Business intelligence is the process of collecting business data and turning it into information that is meaningful and actionable towards a strategic goal.
Predictive analytics uses machine learning and advanced statistical modeling to analyze corporate and customer data, find patterns, and predict future outcomes. It allows an organization to take advantage of huge data sets that might otherwise be wasted.
Predicting the future is what data was made for.
Enterprise businesses gather huge amounts of customer data. But in many cases, they aren’t taking advantage of it all because they are only gleaning insights from the tiny percentage their analysts are able to manually dig through.
Predictive analytics is designed to help these businesses tap into this untapped potential. Hidden in that data are patterns that reveal meaningful insights that — when used effectively — can have a very real impact on the bottom line. Predictive capabilities draw on machine learning and advanced statistical models to dig automatically through enormous amounts of data, searching for those patterns that most analysts simply don’t have time to look for.
At the end of the day, predictive analytics tools help companies get the most out of the data they gather by helping them find the insights the really matter.
Analyzing data means asking it questions and getting meaningful answers. The powerful and interactive analysis tools of today’s business intelligence solutions make it easier to ask data an increasing number of questions and getting meaningful answers–including “what-if” scenarios, multidimensional slicing and dicing, mashing up of data with geographic mapping and much more.
Data analysis can answer such questions as:
- How are my products performing? Which ones are profitable?
- Is growth real and are new customers being retained over time?
- Who are my customers? What about by territory? Or by demographics? Which ones are profitable and if so, how profitable?
- What is the untapped potential of territory X? If we opened a new store/branch, would new customers be attracted and how many existing customers would be pirated from our other stores/branches?
The goal of sophisticated analytics is to enable decision-makers to understand data, to spot patterns between numbers, to identify trends and the reasons behind them–simply put, to contextualize data and gain insight into the unknown.
Tuff Risk’s data analytics digs into the specifics of data, using advanced statistics and predictive analytics to discover patterns and forecast future patterns. Data analytics asks “Why did this happen and what can happen next?” Business intelligence takes those models and algorithms and breaks the results down into actionable language.
According to Gartner’s IT glossary, “business analytics includes data mining, predictive analytics, applied analytics, and statistics.” In short, organizations conduct business analytics as part of their larger business intelligence strategy. BI is designed to answer specific queries and provide at-a-glance analysis for decisions or planning. However, companies can use the processes of analytics to continually improve follow-up questions and iteration.