To survive and thrive in today’s hyper-competitive business environment, companies need the ability to act quickly on business opportunities and threats. We’ve entered the Age of Analytics.
Organizations that collect, analyze, and act on the reams of customer, market, and operational data available to them, position themselves to respond immediately to changing market conditions, as well as stay ahead of competitors.
The Analytics Maturity journey begins with “Measure,” where executives and employees alike are able to keep their fingers on the pulse of their business with metrics delivered to them wherever and whenever they are – at a child’s concert or on the road between client business meetings. This is where the data journey starts, serving as the springboard for deeper discovery and diagnosis.
In the “Diagnose” stage, business leaders are able to visually interact with and drill into all of their data to discover additional answers to questions that arose during the Measure stage, e.g., “What caused customer satisfaction to drop over the past month?”
During this stage, executives and other decision-makers are able to determine why an operational change occurred and examine the root cause of something that happened.
In the next step of Analytics Maturity, “Predict & Optimize” decision-makers begin to use advanced analytic techniques to help determine where the organization is headed and establish the best course of action to take.
It’s this stage of Analytics Maturity when business leaders can forecast with greater certainty (e.g., “If I make this change to customer support, how will this impact customer satisfaction?”), and proactively obtain answers that lead to stronger business outcomes and reduced uncertainty.
In the next stage, “Operationalize,” organizations are able to put all forms of data and analytics into the hands of front-line, day-to-day business users and decision-makers – sales people, marketers, engineers, business unit managers, etc.
This is when analytics steps outside of the domain of analysts and data scientists, becoming part of the business workflows for everyday users. Knowledge workers from across the enterprise are empowered to use analytics to identify trends and make their day-to-day decisions – without having to become analytics experts or data scientists themselves.
The fifth stage of Analytics Maturity, “Automate,” is where decision-makers begin to take advantage of the time-sensitivity of data. In Automate, organizations leverage real-time streaming data in their analytics to respond immediately to an event or transaction as it is happening.
This stage is about taking fast advantage of an opportunity or tackling a potential risk that has emerged. Either humans or systems act on the data to make immediate decisions or take predetermined actions. For example, business rules used by a fashion retailer may send mobile offers to loyal customers when they enter a store during a particular promotion.
The final step of Analytics Maturity, “Transform,” is when a data-driven mindset is embedded into the culture of the organization.
At this stage, employees across the organization – from the executive suite to front-line knowledge workers – are applying analytics to as many use cases as possible. Data and analytics are built into the innate workflow and as part of the organization’s day-to-day operations, helping to enable a culture of continuous improvement.
Analytics is everywhere and decision-makers don’t even think “analytics” anymore. Analytics are simply engrained into the fabric of the company, used to foster data-driven decisions to attain the paramount business and operational results.