Predictive analytics is the practice of gathering and analyzing previous data in order to forecast future results. Aggregating numerous datasets bridges the gap between departments, business processes, and data kinds (structured vs. unstructured). However, just aggregating different data points does not always predict future behavior. To find trends in huge data, predictive analytics employs statistical approaches like as data modelling, machine learning, and even artificial intelligence.

While these patterns cannot forecast what will happen in the future, they can identify trends, herald disruptive market shifts, and enable more data-driven decision making. Any field that captures data is a candidate for predictive analysis. Everything from enhancing cybersecurity to developing more targeted marketing to strengthening actuarial performance is fair game.

Predictive analytics in healthcare

Healthcare is a primary use case for predictive analytics. A major issue in healthcare is the difficulty of predicting patient risk. Actuarial teams need to establish optimal insurance rates and governmental requests for reimbursement for members with various health issues.

Due to this need, health insurance agencies were some of the first companies to adopt big data practices. Actuaries use predictive analytics to determine things like a patient’s predisposition for developing a worsened condition, or a patient’s likelihood of participating in sponsored wellness activities.

Predictive analytics allows health insurance companies to examine patterns of risk among patients of similar age, with similar conditions, and from similar social determinants of health. Armed with this information, health insurance companies are able to make more informed financial and ethical decisions.

Predictive analytics in finance

Lending, a key function of the financial services industry, has been revolutionized by predictive analytics. Before a bank gives out a loan, they want to make sure that a customer is trustworthy. Ultimately, they want their money back. So how do underwriters gauge that trust?

Until several years ago, underwriters would judge an applicant based on past performance and personal hunches. Underwriters would review the applicant’s history and debt-to-income to arrive at a convoluted interest rate. As new financial laws emerged, lenders had to develop a more statistically relevant method for underwriting.

The lending industry underwent a revolution when third-party predictive analytics models like VantageScore and FICO Score became available. These models allowed lenders to calculate accurate risk-based interest pricing, and limited subjective bias. Instead of basing interest rates on a few outdated metrics, the VantageScore and FICO Score models are based on the performance of millions of borrowers with similar spending tendencies.

Read Also: How do I Become a Data Analytics Expert?

You must create a data-driven culture within your business to ensure you are generating the type of data you need to get predictive analytics right.

1. Define the business result you want to achieve

Predictive analytics allows you to visualize future outcomes. Clearly defined objectives help to tailor predictive analytics solutions to give the best results.

Some examples of business questions to which predictive analytics can provide answers are:

• Which of my customers/customer segments are likely to remain loyal without any incentives?
• Which product will most likely be in demand during the end-of-year sale?
• Which of my B2B customers is likely to default on payments?
• Which of my suppliers will likely not deliver raw materials on time?
• Which areas of production might see an increase in costs in the coming quarter?

You may discover that your existing data is not sufficient to answer your questions. In these cases, you will either have to work toward collecting relevant data over a period of time or modify your questions to tackle the same challenge from a different angle.

2. Collect relevant data from all available sources

Predictive analytics models are fed by data. Therefore, identifying the right data that can answer your business questions is important. If you store your data in spreadsheets, pulling them into your predictive models can get tedious and may not even be possible in all cases.

Instead, use your CRM applications, point of sale software, marketing tools, and other software to store relevant data. These tools allow you to store larger amounts of data (often in the cloud, helping you save IT infrastructure costs) in a neat fashion.

You can then use data extraction tools to pull data from multiple sources. APIs also allow you to connect multiple apps to collect data. Database systems, data warehouses, and data lakes are other resources you can use to store large quantities of data.

3. Improve the quality of data using data cleaning techniques

“Garbage in, garbage out” is a computing term referring to the fact that low quality input generates poor output values.

Your predictions will be grossly inaccurate if your input data is poor. You must ensure that sales people, marketers, and other employees enter the right data values in the prescribed format. This helps to reduce the time spent cleaning and formatting the data.

You’ll also need to prevent and fix duplicate records as well as normalize data to ensure consistency in records. Most business intelligence software solutions offer data cleaning features such as data elimination, data standardization, data harmonization, and data profiling.

4. Choose predictive analytics solutions or build your own models to test the data

Building your own predictive analytics model requires expertise in data science. You will need the help of data scientists or someone with advanced analytics skills to build predictive models from scratch.

You have the options of outsourcing this work to a consulting firm that provides analytics services or seeking connections with researchers at universities for their support.

But, if cost concerns prevent your small business from engaging experts, there are many software solutions available that come embedded with predictive modeling tools.

Though these tools may not offer the advanced knowledge that a skilled data scientist can bring in, they offer built-in predictive models, are easy-to-use, and come at a lower price point. Predictive analytics software can be a good starting point for small businesses trying their hand at forecasting.

Look for these key features when choosing predictive analytics software

5. Evaluate and validate the predictive model to ensure robustness

Evaluating and validating your predictive model with alternate data sets allows you to identify weaknesses in the model, as well as helping ensure that the model works well under different scenarios. There are different techniques for validating predictive models, such as cross validation and regression validation.

Don’t worry: Even if you’re unfamiliar with these techniques, nowadays, most predictive analytics tools provide model validation capabilities within the software. You can use these automated features to check the robustness of your model.

Finally, embed the predictive models into your business processes and use the results to make better business decisions.