The Difference Between Predictive Analytics and Business Intelligence

The Difference Between Predictive Analytics and Business Intelligence

Although there is definitely a great deal of value in successfully predicting future actions within your industry, the nature of prediction is uncertain at best. You may have a gut feeling about what will happen, but in many cases, external conditions and unexpected events will override predictive measures and cost your business a great deal of money and time for no reason.

There is a better way to use information than to put it into a predictive analysis algorithm and expecting a result that you can base executive decisions on. Here is a better format for organizing data that a busy executive can use in a streamlined way.

– First of all, create a data dashboard with more detailed information than what you think that you will need.

In order to fully create a picture of the present and the future, you need a full picture of the past. Many predictive models bind themselves because the model itself limits the input that can be acknowledged by the program.

If you want to create real business intelligence that gives you more of an opportunity for a correct decision than a predictive model, then you must begin with a program that allows for substantive input. “Garbage in, garbage out,” as the saying goes.

– Secondly, invest in a trusted line of human analysts to sort through the output of your business intelligence programs.

Before the raw information from a particular campaign is ever presented to an executive to make any kind of decision, there should be a trusted group of analysts whose job it is to sort through the output and determine the most relevant data to present for future analysis.

In order to turn a predictive analysis into a true business intelligence campaign, there must be input from people who understand the things that computers do not. For instance, only a human can respond in real time to unexpected events until that information is input into a program. Humans are able to interpret data trends that do not fit a certain mold – this kind of data will only confuse a computer program, or worse, make it think that a particular situation is something else entirely.

– Third, any executive that is expected to make a decision from information must be presented information that is relevant to a certain topic.

Although it is up to the analysts to sort through the raw data, it is up to the executive to dictate what the actual goal of the analysis is. There should be a meeting of the minds before any data is processed so that the computers can be programmed to look for the right kind of information trends and the analysts will be fully informed about what the goals of the process are. Otherwise, information can be interpreted in a number of ways. This is of no help to anyone in the company.

Although very few executives have the time to ask the “why” questions when information is presented to them, analysts should have this information handy for themselves. Before presenting anything to an executive, analysts will have a much better chance of providing true business intelligence by collaborating on the data that they will present to the decision making class of the company. This process will also help to quantify any of the ideas that are presented to executives rather than simply relying on the algorithm of the program.

– Fourth, the external situation must be considered.

The problem with predictive analysis is that it cannot analyze the situation surrounding any of the results of a certain condition. If trend lines are the same at two points in time but the market surrounding those conditions are different, a different outcome will likely occur. There may also be new competition in the market that was not there any more. All of these factors must be considered. They are factors that may not be able to integrate themselves into a program at all; this is another reason that the human analysis of the computer results are so important.

If there is a new and relevant competitor in the marketplace, then you must incorporate their impact on the marketplace into any new results that are compiled. Be sure that this is a part of the data table that your analysts put together for the executives and decision makers.

Last updated by at .