Data mining is one of a few buzzwords that are tossed around a lot, to the extent that it looks something new. However, it’s not a new invention. The concept was there even a century ago. The first instances were seen in Alan Turing’s idea of a universal computing machine.

However, we are living in the digital age where data mining and machine learning are helping businesses in all sorts of ways. A few of them include sales processes, interpreting financials, and investment. It’s a big reason why more data scientists are needed around the globe.

Data mining involves the analysis of huge data sets to discover insights that help companies solve problems, mitigate risks, and seize new opportunities. Think about mining a mountain for ore. Then, consider that you have to search for valuable information in a large database. In both these cases, we require sifting through tremendous amounts of material to find hidden value. That’s why we have the name data mining.

So, this field can help you with questions that traditionally were too time-consuming to resolve manually. You use a  range of statistical techniques to  identify patterns, trends, and relationships. Eventually, outcomes that will affect the business can be predicted.

Sales, marketing, and product development are a few of many departments it can help with. It can put you on top of your competitors by teaching more about customers, developing effective marketing strategies, increasing revenue, and decreasing costs.

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Key Data Mining Concepts

A merger of an illustrated brain and a real woman’s face data-mining

Experts use multiple tools and techniques to get the best results from data mining. For example, a few commonly-referred terms are:

Data cleansing and preparation — The process of converting data into a format suitable for further analysis.

Artificial intelligence (AI) —  The process of analyzing activities associated with human intelligence. It’s one of the most sought-after fields today.

Association rule learning — Or market basket analysis is about determining relationships among variables in a dataset.

Clustering — The process of dividing a dataset into a set of meaningful subclasses.

Data analytics — The process of evaluating digital information into useful business intelligence. and

Machine learning — The process of teaching computers to“learn” without being explicitly programmed.

Advantages of Data Mining

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Being a data-driven business is not just optional anymore. You’ll have to discover insights from big data and incorporate them into business decisions and processes. This thing alone can play a huge role in your success.

Data mining does what every leader tries to do in business: By looking at the past and present, it gives you the tools and skills to make accurate predictions about the future.

For example, you can understand what leads can get you more profit, and which ones are most likely to respond to a specific offer. It does that by using a simple input, such as past customer profiles. This is a surefire way to increase your return on investment and client base.

So, it empowers you to solve problems related to data, including:

  • Increasing revenue.
  • Understanding customer segments and preferences.
  • Acquiring new customers.
  • Improving cross-selling and up-selling.
  • Retaining customers and increasing loyalty.
  • Increasing ROI from marketing campaigns.
  • Detecting fraud.
  • Identifying credit risks. And
  • Monitoring operational performance.

It certainly offers great business intelligence. So, you just have to use the right techniques. You might also have to work with professionals who can guide you through every step of the way.

Technologies such as machine learning and artificial intelligence let companies dig through huge sums of data in minutes or hours, rather than days or weeks.

How Data Mining Works

Usually, the steps in a data mining project revolve around include asking the right business question, collecting the right data to answer it, and preparing the data for analysis. If one has found success in the earlier phases, success in the later phases will come by easily.

Poor data translates to poor results, so data miners must focus on the quality of the input data. The following six steps typically achieve timely, reliable results:

Business understanding — The data miner must have a clear understanding of the business, its current situation, project parameters, and the primary business objective of the project.

Firstly, Data understanding — What data is needed to solve the problem and then gathering it from all available sources.

Secondly, Data preparation — Formatting the data to answer the business question.

Thirdly, Modeling — Using algorithms to identify patterns within the data. And

Evaluation — How to achieve the best results? And what algorithm can help do that?

Deployment — Finally, making the results available to decision-makers.

Collaboration between domain experts and data miners is also vital. It helps in understanding the results of the project.

The Future of Data Mining

A boy presenting a growing statistic on a whiteboard Data Mining

If you have any doubts about the future of this field, don’t worry: It’s bright. We’ll only increase data in the coming years. So as we’ll see more data, there will be an evolution in the techniques used in this field.

A while back, only companies like NASA could work with huge sets of data. Now, with the advancements in technology, companies are doing all sorts of interesting things with machine learning, artificial intelligence, and deep learning with cloud-based data lakes.

Things like the Internet of Things and wearable technology will bring more data that can yield unlimited insights about people and organizations.

In a century of data, are our governments working enough on data privacy and other related laws? Let me know what you think in the comments below!

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