Business Intelligence Solutions for the Improvement of Argos Sales Department - Coursework Example

BUSINESS INTELLIGENCE SOLUTIONS FOR THE IMPROVEMENT OF ARGOS SALES DEPARTMENT By Location Business Intelligence Solutions for the Improvement of Argos Sales Department Executive Summary The complexity of the business environment and the exponential growth of technology over time necessitates the adoption of systems that keep a business safe from the grip of its competitors. Knowledge entitles power to an organisation and it is necessary that an organisation employs or risks the techniques available in the world of business. In the current business environment, businesses have failed due to the late acquisition of crucial information and this has resulted in poor strategies and policies as well as uninformed decision making. Accordingly, a holistic approach to business functions which will be represented in terms of the development of information technology infrastructure is requisite for production and resource management. Managers have got to have intelligence to respond to a world in is in constant flux. Therefore, organisations use business intelligence in order to make the best decisions so as to gain competitive advantages, understand the customer’s prejudices, bias and preferences, and to efficiently lead business processes. The case analysed in this report concerns Argos, a retail chain based in the United Kingdom. In its sector, customers and customer behaviour are golden information to the company and the competitors; therefore, the report analyses the possibility and feasibility of Argos adopting a Sales Business Intelligence System. The system serves to detail, customer behaviour, including buying patterns and the types and brands of goods most preferred by them. With these data, the system visualises it so as to give the management a template for effective decision making and formulation of strategy (Muntean&Mircea 2007, p. 43). The advantages of such a system are discussed and its feasibility detailed. Furthermore, its architecture is also described and the method of implementation touched upon. It will be discovered through the course of the report that Argos is best suited to survive and to leverage on the information obtained from the system to manage the competitive environment in which it operates. Introduction This report serves to provide a roadmap for the effective implementation of business intelligence systems for the improvement of Argos’marketing and Sales departments. Business intelligence refers to the set of computer based techniques and tools used to transform raw sets of data into meaningful or otherwise useful information. These techniques and tools can transform large sets of the raw unstructured data into useful information that assists to help determine and identify strategic techniques for the creation of new strategic business techniquesVitt, E, (Luckevich&Misner 2002, p. 172). Therefore, business intelligence techniques and tools serve to spot, dig out and analyse data. Ranging from operations to strategic decisions, business intelligence systems can be used to support business decisions that fall in these categories. These techniques and tools are most effective when they combine data from the market the company operates and data internal to the business (Olszak&Ziemba 2007, p. 139). These, in fact, creates what we call an ‘intelligence’ in relation to interpreting data from a single source; for example, using the data internal to the business alone. Common functions of business intelligence technologies include online analytical processing, reporting, analytics, data mining, business performance management, text mining, benchmarking and predictive analysis. A business intelligence system is also a decision support system (DSS). Competitive intelligence is occasionally used as a synonym for business intelligence; however, a subtle difference exists: competitive intelligence uses techniques that concentrate on data relating to the competitors of the company (i.e. External factors) wireless business intelligence refers to techniques, tools and applications relating to factors within the business; that is, internal structured data (Ranjan 2009, p. 66). Business intelligence applications are used for data gathered from a data store (Dataware house). As suggested by Thomas Davenport, its subdivided into querying, reporting, OLAP and analytical tools. Further, business intelligence has a subset ocalled business analytics that contains the organisation’s statistics and prediction. Business intelligence on the aforementioned departments should be applied to the following business purposes: 1) Measurement: A program that creates a hierarchy of performance metrics and benchmarks which informs the business managers on the progress towards achieving business goals 2) Analytics: A program that builds quantitative processes for the achievement of optimal decisions 3) Reporting/Enterprise Reporting: A program that builds infrastructure for strategic reporting to serve the strategic management of a business. This does not refer to operations reporting. It involves data visualization, executive information system, OLAP. 4) Collaboration (platform): A program that gets different areas to work together through data haring and Electronic Data Exchange 5) Knowledge Management: A program that makes the company data driven through strategies that enable insight and experiences that are true business knowledge. The design, development and application of BI systems are guided by both common and uncommon business practices, depending on the type of business, the area of application of the system and the function the system is to serve when adopted. Such business practices and a major practice determining the adoption of the system is the decision making processes. Regarding this, there are a number of theories relating to decision making (Heaton 2012, p 123). These include causal theory which adopts the principle that the consequence of your decision directly relates to the choice made, the evidential decision theory which states that the best decision conditional on having chosen it is the one with the best outcome, game theory which is mathematically founded and takes into consideration the conflict and cooperation between rational decision makers and Bayesian theory which is a probability theory and an extension of logic, enabling reasoning with propositions that take either a true or false state (Baars& Kemper 2008, p. 142). The sales BI system, to be used by the sales department at Argos, is a classical case of the employment of Bayesian theory and game theory. As will be seen, data on the behaviour of customers is collected and analysed and the analysed data used in the prediction of future outcomes of events (in this case, sales). Argos: A Case in Point Argos, being a retail chain, needs ways to monitor not only its sales, but the behaviour of its customers, in effect identifying the most potential and loyal customers it has and equally ways to attract more loyal customers. These goals of management need to be arrived at by the analysis of data acquired from the behaviour and movement of goods from its stores (Chen, Chiang& Storey2012 p. 1173). This is why business intelligence systems specifically suited for the sales department are necessary. Business