Analytics to Drive Decision Making Requires Three Key Components
The three pillars of analytics are vital to the formation of data that helps businesses understand past activity, potential activity, and how to mitigate future risk.
Paul Francis, Business Intelligence Practice Leader at Digital Intelligence Systems, LLC (DISYS) recently gave a webinar on “Driving Competitive Advantage with Customer Analytics” where he discussed the three pillars of analytics and their importance.
“Analytics is what allows a company to drive their competitive advantage,” Francis said. “It allows companies to focus on organic growth through the data it receives and the interpretation of that data to inform strategic growth plans.”
With data in hand, your company can begin making strong decisions to support the business and growing its customer base. But with all the data out there, how can a company know which data is useful and meaningful?
All data should fall into one of three pillars – descriptive analytics, predictive analytics or prescriptive analytics –. By categorizing data into one of these three pillars, it can be shaped into intelligible information that can describe what is happening in your business, predict what could happen and prescribe solutions to help mitigate issues along the way.
“Descriptive data allows you to look at your data and understand what took place in the past – last week, last month, or even yesterday,” Mr. Francis said. “You can derive this data by aligning certain data points – such as geographies and customers – and link those data points to what happened in your business.”
These historical descriptive data points are considered to be the most traditional form of analytics and give senior leadership information on how the company has performed in the past.
“Descriptive analytics is the core component of analytics today,” Francis said. “60-65 percent of all Business intelligence information is still focused on descriptive analytics.”
Francis continues by saying descriptive analytics on its own does not prevent or help an organization to preempt events – it simply describes past events and can allow for interpretation in preventing future negative impacts on the business.
“When companies are focused on how to keep a competitive advantage, the historical data is the driving force,” he said.
Predictive analytics is the next step in data diving. According to an article in Information Week on Big Data Analytics, predictive analytics utilizes a variety of statistical, modeling, data mining and machine learning techniques to study historical and recent data, allowing analysts to make predictions about future business events.
“The shift from descriptive analytics to predictive analytics is critical,” Francis said. “Companies are finding predictive analytics to be extremely powerful in foreseeing negative and positive events. It can give insight into churn rates and business drop off. Predictive analytics is a bold new wave and can give meaning to past events.”
Predictive analytics takes into account all historical data, allows for the linking of key data points over time and can predict customer behavior.
“The cost of new customer acquisition is normally high for most businesses,” Francis said. “It is easier to keep current customers and is more cost effective. Predictive analytics allows leadership to see possible downtrends and compensate for it.”
The last pillar in the analytics framework is prescriptive analytics.
“As it implies, it is a means of telling you about the probability of an event and gives you the data points needed to mitigate it,” Francis said. “It is a new and growing area of analytics but has been embraced as a critical component.”
Prescriptive analytics goes beyond the descriptive and predictive models and recommends one or more courses of action – and shows the likely outcome of each decision. In addition, prescriptive analytics requires a predictive model with two additional components: actionable data and a feedback system that tracks the outcome produced by the action taken.
An article in CIO Magazine on “The 5 Things CIOs Should Know about Prescriptive Analytics” says prescriptive analytics requires more data integration.
“Data scientists spend about ¾ of their time preparing data sets and only a quarter running analysis,” said Forrester Research analyst Mike Gualtieri. “That imbalance could worsen with prescriptive analytics. CIOs can help by making it easier and faster to compile required data.”
But the extra time it takes to create prescriptive analytics is worth it, Francis said – especially if senior leadership takes the time to understand it.
“Information is a critical component in a company’s ability to derive marginal advantages over competitors,” Mr. Francis said. “So driving competitive advantage with analytics is very important – and understanding each pillar vital.”
Note: Digital Intelligence Systems, LLC (DISYS) is a global managed staffing and services company with core capabilities in managed staffing services, agile services, application development, business intelligence, cloud enablement and enterprise resource management. The webinar on ‘“Driving Competitive Advantage with Customer Analytics” can be found on the DISYS YouTube Channel as well as other informational webinars and videos.