Business Intelligence - Essay Example

Business Intelligence Business Intelligence An effective company such as TESCO incorporates BI for intelligence provision pertaining to their environment in order to improve its organizational decision making and possibly respond to identified developments in a timelier manner, which is necessary and vital to its success so as to swiftly and efficiently remedy the current situation it is in. A research conducted by Bain & Company estimates that only 15% of companies have an organization that helps them have a good competitive advantage. The main factor which separates the winners from the losers is the ability to make the most important decisions in a flawless manner —and then make sure they materialize. The main thing here is not structure but rather the application of an integrated organizational system which is capable of aligning vital attributes necessary to leadership and accountability (Blumberg & Atre, 2003). In an economy where speed and adaptability can make or break a business enterprise much like TESCO, companies can only win and as a result, stay profitable through proper organization —and keep on making profits. As TESCO grew over the years, it has become more complex, thus as a result, their capacity for quick response and adaptability to change gets stretched thin. As a supermarket, TESCO thus had to deal with a global playing field, since customers are separating into smaller compact units. Data cascades into TESCO, at the risk of being vague regarding meaningful information. Executives in this supermarket chain may find themselves scrambling to react. It is the organization which holds the key (Power, 2010). As Bain & Company research indicates, of these few 15% companies, TESCO has to ensure its competitive advantage in the market in order to be in this group. What will help TESCO set itself apart and become among the top performers is solely based on the quality of their decision making. The key to revive TESCO is to restructure their whole organization so that they will be able to make the most important decisions well while executing them quickly and consistently. It has frequently been found again and again that TESCO’s main rivals like Sainsbury, which announced UK sales growth to be 1.9% for the second quarter, is a worthy competitor since it has proved to be a decision-driven organization, founded on the pillars of effective decision making and flawless execution. TESCO’s governing body should take it into more consideration that making good decisions simply means being clear about which decisions matter most. This requires getting the right people to focus on those decisions at the right moment in time (Rud, 2009). TESCO the decision driven organization Building a system In order to go back to the winning ways, TESCO will have come to excel at decision making and execution. The top performers in this supermarket industry understand that the organization chart, which indicates who reports to whom, constitutes only a small part of the equation. They should put their focus into building an integrated system which supports a decision-driven organization. TESCO should aim at outperforming their competitors such as Sainsbury’s on these five key dimensions: • Strong leadership which offers vivid vision and clear sense of direction, which should be based on the sources of value in the business. • Clear accountability pertaining to the most essential decisions which are used to reinforce its organization’s structure • Talented personnel strategically placed and vested with the jobs where they can have the largest impact on TESCO, while strictly focused on the measures that matter the most. • Outstanding frontline execution. This can only be best enabled by having the right tools and working practices. • A performance culture which encourages the TESCO workforce at all levels in order to get things done while striving for excellence. In order for TESCO to be in the high performer’s league as an organization, it should comprehend how the various attributes of a highly effective organization reinforce one another. Cohesive leadership and clear vision greatly aid in crisp decision making and vice versa; thus, once the performance culture is in play, it will keep the frontline employees intent on high-quality execution, thus feeding the resultant expectations and shared objectives, which will reinforce this culture. TESCO is capable of achieving this kind of performance. Currently, what is known to hold them back is complacency, which is part of the big problem. Many companies especially in this arena (supermarkets) are willing to compromise and settle for “good enough,” thus being in the comfort zone. It is plausible to bear in mind that raising the performance of TESCO as an entire organization may seem overwhelming, and as a company, they may simply don’t know where to have their primary focus on. It is a very costly mistake if TESCO attempts to emulate their competitors, since an effective organization is an enormous asset that’s quite difficult to copy (Watson & Wixom, 2007). Based on BI technologies on TESCO’s prevailing and known views regarding business operations and most of all its historical background, there seem to be some discrepancy since this company has a rich history with solid foundation. What was also incorporated in assessing TESCO was a data warehouse. This is one of the most valuable things for any Business Intelligence System. An effective use of a data warehouse goes a long way in helping to make decision-making to be in a much more intelligent manner, which significantly improves the operations of Business Intelligence Systems. There was also a provision of a collection of data, which was integrated for on- line analytical processing (OLAP). A data warehouse is known to be highly reliable due to its nature as “a subject-oriented, incorporated, stable, and time- variant compilation of data in support of management’s decisions” (Watson & Wixom, 2007). Since TESCO is a supermarket company, there is a lot of emphasis on accurate data analysis pertaining to marketing of goods. OLAP is known to be able to meet all the needs of the supermarket, and its benefits include trend analysis, customer loyalty analysis and carry out market analysis. These are