In today’s modern society, almost everything around us is dictated by big data analytics. Consumers have gotten accustomed to and expect personalised marketing efforts that showcase new features and goods catered to their unique interests daily. Hence, businesses must continually analyse their customers’ shifting behaviour and preferences to attract attention to their marketing messages and products.
The key to accurately analysing and predicting these behaviours and preferences is data mining and business intelligence. These terms are commonly used interchangeably, but the meaning is quite different.
Read on as we dive into the key differences between data mining and business intelligence.

What is data mining?

Data mining is a branch under the field of data science that seeks to uncover nuggets of wisdom by searching through large datasets. It helps identify data patterns that could offer valuable business intelligence and many other useful applications.
Data mining can be done through several methods:

• Classification partitions massive datasets into specific categories. This method shines in marketing, allowing companies to push different ads across different domains. As a result, they can ensure that ads are seen by target customers likely to respond most favourably.
• Clustering takes classification to the next level since it works to detect minute similarities or anomalies that humans cannot observe. It can identify ways to improve operational efficiency, targeted marketing, and product innovation.
• Association focuses on specific variables and exposes the relationships between them over time. By tracking and analysing consumer activity, companies can then predict future behaviour.

What is business intelligence?

With the high influx of customer data, including previous purchases, social media interactions, and search engine queries, businesses now have a better idea of what their consumers could buy next. However, before that becomes possible, this data must first be converted into something useful. This is where business intelligence (BI) comes in.
Business intelligence is a set of techniques and applications that transforms data into concrete and actionable information. It can pinpoint areas for operational improvements and external expansion through enterprise-level data analysis. Furthermore, BI can leverage data visualisation to facilitate business decisions further.
Besides internal data analysis, businesses can also apply BI on third-party databases to gather insights on their competitors or potential business partners. All in all, BI is used to increase a company’s cost savings and better serve and target its audience.

Key differences

Data mining and BI are different in several aspects, namely in their purpose, volume, results, and focus.
1. Purpose
Business intelligence serves to transform raw data into useful information. It tracks KPIs and presents data to promote data-driven and informed decisions. On the other hand, data mining explores datasets and identifies solutions for specific business issues. Using computational intelligence and algorithms, it can detect patterns that are then interpreted and submitted to executives via business intelligence.
2. Volume
Data mining works optimally when used to process smaller datasets focused on a specific customer segment, department, or competitor(s) and discover hidden answers to certain business questions. In contrast to data mining’s specificity, BI processes relational or dimensional databases to deduce the overall performance of an enterprise.
3. Results
Given that data mining is more geared towards converting raw data into something usable and addressing specific business problems, its results will be unique datasets. Conversely, BI results are shown in digestible formats like graphs, charts, reports, and dashboards.
4. Focus
Data mining studies patterns that assist companies in developing new KPIs used for business intelligence. BI, in turn, focuses on showing the progress of the KPIs defined by data mining. Broad metrics such as total customer support tickets, total revenue, and ARR over time paint the complete picture of the company’s performance, providing stakeholders with the confidence to choose correctly when faced with significant decisions.

Conclusion

For a company to reap the maximum potential of data mining and business intelligence, both must be used in tandem. Through data mining, organisations can understand the “what,” which is then used by business intelligence to answer the “why” and “how.” By investing in data mining and BI, companies will have the tools to quickly perform, test, and interpret complex analyses that streamline processes and increase financial yield. As such, on top of upskilling employees in aspects of cybersecurity training, it would be beneficial for companies to consider looking into data mining and BI courses.
If you’re keen on adding data mining or business intelligence into your growing skillset, BridgingMinds can help facilitate your learning journey. We offer a wide range of professional IT security courses in Singapore, complete with related topics, such as the CompTIA Data+ course that teaches how to analyse and interpret data, communicate insights, etc. Sign up now and reserve your spot before classes start on 27th June 2022. In addition, we will be hosting a full-day Data Analytic Learning Fiesta/Workshop event this 27th May 2022, from 9 am to 5.30 pm. Register here to sign for up the event!