Repository -- Data warehouse or lake systems with well-defined management and governance are utilized to provide a foundational system for reporting, data science and key operational users.
Three: Governed Self-Service Access -- Power users have access to expanded data for exploration with data access granted based on levels of expertise. Reporting teams focus on operational analytics while business users run queries and extract data as needed.
Four: Scientific Hub for Data Insights -- Ability to rapidly deploy technology platforms designed to solve specific business problems. A well-governed data environment and high-functioning data science team is driving thought leadership in a variety of areas.
Five: Insights Driven Culture -- Data-driven insights are ingrained in processes and accessible across the business to measure and drive action, resulting in the ability to seamlessly integrate data and insights into new business policies and processes.
According to a recent survey from New Vantage, more than 90 percent of industry leading firms are increasing their investment in big data and analytics to become more agile and competitive.
However, investment alone doesn't lead an organization up the Data Maturity Curve. Many companies struggle to adopt an insights-driven culture due to a lack in organizational alignment or cultural resistance.
The importance of thoughtful use cases
Customers should identify and prioritize specific use cases that will create value for the organization before diving deep into which algorithms or platforms should be utilized. Thinking about the value upfront and pinning it to a specific use case can make a world of difference during implementation, and help build momentum for future.
When customers reach the pinnacle of the curve, they will have achieved the status of having an insights-driven culture. Data will be democratized and the insights gained from data discovery will be frictionless. Moreover, data science teams will have a seamless path from model training to productionalization.
Customers that have reached the peak of the curve will be able to confidently tackle more complex use cases and more frequently drive value.
WWT is constantly evolving to provide the most strategic and innovative recommendations to our customers. As Wayne Gretzky famously said, "I skate to where the puck is going to be, not where it has been."
To that end, WWT is also looking beyond the curve through initiatives such as our Artificial Intelligence (AI) R&D program, where we are investigating some of the most cutting-edge algorithms, processes and platforms we believe will be disruptive in this space for the years to come.