The Rise of Predictive Analytics and Data Mining

If you own a business, you may have noticed a buildup of data on your systems. You may not have realized it, but these databases have enormous potential for providing richer insights. Thanks to faster and cheaper computers, it is now easier than ever to invest in analytical software delivered via the Software-as-a-Service (SaaS) model.

The opportunities to derive accurate insights grow as databases grow and become available on public networks. Furthermore, predictive analytics tools enable you to make accurate future forecasts and predictions based on various data types.

Businesses frequently require the assistance of a data mining agency to compile relevant data from online and offline sources. Predictive analytics can be used to gain insights from such data.

‍How is predictive analytics influencing data mining?

‍Most people define data mining as sifting through large amounts of data to find consistent patterns and symbiotic relationships.

It also refers to manually and automatically mining data from various sources. This could include converting CRM data into excel sheets or specific formats, or it could be as simple as mining information from different online resources and compiling it in a Word document.

It can also refer to the complex processes of identifying patterns in Big Data using advanced statistical tools to make meaningful predictions. Predictive analytics tools are typically used in such techniques. The advent of predictive analytics has greatly aided businesses in gaining insights and making accurate predictions.

Here are some of the advantages of combining predictive analytics with data mining techniques:

1. Recognize market trends

For 63% of businesses polled by HubSpot, the most pressing issue is generating traffic and leads. This is primarily due to these companies’ failure to analyze and identify market trends. If they do, they use predictive analytics tools haphazardly because it is the latest tech buzzword.

On the other hand, using predictive analytics without qualitative data can result in figures that aren’t as accurate as you might think. Data mining assists you in gathering information from various online and offline sources and correlating it with datasets you already have.

This results in an accurate database from which insights can be derived. Observing social media conversations, purchasing patterns, and other data types can help you identify market trends you were unaware of.

Read more: Why should you outsource data cleansing?

2. Produce precise sales forecasts

Aberdeen Group recently published research indicating that accurate sales forecasts increase the likelihood of revenue growth by 10% year over year. As a result, most businesses use predictive analytics tools to forecast sales.

However, businesses often overlook that sales forecasts are influenced by factors other than complex numbers, such as consumer mood, behavioral patterns, and culture. Data mining methods can accurately understand a given market without relying on raw numbers.

Qualitative research conducted by actual data professionals will always help you understand a market better than mere numbers. As a result, combining data mining techniques with predictive analytics will allow you to gain more accurate insights.

3. Improved KPI management

One of the primary reasons businesses invest in predictive analytics tools is to manage their key performance indicators better. However, tracking KPIs using predictive analytics is futile if the numbers they rely on do not reflect reality.

Inaccurate KPI tracking is caused primarily by incorrect data readings. Data mining assists businesses by verifying the accuracy of datasets and compiling qualitative data that cannot always be automated.

As a result, you will notice that you can better manage your key performance indicators.

4. Develop new commercial opportunities

‍Modern predictive analytics tools have a major flaw in that, while they help make accurate forecasts, they frequently miss what is right under one’s nose because there are far too many variables to consider. Relying too heavily on statistics while ignoring qualitative data and observations can lead to missed opportunities.

Data mining can assist you in putting those predictive analytics forecasts and insights into the context and reading them correctly. This allows you to discover business opportunities that you were previously unaware of. In other words, it will enable you to identify and create new business opportunities.

5. Combine data mining and predictive analytics.

Predictive analytics is an important technology that will continue influencing traditional and modern data mining methods. Using predictive analytics in conjunction with data mining can assist you in identifying market trends based on social conversations and other forms of non-traditional data.

Data mining can also assist you in making accurate sales forecasts based on previous sales patterns and figures. Most importantly, it improves the quality of your insights, allowing you to manage your key performance indicators better.

Finally, without the assistance of a data mining team, you can create new business opportunities based on findings that might otherwise fly under your radar.