the kind of benefits with a great impact for the future development of TESCO chain of supermarkets. So far, I am not convinced that such comprehensive and elaborate systems of BI were erroneous in TESCO’s situation thus, I have devised a proposed solution. This solution involves the OLAP system (Watson & Wixom, 2007). Decision making with DSS - Decision Support Systems In an economy of constant flux, well informed and highly reasoned decision-making is the pillar of success in business. As a manager of such a company like TESCO, one has to make decisions that affect the business on a daily basis. Some may be critical while others may not be very crucial. Decision Support Systems create room for quicker decision making, recognition of negative trends and improved provision of business resources all to the benefit of TESCO. What are Decision Support Systems Decision Support Systems – also referred to as DSS - is a particular class comprised of computer-based information systems that are used to facilitate decision-making activities. Decision Support Systems scrutinizes business data and give interactive information, which acts as props to business executives and professionals during their decision-making process. This ranges from problem recognition to decision implementation. The following are used by Decision Support Systems: (1) models for analysis, (2) databases which are specialized, (3) own insights and judgments of the decision maker, and (4) a computer-based modeling process which is interactive, used in helping semi-structured business decisions (Watson & Wixom, 2007). Give Support to Business Decision making In this highly competitive business environment, time to ponder over decisions for long is considered a luxury. Today as a manager, especially of such a large business enterprise as TESCO, one is required to be coming up with more decisions than before in a short period of time. This is the reason why TESCO requires managers who are capable of taking up snap decisions and flawlessly execute an outlined strategy. Since the whole is a decision-making process, recognizing the problem to action has become highly accelerated, and relying on human response has become impossible. Thus, this is the reason why companies especially of the nature of TESCO, require a Decision Support System that will assist in the reaction and swiftly adapt to the constantly shifting business environments that the managers must relate to. In the prevailing competitive business environment, what is required is the need for maximum performance so as to achieve competitive advantage. The same goes with effectiveness of data capture technologies and application to BI systems, which was a success in assessing TESCO’s situation. A company deficient of these forms of competitive advantages will not be at a position to conduct its activities, thus eventually cease to exist. Incorporation of Decision Support Systems assists in making better decisions in the viewpoint that, as soon as they are functioning, they have such outcome as reducing expenses, more efficient utilization of assets, increasing revenue output, risk reduction, improvement in service delivery to consumers and so on. However, Decision Support Systems can give TESCO many other benefits which include: Prompting up decision making process Increasing organizational control Hastening problem solving within the organization Assisting in the automation of managerial processes Personally improving ones efficiency Elimination of value chain activities Proposed Solution As was previously mentioned, OLAP System is a technology which enables users to perform complex data analyses with the aid of a fast and interactive access to various viewpoints of the information contained in data warehouses. These different viewpoints also referred to as multidimensionality are an essential characteristic of OLAP systems. What is meant by multidimensional is looking at the data in three or more dimensions. In our case study which is TESCO supermarkets, for a database of a sales department, these dimensions can be product, time, store and the age of the customer. This mode of analyzing data in multiple dimensions is for the most part effective in ascertaining relationships within TESCO, which can’t be directly deduced from the data itself (Watson & Wixom, 2007). OLAP System Technology basics So as to give you (the reader) a feeling of how OLAP should be viewed, let us analyze the following case scenario: Consider a retailer with many shops who deals with Daily necessities in various cities with numerous varying styles and types of product, like furniture, food and office supplies. Each of the shop delivers data on a daily basis regarding the quantities sold in customs behavior. All of these data are then stored in a central location. Now, it is called dimensions if the business analyst intends to follow sales by month, outlet and behavior. This dimension can be on basis of a certain viable time frame like monthly. Now, if one wants to analyze the data of these three dimensions mentioning something worthwhile about them, what is actually being done in such a case is analyzing (Watson & Wixom, 2007). Components and their functionality Depiction of subject classification pertaining goods and trade of corporate goods; description of the categorization of business-to-customer and the customer contract management is contained in customer’s topic. The theme elaborates the retailer of enterprise sale of goods and sales of regional conditions. Goods as the main theme is among these three themes and its precise contents include: Product These are intrinsic information of goods which includes aspects such as their merchandise code and merchandise name and their merchandise type among others. Inventory information pertaining to the products includes the merchandise code, their treasury numbers, its inventory, date and many others. Marketing information pertaining to the commodity includes the merchandise code, a customer code, recorded date of sale, its sales price and prevailing sales volume. Customer This includes the intrinsic information pertaining to the customer such as customer number, name, address and other available contact information. The contract information pertaining to a customer include customer