Data Warehousing, Business Intelligence Model, a Source System - Assignment Example

? Business Analytics Business 29 November Contents Questions 3 References 13 Questions Explain SMART. What are the underlying reasons for BA objectives to be SMART? For excellent business functioning one has to accurately determine relevant data especially during the formulation and evaluation stages of strategy. Assessment of strategy requires five target needs to be met. Some of these needs pertain to technically establishing frameworks whereas others deal with the possible course of action provided the framework deviates in a significant manner from the particular targets. These necessary requirements are as follows: a) Specific – the target must be specific including the number of customers that the organization has to collect annually or the forecasted revenue of a firm, the reduction in delivery time and so on. This target may be attained by carefully selecting relevant information from big data (Barton & Court, 2012). b) Measurable – the target must have the quality of measurement for it to be relevant. The figures must be tangible as it may be difficult to allocate costs and revenues to systems that need enhancement and accordingly measurable targets have to be established. One instance is if the firm is not sure about the number of customers it has, then it has to look for another particular target. c) Agreed – the targets have to be accepted by the organization. In other words, there is essential need for claiming ownership prior to strategy implementation. Without ownership, a strategy may be completely ignored or even counteracted. Also when the targets are accepted by a firm, some individual are entrusted with the accountability of meeting the targets. d) Realistic – it is very essential that the firm sets realistic and attainable targets. In most firms, targets may be formulated without trying to determine whether they are achievable or not. This may be as an outcome of the corporate culture in the firm, no possible outcomes for meeting specific targets within given deadlines or the possibility for individuals to extend deadline by buying time. e) Time bound – it is extremely necessary for firms to set attainable targets within a specific period of time and it is imperative for the organization to emphasize on meeting deadlines. Also it is very important for firms to understand at an early stage the targets which may not be achievable and accordingly make modifications. Putting the initial letters of the first five words helps one to get the acronym SMART. There are certain reasons for the goals to be SMART. This is especially true in the context of business analytics so that they may be accurately defined and operational. Many times, it becomes difficult to implement technical processes in case of ambiguous information and this further complicates calculation and collection of data thereby leading to failure of meeting with the proposed goals. It is important to develop measurable goals as they need to be technically quantified. At the same time, these goals must be attained in a given amount of time for the data system to deliver messages to end users especially when significant values are exceeded. Broadly speaking, the above five needs help to ensure clear understanding of business initiatives. Ambiguous objectives may have diverse interpretations leading to diverse authentic versions. For facilitating efficient reporting, goals need to meet specific deadlines (Laurson & Thorlund, 2010). 2. What are the key competencies required by analysts in the business intelligence model? Depending on the organization, the functions of a business analyst may slightly differ, however the core roles of a business analyst remains the same provided the firm wants to smoothly operate the business analytics function. A business analyst should have the required business competencies. The analyst should have thorough understanding of the supporting business process and the manner in which information may be delivered to add to value on a strategic level. Hence the analyst should have fundamental insight of the business with respect to the deliveries. It is also essential that the analyst autonomously optimizes knowledge in a manner for supporting the best possible decision. Another important skill is the consistent dialogue with the business and also the ability to create and detect synergies across diverse organizational functions. The analyst should have adequate knowledge about the method competencies. This means that other than delivering the data in a framework the analyst should review the data to ensure that relevant knowledge is garnered. The analyst is also responsible to ensure that the information users get the relevant knowledge. This means that one of the critical requirements is that a business analyst has a basic knowledge about the relevant use of certain tests to certain data in order to derive authentic and relevant conclusions from the test. This basic knowledge is supplied to the business analyst during the three to five days crash training course that leading suppliers of analytical software runs. Another issue with business analyst is that they are hesitant to work with new software applications. In this context, most business analysts consider themselves to be software programmers rather than analysts. Theory explains an excellent business analyst to possess the skills to understand the type of resources which may aid in appropriately validating and defining specifications and needs in a product scope in a particular project. The next and the final requirement of business analyst is that they should have thorough knowledge of the manner in which data may be processed and retrieved. This translates to the fact that an excellent business analyst should have the knowledge for structured analysis. Business analysis models are mainly employed to enhance and support text requirements, help in recognizing and validating needs, communicate and document needs and organize the data into coherent notions. Expert business analysts play the role of data scientist by churning relevant information from big data to put ideas into implementation (Davenport & Patil, 2012). Thus successful analysts should have three core competencies – data, method and business. Other than these, certain behavioural competencies like communication, relationship building, influencing, political awareness, team work, leadership, attention to detail, problem solving and critical thinking helps one to become an excellent business analyst. The above mentioned behavioural competencies further aid an analyst to understand a particular activity, discussed them with all concerned parties and deliver the work in a manner which makes a difference to business process thereby adding value to the service (Laurson & Thorlund, 2010). 