intelligence systems suited specifically for the sales departments can help improve its sales in the following ways: Identification of and focusing on higher profit customers It may be the case that the most loyal customers are not those that place the biggest orders, but those that come repeatedly to shop at the stores. BI (business intelligence) systems can rank customers based on recency, frequency and the value of goods purchased by these customers in these rounds of visits. An accurate profile of the most profitable customers will eventually help the company focus its marketing energies on the most reliable customers (Gangadharan & Swami 2004, p. 142). Furthermore; such an effort helps in the search for customers with similar attributes, in turn adding them to the fold. Increased accuracy of sales forecasts Information regarding future sales involves the questions: what is going to be sold, to whom it is going to be sold to and when it is going to be sold. When these are accurate and timely, they have immense business value. Industry studies have shown that an improvement in the forecast accuracy produces downstream improvements such as higher profit margins and perfected order fill rates (Negash 2004, p. 54). Assess the effect of your marketing programs Such BI software systems can also help one to assess the effect of the company’s promotional activities to find out what promotional activities your customers may be reacting best too. In this regard, marketing budgets can be shuffled and allocated to the most productive promotional activities. That mentioned; therefore, it is imperative for the company to enhance its sales and cut unnecessary spending whenever appropriate with the aim of improving the business hold and achieving its goals henceforth (Mircea, Ghilic-Micu&Stoica 2011, p. 44). For a company such as Argos, the effect of the implementation of the sales BI system can easily be detected by analysing and assessing the performance measures that are determinants to the evaluation of the organisation’s progress. These performance measures include: 1) Money, normally measured as profit. 2) Input and output, productivity 3) Customer emphasis; for example; on quality 4) Adapting to change 5) Human resources Since these measures of performance are at many times kept on record, it becomes easy for comparison of data and determining the effect of the BI system on the organisation. Furthermore, these measures should be used as a template to decide whether or not to adopt the system. Profit earned (as a performance measure) should be the priority here, for the major purpose of companies in business is to make money (Rodenberg 2007, p. 117). Therefore, BI systems should give a good return on investment to warrant their adoption by the company. Sales Business Intelligence Systems The main objective of the sales BI is to monitor the volume and amount of goods going out of that is to mean, to determine who are the biggest customers of the business and in turn help advise the management of the company on how to tweak the policies and strategies in order to achieve the set targets. Therefore, for this objective to be met, a number of factors directly or indirectly affected need to be delineated. Since the object in question is the sales department, customers of the business, the volume of goods sold, returns on the goods sold and the frequency of purchase by the customers delineated needs to be postulated and input as raw data into the system, from which analysis will be made leading to information useful in the formation of policies, strategies and making of decisions (Zeng et al. 2006, p. 4724). This therefore requires the architecture of the adopted BI system to have certain features, which will be crucial in the attainment of its goals and eventually the goals of the company. The architecture of the system should organize the data, information management and technological components used to build the system for reporting and data analytics. The data components of the system include the sources of data that the management needs access to so as to analyse. These sets of raw data, for the case of the sales BI system designed for Argos, have already been mentioned before (Azvine et al. 2006, p. 29). However, it should be mentioned that both structured and unstructured data may be required for the BI architecture, as well as information from both external and internal sources. Information management, architectural components transform raw data into a set of consistent and coherent set of information suitable for BI use. This part of BI architecture includes data integration, data cleansing and the creation of data dimensions and business rules that conform to the guidelines of the architecture. The presentation of information to the management of the business should be done using the technological components. This includes the BI software tools to be used within an organisation, including the supporting IT infrastructure which is: the database software, the hardware components and the networking components. Various types of BI applications can be built into the architecture (Miller, Brutigam&Gerlach 2006, p. 201). Since the information has to be presented data visualization tools are important. Reporting, ad hoc query and online analytical processing software, business intelligence dashboards and performance scorecards may as well serve important functions. The skill set required to implement the architecture is a proficiency in IT and familiarity with data representation and analysis techniques. To implement the system, a database of customer type, number of times visited, type of goods sold and also the time or season and the volume sold over that period is created. If it is only possible or in the cases where it is only possible to perform online transaction processing, data extracted from databases through ETL processes are loaded into a data warehouse. This includes all the information about the customer and the type of goods bought as well as the times (Marjanovic 2007, p. 215). In order to build the system, a SQL Server Data generator can be used to generate the data and SQL Server Data tools to implement a data warehouse. The user interface can be designed by Visual Studio. Presentation and visualization of the data are done through the development of an appropriate interface using the appropriate tools. The Oracle Business Intelligence Enterprise Edition (OBIEE) offers a good platform for the interactive analysis of data with the advantage of comprehensive BI tools which inform and inspire better decisions, and capability to allow full collaboration within the organisation. For Argos, this would be an ideal system since being a retailer the various departments need to collaborate and share data and information in time to formulate strategic policies. Bibliography Azvine, B, Cui, Z, Nauck, DD &Majeed, B 2006, “Real time business intelligence for the adaptive enterprise. In E-Commerce Technology, 2006” The 8th IEEE International Conference on and Enterprise Computing, E-Commerce, and E-Services, The 3rd IEEE International Conference. 29-29 IEEE. Baars, H & Kemper, HG 2008, “Management support with structured and unstructured data—an integrated business intelligence framework” Information Systems Management, 25(2), 132-148. 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