How Do You Do Predictive Data analysis?

Every day businesses are exposed to many risks; environmental factors cause uncertainties that could even threaten the business’s survival. Predictive data analysis uses existing data to predict future outcomes and reduce uncertainty risk. Using math, a predictive analysis model analyzes patterns in the input data and predicts future patterns and trends as the output.

Steps to perform predictive analysis

Artificial intelligence and machine learning aren’t just used in some remote laboratory for space crafts. Their broad uses have put them at the forefront of most businesses. And to accommodate this, many companies have shifted gears to becoming data-centric. In order to perform predictive analysis, a business needs to have information.

Define the objective

When fellows have many questions but only get one chance, which question should they ask to clarify most of their doubts? While they get more than one chance in predictive analysis, it is time-consuming. Define a problem statement along with its scope and objective. One will need to identify and input information according to this desired outcome.

Collecting input data

Once the objective is clear, folks need to input the related information sets. This is the most crucial stage as this will direct the output. If people have information from varied sources, they must organize it in the same format before putting it in. Relevance of the information to the objective will determine the accuracy of the predictions, so filtering that information as per the scope of the objective is essential.


All the data don’t need to hold the same weightage. Depending on the objective, some information will be more significant than the rest. Folks need to analyze their input information and filter it as per their requirement. Some information may be redundant, some may be erroneous, and some sources may not be trustworthy. All these factors will come while filtering.


Before analyzing the data, fellows must build their model or outsource. While building their own model sounds ideal, it is extremely capital intensive and will need data scientists on board. Not every business can afford that; even when some can, they lack the expertise to build a model that accurately predicts. Not to mention, folks may have more than one problem statement, and if they want solutions to all of them simultaneously, their best bet is outsourcing. There are quite a few options to get AI-driven predictive analysis.

To run a successful business, you don’t need to be a professional in AI; outsourcing your analysis will give you more than predictions. It will give you insights. A predictive analytics model for a specific problem using a separate set based on your industry will lead to accurate outcomes.

Validate the model

Last but a necessary step in prediction analysis is validating the model, to ensure that the model gives out accurate predictions. Since change is constant, you need to make sure your model can accept and evaluate the change and modify the results accordingly, and in doing that, you need to keep testing it. You can use different sets to ensure robust outcomes.

This may be new territory for you, and just because you’re not an expert here doesn’t mean you can’t reap the benefits of predictive modeling. Being an information-centric business will lead to informed business decisions with less possibility of failure, and that’s worth the investment. As business2community reports, The value of predictive analytics, “a helpful data processing technology that may help organizations analyze and forecast customer behavior and organizational performance,” much exceeds its price tag.

Adam Hansen