3. Why is data warehousing important? In what manners can a data warehouse based business intelligence solution add value to users? Largest and complex business organizations employ data warehousing. Data warehousing is important for the below given reasons: a) It helps in evading data islands and manual processes in connection with the primary systems of the firm b) It helps in evading the overload of source systems with daily analysis and reporting c) It aids to integrate data from diverse source systems d) It aids to develop a historical data foundation which may be modified or erased from the source system e) Helps in aggregating the data needs and performance for business requirements f) Helps to add new business terms, logic and rules to information g) Establishes analysis environment and central reporting system h) Holds documentation of metadata centrally after collecting data i) Secures scalability so that augmented volumes of data may be handled in future j) Ensures valid and consistent data definitions across countries and business areas Thus, a well planned data warehouse helps the organization in the creation of well documented, authentic figures with history and qualitative data across the business areas and source systems having a scalable solution (Laurson & Thorlund, 2010). It is very important to have a data warehouse in place and populated with critical information. Organizations may add value to business intelligence by accurately using the information through data warehousing. A data warehouse facilitates in generating scheduled reports. Moving the reports created to a business intelligence solution augments accuracy and consistency and frequently helps to cut costs. Allowing end users to directly create reports in a business intelligence solution is easier. Data warehouses are a link between business intelligence mechanisms and source applications and provide an ideal chance to create metadata. Metadata of extreme significance may be stored and created in data warehouses. In order to meet with ongoing business requirements organizations should develop sound business intelligence systems. Business enterprises having diverse software processes use data warehousing to ease the financial consolidated outcomes in complex organizations thereby excelling in implementing sound business intelligence tools. Another situation in which data warehousing is used is to meet with reporting requirements in business. Another important tool under data warehousing is that of data mining. This software helps in sifting through large complicated data and garner hidden insights. This software works best provided a data warehouse is in place. Excellent data warehouse helps in storing the relevant data. This is ensured by excellent business intelligence systems which ensure that relevant regulations including those created by Sarbanes-Oxley may be stored. Such regulations trigger demand for transaction systems which may not be able to support such rules. This is where the need for sound data warehousing is essential for excellent business intelligence solutions. Data warehouse provides secure access to people requiring relevant data and exclude others who may not need a specific data. Hence they help in providing data security. The market is flooded with increased analytical software solutions and a data warehouse helps in storing them. Such packages not only provide already defined metrics and reports but also help in measuring performance. Data warehousing tools include dashboards which help supervisors to enter interactive display of updated significant management information. Complicated displays depicting real time information in high graphical and creative manner are known as dashboards. Data warehousing eliminates the requirements of business intelligence mechanisms to compete with processing of transactions leading end users to review data rapidly and easily generate reports. The mechanisms available with business intelligence system also help in enhancing the business analysis function. They aid to attend to data rather than stocks. Moreover managing this data requires data scientists as opposed to data analysts (Davenport, Barth & Bean, 2012). The above benefits are reasons as to why most firms prefer a business intelligence based data warehousing so as to reach a particular level of sophistication. Hence, for business enterprises to gain competitive edge, a data warehousing systems related with business intelligence solution is critical (Guerra & Andrews, 2011). 4. What is a source system? What are the different data generating source systems? Source systems are the sources of information on which the data warehouses function. Most firms have a host of data warehouses that are integrated in such a manner that data warehouses also perform the additional function of a source system. There are several data generating source systems. These are as follows: a) Billing systems – billing systems are used to print bills which are provided to customers. Such bills have the customer name mentioned on them and this facilitates value based and behaviour based segmentation while reviewing this information. b) Reminder systems – such systems help in sending reminders to clients for not settling bills on time. Analyses of this data help in treating clients depending on their payment records and develop credit scoring. c) Debt collection systems – these systems send status messages for cases transferred to outside debt collectors. The information with respect to customers with whom dealings should not be established further is garnered and accordingly removed from the customer relationship management campaigns. d) CRM systems – these systems have detailed histories about conversations and call records of consumers. This information may be reviewed for complaint conduct and providing better quality by firms. Information related to finding consumers who have terminated taking services or purchasing products from firms and the reason for them to display this conduct can be analysed from this system. e) Consumption and product data – this provides data about the services and products that have been sold