IELTS® Listening Practice 56

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Listening Script Vocabulary

(Section 4: You will hear a talk on the topic of data mining. First, you will have some time to look at questions 31 to 40 [20 seconds]. Listen carefully and answer questions 31 to 40.)


Thanks for attending today's lecture, everyone. Today, as you know, we are going to have an introduction to 'data mining' – what it involves, how it is carried out, etc. Then we'll have a brief look at 4 examples that show how data mining is put to use in various fields. 

Let's start with a definition. What is data mining? Data mining is the process of analyzing hidden patterns of data. The data is collected according to different perspectives and then it is categorised in various ways into useful information. This data is collected and assembled in common areas, such as data warehouses for efficient analysis. What is all this data used for? That is a good question. Well, the data is typically used to facilitate business decision-making, and ultimately to cut costs and increase revenue. Data mining is also sometimes known as data discovery and knowledge discovery. 

So, what does data mining involve? There are 4 major steps involved in the data mining process. First, extracting the data and loading it into a data warehouse. Secondly, storing and managing the data in a database. Third, providing data access to business analysts using application software. And finally, presenting the analysed data in easily understandable forms, such as graphs.

The first step in data mining is gathering relevant data that is important for businesses. Company data may be related to day-to-day operations like sales, inventory and costs etc. Or it might be data that allows analysts to forecast or predict future trends. What is important, is the patterns and relationships in the data which provide valuable information that businesses can use to increase profit. Organisations with a strong consumer focus use data mining techniques to get a clear picture of the number of products sold, prices, who the competition is, and of course, customer demographics – age, gender, spending power etc. 

Let's look at a couple of examples of how data mining is used in reality. For instance, the retail giant Wal-Mart, like all major supermarkets, sends all its relevant information to a data warehouse. This data can easily be accessed by suppliers enabling them to identify customer buying patterns. They can see patterns of shopping habits, the preferred days for customers to visit the store, the most popular products and so on.

Data mining also holds great potential to improve healthcare systems. Healthcare providers analyse data to identify best practices - the most effective actions to improve care and reduce costs. Data mining can also be used to predict the volume of patients visiting particular departments of a hospital, allowing managers to make better staffing decisions. Processes are developed that make sure that the patients receive appropriate care at the right place and at the right time. Data mining can also help healthcare insurers to detect fraud and abuse.

My next example is market basket analysis. Market basket analysis is a modelling technique based upon a theory that if you buy a certain group of items, you are more likely to buy another group of items. This technique may allow the retailer to understand the purchase behaviour of a buyer. This information may help the retailer to know the buyer's needs and change the store's layout accordingly. The data can be used to compare the results of different stores to find the more effective way to organise products, for example. It can also be used to compare the purchasing habits of customers of different ages and genders to help managers buy appropriate stock depending on the local population. 

Finally, there is a new emerging field, called Educational Data Mining, or EMD, which is concerned with using data from educational environments. The goals of EDM are identified as predicting students' future learning behaviour, studying the effects of educational support, and advancing scientific knowledge about learning. Data mining can be used by an institution to make accurate decisions on what to teach and how to teach it most effectively. The learning pattern of the students can be captured and used to develop suitable techniques to teach them.

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