How to Apply Data Mining in Business Analytics in '24

Data mining is the process of finding patterns in order to predict outcomes in big data. Companies use data mining techniques to turn raw data into beneficial information to increase revenues, reduce costs, enhance customer relationships, and decrease risks. Today, 90% of businesses consider big data and analytics as a key to their organization’s digital transformation.

What is business analytics (BA)?

Business analytics analyzes historical data using quantitative methods (e.g. regression analysis, linear programming, data mining) and technologies to gain new insight and improve strategic decision-making.

Business analysis has three primary methods :

• Descriptive: The data is interpreted to identify trends and patterns. 
• Predictive: The data is analyzed by applying statistics to anticipate future outcomes.
• Prescriptive: Testing and other techniques are employed to determine the best result-providing outcomes.  

Business analytics and business intelligence are used interchangeably since they both serve similar purposes. However, they are different in terms of their focus.

• Business analytics determines the likelihood of future outcomes through prescriptive analytics, data mining, modeling, and machine learning in order to predict what would happen in the future if the trend continues or changes.
• In comparison, business intelligence tries to describe the situation by analyzing past and current data. It examines what happened and is happening at the moment and what needs to change.

How is data mining used in business analytics?

Data mining complements business analytics by:  

Defining the problem

Data mining processes start with a clearly defined business problem. For instance, increase sales or get more return customers can be business problems to study.

Selecting features/variables

For instance, businesses collect data based on the customer and what they have purchased from the company to examine returning customers and create customer profiles. Age, location, and income would be helpful variables to include in the selected dataset for curation.

Collecting and curating data

Once the question and dataset are determined, data engineers create the data pipeline to collect the data or put existing data in the desired format. Depending on the problem, the dataset is curated to give insight about this specific business problem.

Analyzing the data

Data scientists investigate the data to remove outliers or anomalies and analyze it to determine patterns in order to help solve the business problem.

Make business decisions and changes

Once the results are out, the BA team can makes data-driven decisions to change or optimize a certain business strategy or operation.

Track changes

Based on the decision, the data collection and analysis processes continue to understand if the decisions work as expected. 

Adjust and repeat

If the results are as expected, BA teams can generalize the changes to optimize similar processes or strategies. If the results are not desirable, BA teams can do further testing to understand why changes did not work and adjust the strategies.

Why is data mining important in business analytics?

In 2010, big data becomes more widespread and data mining remains relevant field to increase the impact of big data. During the following years, data mining experience relatively small decline since data diversity and data processing algorithms have increased and data mining has become a subset of data science. The same developments led business analytics to merge with data mining and be empowered.

Some of the benefits of applying data mining for business analytics include:

• Customer benefits:
  • Understanding the customer landscape
  • Increasing marketing effectiveness
  • Enhancing customer experience
• Operational benefits:
  • Improving Operational Efficiency
  • Making data-driven decisions
  • Retaining employees
• Business benefits:
  • Examining the competition
  • Expanding sales and increasing Revenue

What are the challenges that face data mining in business analytics?

Data Security and maintenance

The cost of a data breach was estimated to rise from ~$3.86M to ~$4.24M in 2020. To avoid penalties and protect reputation, companies secure data by complying with the legislation. Yet, storage for big data is limited to keep information secure from hackers as well. 

Lack of strategy

BA teams are expected to be clear at which aspect of the data they will be looking for forecast. However, it is usually not the case for many companies. When there is no strategy over the analysis process, data mining team might deliver a dataset that lacks relevant variables and creates strong biases in the predictions.

Business analytics case study

Uber

Uber is an American mobility service provider. In 2018, the company upgraded Customer Obsession Ticket Assistant (COTA), a tool to help agents improve their speed and accuracy. The company used the A/B testing method to compare the outcomes of two different choices. They estimated that updated product leads to faster service, more accurate resolution recommendations, and higher customer satisfaction scores. In the end, the company improved its ticket resolution process and increased its savings.

To discover data solutions, check our comprehensive software and tools lists and compare analytics tools in our data-driven list.

If you believe your business can benefit from data mining and business analytics, we can help you to find the right vendor: