Data Mining and Data Warehousing Importance to ASDA

Introduction

Day after day, in the pursuit of improving on its market strategies, mega retail companies like ASDA find it necessary to adopt certain plans to get a hold of newer and bigger customer bases, to increase sales. Building such systems as data warehousing systems will assist the company further in collecting a great deal of data that will be used later in accounting for most of what the company is going through. Similarly, data mining will go a long way in ensuring the analysis of such figures that depict the trends in the market, customer loyalty, and also profits and losses that the company goes through (Introduction to Data Mining, n.d. p.1).

Asda is no exception. The need to go through some of these processes goes beyond strategic planning; it is a necessity in such a company like this one. The amount of customer base that already exists also heightens the need to do data mining and warehousing. The following essay explains further how ASDA uses its data warehousing and mining strength to strengthen its market power and to improve its general performance as a mega retail store.

Importance of Data Mining and Data Warehousing to Asda

Primarily, the store must get a hold of the retail link that it has with its customers. The store has kept vast amounts of data for the last ten years in comparison with trends in weather, climate, changing times like holidays and school periods, to come up with vital information that is required in making necessary market moves. The net result of serious analysis of these trends, acquired from data warehouses in Asda is that optimum product supply will be realized.

The store can therefore know what type of product to acquire in a specific amount and a specific period. Availability of some products in a particular time over a given season will give the store optimum profit margins and will cause the store to be more and more productive. Merchandising is therefore controlled.

Secondly, the collected data gives the store an advantage since it minimizes inventory costs. The simple fact that the statistics that they acquire from analyzing data are at their hands, it is easy for the storekeepers to ask the particular vendors to provide a certain amount of their products to the store at what particular amount (Beccary, 2006, p.1).

The data gives the trends of purchase versus selling prices and amount and vendors are always kept on high alert concerning these trends. The store, therefore, keeps its stocks up-to-date, while ensuring that its products are available for the customers whenever they need them. Point of sale numbers and figures will therefore work a great deal in ensuring customer satisfaction. In advancement of this idea, this company has brought the suppliers into the picture to share in the plans of the store. Both parties have come together to analyze the numbers from the data banks and have created ways in which newer products that are related to the ones on the shelves can be introduced to the customer (Benefits for Retailers, n.d. p.1).

Collaborative plans such as these are an advantage to all parties involved, from the customer, who gets a chance to be spoilt of choice, to the storekeeper, whose profits tend to increase, and the supplier, who gets to diversify what he has to offer. Data mining, therefore, assists Asda and its suppliers in forecasting the possibility of introducing these related products on the shelves without the possibility of huge amounts of losses due to negative responses from the consumer. In effect, the relationship between the supplier and the buyer greatly increases (Khosrowpour, 2006, p.110).

In addition, data mining has had a rippling effect on the globalization of stores like Asda. Since the expansion of the store overseas, the data has been used to study the trends that the customers in overseas countries are showing. Suppliers have therefore been put on the edge as to which product will best suit the people. In effect, quality overseas has greatly improved. By analyzing some of the data from the stores, Asda has been able to pick up the correct suppliers for each type of commodity in its stores and thus singling out particular suppliers who have achieved optimum quality at the lowest amount possible. In this way, the retail store maintains trust in certain suppliers while satisfying vast amounts of customers.

Retail links and data mining have facilitated the improvement of logistics (Weber, 2008, p.1). Generally, it has been discovered that logistics are the key to supply and demand management. Without data, on one hand, no logistics about supply can be managed. In this simple reasoning, the store has invested heavily in ensuring that relevant forms of data are collected and are put within the confines of a data warehouse.

Outside the retail and purchase scope, another way that data mining assists the store is through its use in assessing the risk of theft and in its ability to determine the cost the store should incur in having security around its stores’ vicinity. (ADT Security Services, 2010, p.1). This was confirmed without doubt by the chief head of security Bowen who in an interview said openly that the data that the bank collects goes a long way in studying the trends of petty theft in the stores that the company runs.

It was noted earlier that the stores had expanded from merely being retail stores selling groceries and other small commodities, to a store handling clothing, living room items, and other larger commodities. The risk assessment showed that the same amount of expansion needed to be done in the security measures. Such information as the Supplier Cap Index could be analyzed to generate statistics that involve crime. These analyses help the company to budget on security requirements and introduce policies within the company that will be useful in enhancing security measures in the company (Friedlos, 2006, p.1).

In addition, customers who use the “Collect-and- go” system for their purchases have the advantage of going through purchases made over the last four months by use of special palm tops given by the stores. Seth explains that since 1999, people have been assisted in purchasing the right kinds of products in the Asda stores because of this system. The bar code inscription in the products that are stored and can be retrieved from the database of the stores can be read through these palmtops and a good data mining analysis can be done to confirm the authenticity (Seth, 2001, p.264).

Conclusion

With no doubt in mind, there is a necessity for a store as big as Asda to invest in data warehousing and data mining. The ability of a store to over-perform its competitors majorly lies on such measures as the ones this company is undertaking, it is necessary to understand the intricacies and importance of getting the right kind of data to be used in the future. The one risk observed for not doing this kind of work is the fact that it cannot be done later. Real-time data entry is needed for accuracy and consistency. Without this, some of the most common trends as the effect of weather on sales or the effect of the holiday season on sales will not be achieved (Exforsys Inc. 2010, p.1).

The result of something like this is devastating. Logistics concerning future restocking will be inaccurate and profit trends will go amiss. Losses due to unfamiliarity to consumer desires will be felt and eventually, the company experiences huge losses. Customer trust is gained through such analysis from data warehouses through the enhancement of quality as seen earlier.

If that is not all, data mining goes all the way to building necessary relationships with store suppliers. In the end, such relations get to benefit both the supplier and the store owner. Statistics are generally the best functional way to predict the future, to learn from the past, and to know what to do in the present. For retail stores, such statistics that are stored in data warehouses are necessary for the survival of the store.

Recommendations

Now that it has been vividly seen that the importance of data collection and analysis is important, some of the issues to be recommended will include the following. Real-time updates should be implemented. This means that systems should be created for such measures as data collection to be effected as purchases take place. These include getting hold of the item bought, selling and purchase price, date of purchase, manufacturer details, made of payment, and the like. Surveillance is also part of data collection. It usually assists immensely in security measures as seen above.

In the same line, data should be provided for security to curb thefts that are continually rampant in these stores. By updating security feeds and having comparisons with the databases of the store, shoplifting will be immensely reduced. This takes a lot of investment on the part of management, but more often than not, it will tend to pay off.

Finally, the store will need to do a little more than simple data collection. A lot more needs to be done in analysis. Computerizing the systems that are involved in data analysis is one idea. Much has been in ensuring this takes place, but the feeling is that more needs to be done for such indices as the Supplier Cap index, which has just been computerized of late.

Reference List

1. ADT Security Services. 2010. ADT Retail Solutions give you the edge to succeed.
2. Beccary. 2006. Advantages of Data Mining. Advantages and disadvantages of Data Mining. Web.
3. Benefits for Retailers. n.d. The Global Language of Business.
4. Exforsys Inc. 2010. Data Mining – The Importance of Interaction in Data Mining. Web.
5. Friedlos, Dave. 2006. Asda Tools Spot Theft Trends. Web.
6. Introduction to Data Mining, n.d. Discovering Hidden Values in your Data Warehouse.
7. Khosrowpour, Mehdi. 2006. Supermarket industry in the UK. Cases on electronic commerce technologies and applications. Chocolate Avenue, Hershey: Idea Group Inc. p.110.
8. Seth, Andrew. 2001. What are the Retailers Offering? The grocers: the rise and rise of the supermarket chains. London: Kogan Page Publishers. p.264.
9. Weber. R. 2008. Dynamic data mining for improved Forecasting in Logistics and Supply Chain Management.