Data Mining and the Emergence of Predictive Analytics

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TransForm Solutions

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

As databases grow larger and start being available on public networks, the opportunities to derive accurate insights grow. In addition, predictive analytics tools help you to accurately make future forecasts and predictions based on different kinds of data.

To compile relevant data from online and offline sources, businesses often need the assistance of a data mining agency. Data mining agencies pull relevant datasets and information from all sorts of sources, both online and offline. Such data can be used to derive insights by using predictive analytics.

How predictive analytics is influencing data mining?

Most people define data mining as the process of exploring large amounts of datasets to identify consistent patterns and symbiotic relationships.

However, it also refers to the process of manual and automated mining of data from various sources. This could involve converting CRM data into excel sheets or to certain formats or a simple task such as mining information from various online resources and compiling them in a Word document.

It can also refer to complex processes of using advanced statistical tools to identify patterns within Big Data to make predictions that matter. Such techniques usually make use of predictive analytics tools. The emergence of predictive analytics has greatly helped companies to derive insights and make accurate predictions.

Here are some of the benefits of using predictive analytics alongside data mining techniques:

1. Identify market trends

Generating traffic and leads is the most important problem for 63% of businesses interviewed by HubSpot. This is mostly because, these businesses aren't analyzing and identifying market trends. If they do, they blindly use predictive analytics tools because it is the latest tech buzzword.

However, using predictive analytics without having qualitative data can result in figures that aren't as accurate as you may assume them to be. Data mining helps you compile information from various online and offline resources, and corroborate them with datasets that you already have.

This creates an accurate database which can be used to derive insights from. Observing social media conversations, buying patterns and other forms of data can help you identify market trends that you didn't know existed.

2. Make accurate sales forecasts

Aberdeen Group recently published research which shows that accurate sales forecasts result in a 10% increase in the likelihood of revenue increase year-over-year. Consequently, most businesses use predictive analytics tools to make sales forecasts.

However, businesses forget that sales forecasts do not depend only on hard numbers but on a number of extraneous factors such as consumer mood, behavioral patterns and culture. You can use data mining methods to accurately understand a given market without depending on sheer figures.

Mere numbers will never help you understand a market like qualitative research conducted by actual data mining professionals. Thus, pairing data mining techniques with predictive analytics will help you derive more accurate insights.

3. Better management of KPIs

One of the main reasons why businesses invest in predictive analytics tools is because they want to manage their key performance indicators in a better way. However, if the numbers they rely on do not represent the reality on ground, tracking KPIs using predictive analytics is a futile effort.

Inaccurate tracking of KPIs is mostly because of incorrect readings of data. Data mining helps businesses to fill the gap by verifying the accuracy of datasets and compiling qualitative data that sometimes cannot be automated.

Consequently, you will see that you are able to manage your key performance indicators in a better fashion.

4. Create new business opportunities

A major failing of modern predictive analytics tools is, though they help make accurate forecasts, they often miss what is right under one's noses because there are far too many variables to consider. In fact, relying too much on statistics and ignoring qualitative data and observations can make you ignore opportunities that lurk right around the corner.

Data mining can help you put those predictive analytics' forecasts and insight into the perspective and read them in the right context. This helps you to uncover business opportunities that existed all along, but you somehow missed.

In other words, using data mining alongside predictive analytics will help you to not only identify new business opportunities, but also create them.

5. Use data mining alongside predictive analytics

Certainly, predictive analytics is an important technology that will continue to influence both traditional and modern methods of data mining. Implementing predictive analytics alongside data mining can help you to identify market trends based on social conversations and other forms of non-traditional data.

Data mining can also help you make accurate sales forecasted on processing previous sales patterns and figures. Most importantly, you will be able to manage your key performance indicators better as data mining adds a certain level of qualitative touch to your insights.

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

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