Introduction
Advantages of Data Mining Over Business Intelligence
There are several advantages data mining has over business intelligence. To understand that let us discuss what business intelligence and data mining are.
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What is Business Intelligence?
Business intelligence (BI) is a technology-driven process to analyze complex data and deliver actionable information to business leaders, corporate executives, managers and teams to make informed business decisions.
BI entails enterprise-level data analysis for organizations to determine their areas for operational improvement and external expansion. In addition, business intelligence incorporates data mining and data visualization for strategic business decisions.
What is Data Mining?
Data mining is commonly known as knowledge discovery in data (KDD) as it helps businesses discover valuable information from a large dataset. It searches for a pattern in datasets that provides valuable business intelligence.
Data mining falls under the business intelligence umbrella and is a form of BI.
It is widely used to collect relevant information and gain insights.
With the increased technology and wider data scope, data mining techniques have accelerated and served large companies to transform their raw data into useful knowledge. Thus, improving the organizational decision-making process through insightful information.
Today all big businesses use data mining as it is important for decision-makers to understand the data mining process and how it helps in key business decisions.
Here are a few advantages of data mining over business intelligence.
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Competitive Edge & Difference Between Data Mining Over Business Intelligence
BI encompasses a technology-driven process that transforms data into actionable information. Organizations have a huge flow of data coming from their customer end.
It allows data analysis to unleash trends, patterns and business insights. The purpose of business intelligence is to track data which encourages organizations for better data-driven decisions. Whereas, data mining is more geared toward exploring data and finding solutions to only one particular business issue. Therefore, making it more focused and giving in-depth analysis. Data mining has intelligence and built-in algorithms to detect patterns that are interpreted and presented to management via business intelligence. This is why it is considered part of BI.
Data mining is most suitable to identify a particular dataset. It focuses on one customer segment, competitor, business function and department. With this small dataset, businesses can get better and more detailed answers to how the business is performing and which key area requires the most focus.
Findings through data mining are unique whereas business intelligence BI are accurate and shows views of the company’s processes thus both of them help in decision-making purpose in the organization.
BI brings a large dataset processed on relational databases and represents results on dashboards, reports and cards with KPIs.
Data mining includes cleaning, combining, transforming and interpreting data and business intelligence includes the creation, aggregation, analysis and visualization of data.
Competitive Features of Data Mining and Business Intelligence
| Feature | Data Mining | Business Intelligence |
|---|---|---|
|
Purpose |
Explores and format datasets to find solutions and answers to key business challenges |
Interpret and present data information to all the stakeholders for informed and improved data-driven decisions. |
|
Mission |
Leverages out information of your company to provide insights on ROI, process improvement and resource optimization. |
Answers to specific queries for decision-making or planning process |
|
Volume |
Processes small datasets for focused analysis on business |
Processes relational databases to track enterprise-level metrics |
|
Competitive Edge |
Provides a better understanding of customers, oversees business operations and improves overall business customer acquisition processes |
Identifies risky behaviour and more cost-effective mitigation. Allows managers and leaders to look into details and eliminate any business risks by looking at a clear picture of business spending on various projects |
|
Result |
Brings a distinctive dataset in a usable data format for better understanding |
Brings results using dashboards graphs, charts, and reports for clearer data understanding |
|
Focus |
Identify new KPIs |
Demonstrate KPI progress |
The purpose of business intelligence is to support organization convert their data into useful information.
It supports and facilitates better business decisions. It also allows organizations to access critical information for different business functions including sales, marketing finance and operations.
Business intelligence helps to track key performance indicators and presents data to stakeholders encouraging them to make data-driven decisions. Whereas data mining is more focused on exploring data and finding solutions to a particular business issue. Data mining detects data patterns and presents them to the organizational management team via business intelligence.
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Overview of the Data Mining Process.
In current times where data is the key to sustaining growth, all businesses understand its importance and use the data mining process for business decisions.
Here’s an overview of the data mining process.
- Business Understanding
The first step for data mining is to understand the business, and its objectives and convert it into mining. Without a clear understanding, there is a high chance that one may be unable to design a great data mining algorithm.
Example:
A retail store may want to use data mining to understand what their customer buys most. This will help them in creating demand and managing revenue and stock of the preferred product.
