In a previous post, we described how the third stage of the Analytics Maturity Model, “Predict & Optimize,” allows organizations to plan for the future and use advanced analytics to become more proactive in their analytic decision-making and to begin to project analytics forward to produce better business outcomes and reduce risk.
The fourth stage of the Analytics Maturity Model, “Operationalize,” is where analytics steps outside of the domain of analysts and data scientists. This is where we put the Measure, Diagnose and Predict & Optimize capabilities into the hands of front-line, day-to-day business users and decision-makers – sales people, marketers, engineers, business unit managers, etc.
Business is moving at a breakneck pace. To keep up, decision-makers want, need and demand access to data. But most business users aren’t analytics experts and don’t have the time to become experts because they need to focus their attention on their everyday responsibilities.
“Operationalize” makes analytics part of everyday users’ business workflows. It empowers knowledge workers from across the enterprise to use analytics to identify trends and make informed decisions, without requiring them to have advanced training in data analysis or statistics.
For non-analytic experts who need to make data-driven decisions, self-service analytics, with step-by-step analytic workflow embedded within an analysis or analytic application, is empowering.
Through the use of guided analytic applications and dashboards, analysts and data scientists can create visual, highly interactive analyses for end users that can be used to strengthen their day-to-day decision-making.
The next step of Analytics Maturity, “Automate,” is where decision-makers can make decisions based on real-time data and systems can be programmed to take immediate actions based on pre-defined, real-time events.
