3 Data Mining Techniques for Modern Business Analysts


Data is revolutionizing the business world. It has always been a best practice to keep accurate company records; however, the current data boom is allowing organizations to gain deeper insights into the discrete causes driving consumer behavior.

It’s no longer sustainable for businesses to avoid incorporating data into their operations. Doing so is essentially equivalent to admitting defeat, as the companies that do the best job using data to their advantage will almost always have better results than those that do nothing.

As a business owner, you should understand that data mining tools are essential. Here are three data mining techniques for modern business analysts.


Clustering Analysis

Clustering is one of the most common data mining tools utilized by businesses. While clustering itself doesn’t necessarily provide answers to problems, it’s one of the most important steps in the data mining process.

Essentially, clustering does exactly what you would expect based on its name. It puts sets of data into groups. One cluster of data will often be much different than another cluster.

The idea is to separate the data points as thoroughly as possible, which will help provide more in-depth and useful insights later down the line.

How Can You Use Clustering Analysis?

Marketing is one of the most applicable uses for clustering analysis. When you’re trying to sell a product, it’s important that you know your audience.

Otherwise, your organization will spend too much money on advertising per conversion. Keeping long-term marketing costs low in relation to sales is one of the most crucial functions for clustering analysis.


Search-Driven Analytics

Search-driven analytics is one of the newest data mining tools to hit the business world, and it’s sure to completely change how companies deal with data. search-driven analytics is a blessing to all business owners who are tired of having to wait weeks for the results of data requests.

That’s because of search-driven analytics functions unlike any other forms of data mining tools or analysis out there. It’s basically a search engine for structured data. In other words, you can enter terms into a search bar, as one might with Google, and instantly receive accurate graphs and outputs based on the input relation.

For example, a user could enter “Number of sales in the eastern region to seniors,” and immediately get the answer.


How Can You Utilize Search-driven analytics?

There are a couple ways search-driven analytics will completely turn your data mining strategy on its head. The first of the benefits should be fairly obvious by now: You will be able to get insights much faster than with traditional methods. This will have a compounding effect—allowing businesses that effectively use this tool to rapidly blow away their competition.

Another benefit of search-driven analytics is that it opens up data to a broader portion of your staff—or anyone with sufficient permissions.

At first, this might seem like a negative, as you don’t want sensitive data to be leaked to the public or competitors. But think of it this way: Your employees know their jobs and departments better than anyone. Doesn’t it make sense to give them some autonomy when it comes to revealing insights related to their work?

There are likely many great innovations just waiting to be made by employees with a great idea, but no easy resource for qualifying their hunch without going through the long process of sending a request through the analytics team.


Predictive Analysis

Unlike the previous two examples, which deal with past and present data collection, predictive analysis is a method for identifying future trends.

There are a lot of ways to perform predictive analysis, but many of the most effective techniques today combine collected data with machine learning and AI in order to produce forward-looking models.

While this can be labor-intensive, predictive analysis helps businesses make smarter decisions by mapping out potential outcomes. There are practically limitless business applications for predictive analysis. The organizations that do the best job utilizing predictive analysis will be the best-prepared for the future.

There’s little room for error in today’s business world, where the competition is fiercer than ever due to the development of powerful data mining tools. It’s essential your organization starts utilizing these techniques in order to stay ahead of industry trends.

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