- Data Understanding
Once you get an idea about what a business is looking for, it’s now time to gather data. There are many ways to obtain data including organizations, primary sources and secondary sources. Data mining involves getting familiar with data and gathering insights.
Example:
A supermarket may use reward programs in form of points to give customers benefits where customers can share their phone numbers when making a purchase. Thus giving data to supermarket owners.
- Data Preparation
It involves getting the data ready. This is an integral part of data mining where it takes computer-language data and converts it into a form that helps people understand, measure and quantify.
- Modelling
In this phase modelling helps to search for patterns in the data. It involves several techniques for the same set of data and involves multiple trials and errors.
- Evaluation
Once modelling is complete, it is carefully evaluated and reviewed to make sure that it meets organizational objectives and business needs. Once the evaluation is done, data mining will be made.
- Deployment
It is the most simple and complex part of data mining. The complexity depends on the output of the process.
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Overview of the Business Intelligence Process
Business intelligence is used by different companies in different ways. However, the process is similar throughout the companies and it includes
- Data from different sources: external data gathered by data engineers, and internal data from company.
- The data set is then created for data analysis using data analysis models.
- Data analysts run queries for datasets.
- This in turn results in data visualization through charts, graphs and visuals.
- It later helps decision-makers and leaders to make informed decisions.
How Data Mining and Business intelligence Work Together
Data mining is a part of business intelligence. In data mining, data is often mostly raw and unstructured, making it challenging to conclude. Data mining helps decodes complex datasets and later delivers a better insight for the business intelligence unit.
Data mining divides into smaller data sets. It allows businesses to identify a specific root cause of business trends and use business intelligence for insights.
Analysts use data mining to gather specific information in the format they need, and use business intelligence tools to determine and help leaders understand why the information is important.
Today, companies use data to understand questions like “how”, “why”, “what” of data mining and business intelligence. Data mining helps them get to those insights where leaders can invest for decision-making purposes.
Data Mining Techniques in Businesses & Institutions
- Sales
Companies collect a massive amount of data about their customers for insights. Their observations on consumer demographics, customers insights, and online user behaviour, help them with data, optimize sales, improve marketing campaigns, and enhance business segmentation. It also helps to come across cross-sell offers, and initiate customer loyalty programs that yield higher ROI on marketing efforts.
- Education
Educational institutions and academia use data mining to understand their students for diversity and inclusion matters. To launch courses according, and to use metrics to evaluate each student’s performance. They use data mining for student profiling, and universities and understand their patterns.
- Operational Optimization
Process mining provides leverage in data mining techniques which reduces costs across operational functions and enable organizations to run more efficiently. This practice helps identify and improve decision-making among corporate leaders.
- Determining Customer Groups
Data mining helps companies to get a response from customers through their marketing campaigns. It helps provide information during customer groups in form of surveys.
- Increases Brand Loyalty
Data mining helps improve a brand’s loyalty. It helps in marketing and business decisions and shares insights for leaders to focus on their brand.
- Support Decision Making
People use data mining techniques to get help in their decisions regarding finance, marketing and business operations. Today, technology helps in decision-making with information that shares the unknown and unexpected.
- Increase Company Revenue
Data mining involves some technology. One must collect information on goods sold online; this eventually reduces product costs and services, which is one of the data mining benefits.
- Predict Future Trends
The data mining systems help predict future trends and with the help of this advanced data technology, it can help people to adopt behavioural changes.
- Helps with Website Optimization
Data mining helps businesses to find all kinds of unseen element information and supports organizations in website optimization. Similarly, data mining also provides useful information on the technology.
Data mining help companies monitor trends and adapt to different market conditions for improved decision making at all levels of the organization. Companies today are interested in gaining insights into their consumer’s buying patterns, improving employee productivity or observing the specific challenge and indicators to identify hindrances or growth in one’s performance.
Conclusion
Partner with Ascend Analytics to get started with real-time insights on your latest data mining project. Ascend Analytics helps businesses like yours to go through your data in real-time to reveal any hidden patterns, trends and relationships between different pieces of content.
We use our data mining techniques to gain insights into your customers and user behaviour. We also help you analyze trends in consumer buying behaviour, their presence and pattern on social media and share user trends for e-commerce platforms.
Let us help you find the root causes of your problems and make the right use of data. There is an untapped market hidden in your business insights.
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