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Data mining

  • /
  • Unit 5 - Operations Mgt/
  • 5.9 Management Information Systems (HL)/
  • Data mining

Data mining (AO3)

Data mining is the management process of using data for predictive analysis and forecasting purposes. It is the use of management information systems to find trends, patterns, and correlations from large data sets, and using the findings to make predictions about future situations. Hence, it is sometimes referred to as knowledge discovery in data (KDD).

The term comes from the mining industry in which mining for gold (or other precious minerals and resources) involves digging through earth and rock for the valuable parts. In the same way, data mining involves sorting through large data sets in order to find usable and valuable information to improve business decision making. This is done by extracting and processing raw data from large data sets, data mining allows managers and decision makers to discover patterns, relationships (correlations), and trends.

Data mining is used for many business functions, including:

Data mining relies on other aspects of management information systems (MIS), such as databases, data analytics, big data, and machine learning to discover patterns and relationships (correlations) in these large data sets so as to inform business decision making. Data mining enables managers to make sense of past trends in order to make informed predictions of the future, rather than relying on management decisions and corporate strategies to be based on intuition and guesswork.

 Advantages of data mining

The main benefits of data mining techniques can be outlined as follows:

  • They help manager and decision makers to predict future situations.

  • Effective use of data allows businesses to understand their customers better, which helps to improve customer relations.

  • Being able to make more informed decisions enable businesses to increase sales revenue.

  • Improved risk management as data mining can be used to detect fraudulent activities and unusual financial transactions. It helps firms to identify potential risks and enhance security measures to protect their assets.

  • Data mining techniques cut wastage and inefficiencies in operations management, thereby helping businesses to reduce costs, e.g., it enables firms to improve sales forecasting and optimize stock (inventory) levels.

  • Overall, data mining methods enable businesses to reduce risks and exposure to fraudulent behaviour.

 Disadvantages of data mining

The main drawbacks of data mining techniques can be outlined as follows:

  • Privacy issues are a growing concern due to the increasing amount of data about private individuals on platforms, such as social networks, e-commerce, online forums, and smartphone apps.

  • Security issues surrounding hackers gaining access to big data of customers, including data on personal and financial information, credit card fraud, and identity theft.

  • Personal data can be collected and misused, including the unethical sale of private information to third parties. The information can be used unethically to take advantage of vulnerable people or to discriminate against a group of people.

  • Data mining is challenging and complex. Finding the right or required data is a time consuming and difficult task given the huge volume of data present, which are also generated continuously.

  • It can be highly expensive, including the need to invest in advanced data mining technologies and hiring specialist technicians. Staff training about the use of mined data may also be required, which further increases costs.

 Case Study - The Information Commissioner's Office (ICO)

The Information Commissioner's Office (ICO) is the the UK's independent governing body set up to uphold information rights to protect the public interest. Every organization that processes personal information is required to pay an annual fee to the ICO, unless they are exempt. Failure to do so will result in a fixed penalty fine. ‘Processing’ is a broad term which describes anything a business can do with personal information, including (but not limited to):

  • Collecting

  • Recording

  • Organizing

  • Storing

  • Using

  • Retrieving

  • Altering

  • Erasing

  • Disclosing

‘Personal information’ means any detail about a living individual that can be used on its own, or with other data, to identify them.

The ICO is primarily funded by organizations paying the data protection fee. This accounts for about 85% to 90% of the ICO’s annual budget. In 2020, the ICO collected around £46,560,000 (approximately $57.2 million) through the data protection fee. Other sources of funding come from the government and the ICO’s regulation of various other laws.

 To review your understanding of this topic, watch this short video from Eye on Tech about the importance of data mining. As you watch the video, take note of why data mining is so important for businesses in today's corporate world and the benefits that businesses can reap from data mining.

 ATL Activity (Research skills) - Data mining in schools

Investigate how your school uses data to inform decisions about teaching and learning. Consider how data mining helps schools to know their students better and to improve IB examination results.

You might find this YouTube video helpful as part of your investigations.

Be prepared to share your findings.

If your school has a data manager / senior member of staff in charge of data, perhaps you could invite them into the classroom to address the above questions to add context of data mining in the workplace.

Exam Practice Questions

 

(a)Define the term data mining.[2 marks]
(b)Outline why data mining is important to businesses.[2 marks]
(c)Explain two advantages of data mining for businesses.[4 marks]
(d)Explain two disadvantages of data mining for businesses.[4 marks]
 Teacher only box

Answers

(a)  Define the term data mining.  [2 marks]

Data mining is the management process of analyzing data from various sources to extract specific data for predictive analysis and forecasting purposes. It involves collecting raw data, and then extracting and refining data to determine patterns and insights from  from the refined data.

Award [1 mark] for a definition that shows some understanding.

Award [2 marks] for a definition that shows a clear and accurate understanding of data mining.

(b)  Outline why data mining is important to businesses.  [2 marks]

Data mining is an important part of a firm's management information systems (MIS). The process involves the extraction and analysis of large volumes of data in order to establish possible patterns, trends, and associations in the data. This provides valuable information for decision makers to make predictions and forecasts about future trends that may impact the organization. For example, data mining can help businesses to find new sources of revenue and opportunities for cost savings.

Award [1 mark] for a response that shows some understanding of the demands of the question.

Award [2 marks] for a response that shows a clear and accurate understanding of the importance of data mining to businesses.

(c)  Explain two advantages of data mining for businesses.  [4 marks]

Possible advantages could include an explanation of any two of the following points:

  • Improved decision-making - Data mining can help managers to make more informed decisions as they are able to extract valuable insights based on predictive analysis of the large amount of data.

  • Enhanced understanding of customers - Data mining helps businesses gain a deeper understanding of their customers by analyzing their behaviours, preferences, and purchase patterns.

  • Predictive analysis - Data mining enables firms to predict future outcomes based on historical data and patterns. This can be used to improve sales forecasting and optimize stock (inventory) levels.

  • Improved risk management - Data mining can be used to detect fraudulent activities, unusual financial transactions, and anomalies within the data.

Mark as a 2 + 2

For each advantage, award [1 mark] for a suitable benefit and a further [1 mark] for an appropriate explanation, up to the maximum of [4 marks].

(d)  Explain two advantages of data mining for businesses.  [4 marks]

Possible disadvantages could include an explanation of any two of the following points:

  • Privacy concerns and legal issues - Data mining involves the collection and analysis of large amounts of data, which raises concerns about data privacy and data protection.

  • Data security risks - Data mining requires secure storage and access of large data sets, which can make businesses vulnerable to data breaches and cybersecurity threats.

  • Data quality - Data mining is heavily dependent on the quality (reliability) of the underlying data collected and extracted. If the data used for analysis is inaccurate, incomplete, or outdated, it can lead to substandard insights and decisions.

  • Complexity and resource requirements - Implementing data mining processes can be complex and resource-intensive for businesses, e.g., skilled data analysts, appropriate hardware and software infrastructure, and ongoing maintenance and updates.

  • Cost and time implications - Similarly, implementing data mining processes can be costly and time-consuming for businesses, especially for smaller organizations. It requires investments in technology, training, and data management.

Mark as a 2 + 2

For each disadvantage, award [1 mark] for a suitable drawback and a further [1 mark] for an appropriate explanation, up to the maximum of [4 marks].

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