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How to Pair Data Mining & Business Intelligence in '24?

How to Pair Data Mining & Business Intelligence in '24?How to Pair Data Mining & Business Intelligence in '24?

Companies reported that they spent $187 billion on big data analytics in 2019. Big data analytics which is also called data mining is critical for business intelligence since businesses are relying on increasing levels of data. Data mining helps businesses prepare data for BI by identifying anomalies, root causes and predicting events.

What is business intelligence?

Business intelligence (BI) is a collection of processes, technologies, applications, and skills which businesses use to produce informed and data-driven business decisions

Companies apply BI to analyze and visualize

  • internal data so that they can optimize their business strategies which may include enhancing customer experience, saving costs etc.
  • external data sources to obtain insights about competitors or potential partners.

Data repositories for BI applications include data warehouses (centralized or decentralized), production databases and operational data stores.

Process mining is an alternative approach to analyzing organizational process data. Process mining delivers insights about performance issues, the root causes, and automation opportunities.

How does data mining help business intelligence?

Data mining is a collection of techniques for generating insights from data. These techniques can be used to generate insights from business data, contributing to business intelligence (BI).

Some of the ways data mining techniques are used in BI include:

Data preparation

Data mining techniques can be used to clean and prepare the data for analysis. 80% of the data is unstructured when first collected, so it requires cleaning and wrangling before being delivered to the business intelligence team to derive insights from it. For example, data mining algorithms can convert images or documents into machine readable data for analysis by business intelligence tools.

Data mining enables businesses to identify the root cause of a specific issue/trend, predict outcomes, identify anomalies. These inputs help business intelligence teams identify actions to take to improve the business.

What are the benefits of data mining in business intelligence?

In the domain of business intelligence (BI), data mining has the applications listed below. These applications each have specific benefits. Please note that this is a high level list, more granular applications of data mining also exist in BI

  • Business analysis: Organizations’ data includes information about internal structure of the company and lines of business (e.g: sales, logistics, manufacturing). Leveraging data mining on operations data reveals information about processes to improve. Understanding the data and applying strategies to improve processes can improve the company’s efficiency (reducing costs) and increase its effectiveness (improving the quality of its products & services).
  • Customer analysis: Customer data exhibit preferences, thoughts, needs, demands, and intents of target prospects and customers. Applying data mining on customer data:
    • provides insights about customer purchase trends and seasonal needs in order make predictions on decisions, actions and product launches.
    • helps company to prioritize initiatives to respond to customer needs and demands.
  • Market analysis: Constant collection of real-time data about the market and industry gives businesses data to be used in data mining/data science to make predictions about the market, competitors, and customers, and enables companies to discover new business opportunities.

What are the challenges of data mining in business intelligence?

Data mining in business intelligence faces the challenges of both data mining and business intelligence separately, which may include:

Lack of BI Strategy

Data mining can be time intensive. Having clear goals for BI in terms of KPIs helps BI team focus on the right initiatives and make use of data mining technique either themselves or with support from the data science/data mining team. However, especially newly formed BI teams may not have clear goals and may try to find insights without focusing on high priority issues. Therefore, it is paramount to have a BI strategy before starting detailed analyses.

Communication issues between data science/data mining and BI teams

Before data mining, the business intelligence team should specify what kind of data & insights they need. Yet, some BI teams are unclear on what to measure and can not provide detailed specs to the team in charge of data mining. As a result, the analysis process can become wasteful.

Data Preparation

Data preparation is essential to provide an accurate and consistent data to BI team. It’s been estimated that data scientists spend 80% of their time cleaning and preparing datasets. Yet, 57% of them complain about preparation step because of its repetitive and time-consuming nature.

Data privacy and security

Collecting and using private information on clients or details of company is a privacy-sensitive issue. There are increasing numbers of cyberattacks threatening confidential data. To protect clients’ data, businesses need to apply data privacy regulations and standards, and they can also leverage data privacy enhancing technologies to improve the overall privacy of business data.

Which industries benefit the most from data mining in business intelligence?

In a 2021 survey of French insurance professionals, ~63% of respondents said that they use data mining to enhance the the value of collected data in order to improve their relationship with their customers. Some of the top industries and business functions that benefit from data mining in business intelligence include:


Mobile phone and utility companies use data mining and business intelligence to predict why a customer would change to a different provider. They examine billing information, customer services interactions, website visits, and relevant metrics to provide probability scores, offers, and incentives to customers likely to leave the company.


Retailers apply data mining in business intelligence to categorize customers into groups and offer promotions accordingly. For instance, they may offer loyalty cards, or upsell and cross-sell offers to those who spend little but often. Meanwhile, the customers with one big purchase may receive engagement campaigns to encourage them to come back.


Many e-commerce companies employ data mining and business intelligence together to offer cross-sells and up-sells through their websites. A well-known example is Amazon who leads customers by “people who viewed that product, also liked this” functionality.


Supermarket loyalty card programs are usually offered to gather comprehensive data about customers. This data is then used to suggest cross-sell & upsell opportunities.

To explore more on data, you can check our comprehensive software and tools lists.

If you believe your business can benefit from data mining in business intelligence, let us help you to find the right vendor:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Hazal Şimşek
Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.

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