Select Page
Business Analytics Nanodegree Program - data mining for business analytics

Data Mining for Business Analytics Explained

Data mining is the process of combing through mountains of data to find patterns and insights. When it comes to business, making decisions based on data increases the effectiveness of running your company and a greater return on investment (ROI).

“Businesses that utilize data mining are able to have a competitive advantage, better understanding of their customers, good oversight of business operations, improved customer acquisition, and new business opportunities,” according to WGU.

That all sounds great, but how does it actually work in the real world?

Understanding Data Mining for Business Analytics

There are seven steps for data mining to be used effectively by businesses.

1. Define the problem

Data mining, especially when used for business analytics, is not just taking whatever data is available and looking for patterns. Instead, the process begins by clearly defining a business problem that you want to be solved. For instance, it could be finding ways to increase sales or get more return customers.

2. Select your dataset

When using data mining as a business strategy, it’s important to be selective about what data you collect. The best datasets are perfectly curated to give insight into the business problem. For instance, if you were looking at ways to get more return customers, you would want to collect data based on the customer and what they have bought from you in order to create customer profiles. Data like age, location, and income would all be useful.

3. Collect data

Once you’ve determined what data you collect, you’ll need to use a data engineer to create the data pipeline for actually collecting the data from customers and putting it in a usable format.

4. Analyze data

Once the data is collected, a data scientist will sift through the data to remove outliers and the like. Then, they will analyze the data and search for patterns that can help solve the business problem.

5. Make business decisions and changes based on outcomes

Once the results are in, it’s time to make concrete decisions based on the data. Since these choices are backed up by data, it’s easier to feel confident in the direction you choose.

6. Track changes

Believe it or not, the work is not over. Once you’ve made changes aligned with the results of the data, it’s important to keep collecting and analyzing data. Over time, it will tell you if the decisions you made are working.

7. Adjust and repeat

Check in regularly with your data and see if you see the results you want. If you do, keep doing what you’re doing and maybe make additional changes. If you don’t, hypothesize why the changes didn’t work and try again.

Areas to Use Data Mining for Business Analytics

The kind of problems to be solved using data mining for business will vary greatly depending on the type of business. Some common use cases include:

  • Understand the customer base
  • Analyze the competition
  • Increase effectiveness of marketing
  • Retain employees
  • Grow sales
  • Improve customer experience

The possibilities are endless and typically companies that use their data mining to creatively solve business problems are well rewarded.

Learn to Harness the Power of Data Mining for Business Analytics

If you think that data mining for business analytics could be a useful tool at your job, check out Udacity’s Business Analytics Nanodegree program. In as little as three months, you could be helping your company succeed by using business intelligence. Check it out!

Start Learning

Jennifer Shalamanov
Jennifer Shalamanov
Jennifer is a content writer at Udacity with over 10 years of content creation and marketing communications experience in the tech, e-commerce and online learning spaces. When she’s not working to inform, engage and inspire readers, she’s probably drinking too many lattes and scouring fashion blogs.