out over a period of time. f) Customer data – this contains every detail about the consumer including consumer name, address, cancellations, entry time, segmentation, special contracts and so on. This information helps firms to garner market data. g) Business information – this data helps in collecting employees, accounting figures and industry codes. For firms operating in a business to business market, this represents customer data. h) Campaign history – this records information on campaigns which is significant in marketing history as it permits follow up on marketing initiative effectiveness. An aggregate campaign data helps in determining critical elements of particular campaign and the market development scenario. i) Web logs – this provides data about the user conduct on the website of the firm. It may help in predicting the number of visitors and navigating the website. j) Questionnaire analyses performed over time – for named information, the data provides CRM information. This helps in collecting and recording questionnaire surveys to determine relevant data. k) Human resource information about employees – this data helps in optimization of human resources. It helps in disclosing the number of leaves taken and checking on errant workers. It also helps in finding past information on employee history, competencies, salaries and so on. l) Production information – this data helps in maximizing production processes, procurement, control of stocks and so on. m) Accumulation of KPIs – they help in evaluating present processes and can be used to maximize processes as they reveal correlations between tasks and financial performance outcomes. n) Data mining results – these outcomes are in the form of loyalty segmentations or sales models provide history if placed in data warehouse. It helps in developing causal relations and learning across diverse campaigns (Laurson & Thorlund, 2010). o) Data on ERP systems – this data takes care of account management systems having critical information about the financial transactions of the firm for accounting formats. The data can be linked with KPI data especially in revealing correlations between initiatives and to determine whether outcomes matched expectations. However the creation of an ERP is time consuming and needs to be accurate for excellent decision making using BI tools (Wixom, Watson & Werner, 2011). 5. What is performance management? Explain supply chain management in business analytics. Performance management may be explained as process optimization. A process may be optimized by ensuring that resources are excellently employed for the process to continue and by enhancing the outcomes of the process. Emphasizing on process optimization does not necessary translate to ignoring strategy since the optimization need may stem from the strategy itself. Moreover, strategy always limits the scope of implementation. Hence, performance management in business analytics requires one to draw a balance between the resources deployed with the potential outcomes. At the same time, the organization also emphasizes on cost cutting without compromising on quality. In this case the importance of strategy function may be observed if it successfully attains a balance between the above two objectives. This is one of the main reasons as to while employing performance management individuals have a tendency to save the outcomes for future reviews. Also the firm should recognize activities which lead to excellent impacts with relation to costs. Also the management is interested to know whether the organization with the help of specific tasks is able to decrease the objective of cutting down on expenses and at the same time achieving excellent levels of quality of service. Performance management in business analytics also tries to analyse the time required for a particular impacted to be noticed. All the above mentioned tasks have to be associated with financial objectives in order to review the activities rendering the best revenue for the organization. Hence, performance management has the significant objective of systematically garnering experiences which are based on performed tasks by systematically reviewing and saving lag data. Hence, the user is able to get descriptive insight about the processes which further helps users to get a holistic view of the organization. Thus, performance management has everything to do with striking the correct balance. The balance concerned is minimizing resources for a process to smoothly function and at the same time ensure that the expectation of the user is met by following the process. Supply chain management refers to the manner in which the company associates with its suppliers. These associations may change from firm to firm and vendor to vendor. In supplier associations which are casual in nature, the price may be negotiated depending on the deal and the necessary data will provide the buying firm all details about the vendor. For large organizations, analytical supply chain management deals with collecting absolute data about its suppliers so that the firm has descriptive knowledge about the supplier market prices so that it is in a position to exploit a strategic position as a consumer. It is essential that the purchasing firm has adequate knowledge on procuring similar service from other suppliers. It not only helps in strengthening the bargaining position of the organization but also to determine whether the company is majorly purchasing services from an individual supplier. Hence, business analytics in supply chain management is highly essential for purchasing firms to keep track of suppliers and ensure that they are not exploited (Laurson & Thorlund, 2010). References Barton, D & Court, D. 2012. Making Advanced Analytics Work for You. Harvard Business Review, vol. 90, no. 10, pp. 78–83. Davenport, TH, Barth, P & Bean, R. 2012. How “Big Data” Is Different. (cover story). MIT Sloan Management Review, vol. 54, no. 1, pp. 43–46. Davenport, TH & Patil, DJ. 2012. Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, vol. 90, no. 10, pp. 70–76. Guerra, J & Andrews, D. 2011. Why you need a data warehouse? Available from [29 November 2013] Laurson, GHN & Thorlund, J. 2010. Business Analytics for Managers: Taking Business Intelligence beyond reporting. John Wiley & Sons, New Jersey. Wixom, B, Watson, H & Werner, T. 2011. Developing an Enterprise Business Intelligence Capability: The Norfolk Southern Journey. MIS Quarterly Executive, vol. 10, no. 2, pp. 61 – 71. Read More