UCL’s Dean reiterates the importance of DATA SCIENCE
Dr. Dhananjaya Kulkarni , the Dean of Academic Affairs at Universal College Lanka presents his insight into how Big Data Analysis and Data Science in general enable interesting ways of conducting market and consumer-behaviour analysis, which are considered as key areas of importance for strategic business growth today.
BIG DATA AND DATA SCIENCE
Large volumes of data, commonly known as ‘big data’ are generated and stored by businesses though point-of-sale systems, online-shopping websites, or other transaction systems. Have you ever wondered why the friendly cashier at your favourite grocery store always asks for your Loyalty or Reward Card? Yes, you got it right – data is being collected about who you are, what you buy, when you buy, your age group etc. But why? Well, this big data is now considered a very rich resource of information, especially for the strategic and commercial growth of a company.
Data mining for example, could be done by grocery stores (or any business for that matter) today for understanding buying patterns and catering to their customer needs in a more efficient manner. In fact, the survival of companies now depends on to what extent businesses are able to analyse their own data and then develop strategic plans for growth or profitability.
Amazon for example recommends you relevant books based on what you are currently buying, in turn based on what other readers like you generally like to read! Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from such structured and unstructured data.
Dr. Dananjaya
THE BIG NEED TO ANALYSE BIG VOLUMES OF DATA
The centralised systems used in retail stores today record and store sales, staff, inventory and account details at each site, but these IT systems are transactional in nature, hence are not capable of analysing associations between frequently purchased items. Such associations, if available, would be useful in marketing promotions, prediction of item sets that would be purchased together, placement of items, and understand buying behaviour from different dimensions. Availability of such trends or patterns enables new business opportunities, helps understand customer-satisfaction, and ultimately improves profitability.
TYPES OF BUYING PATTERNS THAT CAN BE DISCOVERED
When dealing with big data, Market Basket (or Affinity) Analysis is aimed at discovering connections between specific data which lay hidden inside the data stores.
Various types of buying patterns could be discovered to identify customer preferences, multi-item buying trends, buying patterns as per age, region, time of year etc. However, conducting this task manually may be impossible because of the thousands of items typically sold at a store per day. Hence association mining algorithms, as part of software may be utilized to automate this process.
These algorithms are the core for Data Mining tools now and have made their way in popular platforms such as Hadoop, as well as custom-built software to enable data analysis.
BECOMING IMPORTANCE-AWARE, DISCOVERING THE MOST INSIGHTFUL PATTERNS
Although such data mining systems have been proposed previously, they have limited ability to provide a user-friendly mechanism to configure, generate and view the associations among items.
It is crucial to extract more meaningful associations during analysis and filter out the ones that provide little insight. Importance-aware systems are a novel idea – they perform the market basket analysis using association mining algorithms to identify hidden affinities between products, by keeping some notion of ‘importance’ built into the system. Importance can differ depending on the market, such as price, expiry date, local/international product, health benefits, target age group etc.
UCL Faculty Team led by Dr. Dhananjay has worked on such data analysis tools and has incorporated Data Science concepts into the curriculum of the BSc (Hons) Software Engineering degree programme delivered at UCL in partnership with the University of Central Lancashire.
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