number, the contract code, date started and date ended, quantity purchased and price. The purchase information regarding the customer includes the customer number, the code of the product, its unit price, the quantity, its date and many more. Vendor This includes a vendor’s inherent information such like a vendor ID, the merchandise being traded, promotion trade names and the address of the vendor among others. Meet strategic objective for TESCO supermarkets This will incorporate the reduction of inventory costs – throughout the data warehouse system will be numerous kinds of goods, sales data and inventory data put together via data analysis and knowledge classification. One is able to know the stock over a specified period of time, orders for goods not received, and if it received lesser orders for goods and inventory exchange of goods. To decision-makers, it can ascertain a corresponding variance in the goods so as to ensure that there is proper inventory, thus hasting up cash flow and as a result reduce inventory costs (Power, 2010). Carry out market analysis – I would also advice TESCO on the incorporation of OLAP data analysis tools in order to scrutinize data received from a data warehouse so as to examine consumer purchasing habits, composition of the product and other strategic information. One should also structure the largest-selling product analysis, then ensure that it’s at the correct time, correct place at the stipulated inventory. For trend analysis, integration of a data warehouse should be done for various products and catalog trends analysis in order to select the products needs to be supplemented, so as to analyze customer buying trends, study seasonal buying patterns, spot bargains and its responding numbers. To be able to accurately forecast seasonal sales, the system will have to regain the data warehouse on estimate of about 1 million products in basically over one year of sales data in addition to this basis for analysis (Power, 2010). For commodities subgroups, the layout, purchase of the analysis, recommendation and merchandise-which would be excavated from the consignment record important information, can be established to purchase a certain kind of product, since customers may opt to buy other commodities. A typical example of such information is analysis pertaining to the mining of market basket. Customers through research into their shopping baskets in relationship to the variable commodities, study customer buying habits. The use of Apriori algorithm which can be found in products that are frequently bought by customers simultaneously is critical (Power, 2010). Essential also is the analysis of the effectiveness of promotional activities which are carried out in supermarkets such as TESCO, often through advertising, coupons and discounts. Customers enjoy a variety of ways and engage in promotional activities so as to promote product sales, the main purpose being attracting of customers. It’s important to only completely figure out the customer, so as to be able to identify promotional activities, improve customer response rates and reduce the expenses resulting from activities in promotion. With promotion via the multi-dimensional scrutiny, TESCO can carry out a comparison on promotional activities with the situation before and after, and the number of business deals during the sales and correlation analysis which can be reclaimed after a mention of which commodities may be purchased along with the marketing of goods. The use of data mining techniques can also study certain promotional activities in order to accurately attain the marketing objective that deals with avoidance of unnecessary waste of company resources. All together, data mining can also be used in the past, which involves promotional data to search for future investment return of the largest users (Power, 2010). To carry out customer loyalty analysis-TESCO Supermarkets are often involving the handle membership cards throughout the process. It is very important to ascertain members through the information to the customer data mining, which can record a customers buying series, customer loyalty and purchasing trends. These can be assessed in a systematic way. By the same method, customers who buy goods at different times of the day can be categorized as a sequence. A sequential pattern mining can be used to analyze TESCO’s customers consumption or changes in customer allegiance whereby, they outline the price and merchandise to be attuned and modernized so as to retain existing customers and attract new customers, thus increasing TESCO’s revenue. A good advantage of such kind of success is Hypercity, which is a popular retail store owned by the K Raheja Group, which is known to offer wide range products and the retail store has greatly been helped by BI implementation especially of OLAP. The store has a large range of goods which also mean that there was generation of vast amounts of data, thus the most important factor being the need to analyze it (Power, 2010). Conclusion Data warehouse and multi-dimensional analysis that has complete data pertaining to capacity can be a quick and accurate mode for evaluating data in order to assist managers especially of big enterprises such as TESCO supermarkets to make better business decisions, thus bringing a competitive advantage. TESCO’s revenue decline is valid and the best way forward is to embrace the new systems and technologies, especially on matters of data. The existing data mining technology and data warehouse in domestic functions is not sufficiently extensive, but TESCO tends to have complex business structures. There is a big number of invoicing business data and a specific need for proper decision analysis. The data warehouse technology in these business enterprise applications has broad prospects (Power, 2010). References Blumberg, R., & Atre, S., 2003. The Problem with Unstructured Data. DM Review, pp. 42–46. Power, D. J., 2010. A Brief History of Decision Support Systems. Available from http://dssresources.com/history/dsshistory.html (Accessed January 19, 2013). Rud, O., 2009. Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy. Hoboken, N.J: Wiley & Sons. Watson, H. J., & Wixom, B. H., 2007. The Current State of Business Intelligence. Computer, 40 (9), p. 96. Read More