Predictive Analytics vs. Business Analytics: What’s the Difference?
Predictive analytics is a hot issue in today’s business and information technology world. People in the business intelligence community are both fascinated and fearful at the same time.
Fascinated – because the progress in computing technology, such as decreased storage costs and cloud computing enables us to explore great new capabilities— not just to solve business problems but to discover undefined relationships within the vast amounts of data.
Fearful – because it is really hard to master everything that has become a part of predictive analytics landscape – such items as big data and data mining, statistical modeling and forecasting. Not only that, the definition of what belongs and what doesn’t has not yet been decided.
Fear not: Predictive analytics in one form or another has been around for at least 50 years. The easiest example is the FICO score. By using certain known information (payment history, credit utilization, and residency length), lenders can make automatic decisions about issuing credit and loans, resorting to manual review in certain instances. In past years though, the advances in technology has allowed many companies (not just financial powerhouses) to collect and retain huge amount of data.
The challenge is that as more data has become available, many companies are suddenly unable to adapt to the ever-changing landscape. The data is there; however, internal IT departments are frequently operating at over capacity and not able to handle big data for these reasons:
- They’re focused on running “business as usual” and maintaining day-to-day operations
- They lack personnel needed to successfully executing predictive analytics projects
- Internal business analytics projects have just reached maturity
- They fear failure, lack strategic vision, and are operating within data silos
While business analytics projects have matured, predictive analytics have still yet to gain similar traction. There are many companies utilizing predictive analytics in a profitable way and with positive impact on their bottom line. However, they are not fond of sharing the details, since it might represent their competitive advantage.
Some industries like finance, insurance, and travel survive on predictive analytics, whether machine-based or human-based. However, other industries cannot afford to ignore predictive analytics, as it has a potential to create great potential advantage.
In my opinion, the primary difference between predictive and business is that business analytics is reactive in nature – analysts and management observe and act on historic measurable information, such as revenue, profit, loss, employee turnover, customer churn. Predictive analytics, on the other hand, is proactive: it can assist companies in actionable intelligence and anticipative action where a human intervention might not be required at all. The software will trigger action, be it sending a marketing email with a coupon or automatically creating a position vacancy notice on a job site, for example. Forecasting, projecting, and predicting: this is what distinguishes predictive analytics from traditional business analytics. Another key difference is that real-life usage of the predictive analytics might include utilizing unstructured public (ex. Twitter) or proprietary data (ex. subscription to retail feeds or court decisions) for immediate decision-making. Business analytics uses traditional data structures, such as data warehouses and data marts.
It is likely that in the next few years both will blend together and be indistinguishable, as the border between them gets thinner and most companies would find new ways of using it. Meanwhile, it is advantageous to at least explore how predictive analytics can boost the growth of your business. If you want to learn more about how predictive analytics can help your company, contact us.