Business Analytics - Assignment Example

Implement a Data Warehousing Solution alongside the Formation of a Business Analytics Table of Contents Project Overview 3 2. Data Availability for the Warehousing Solution 4 2.1. Geography and Market Share Related Information of Morrisons Plc 4 2.2. Operations Related Information of Morrisons Plc 4 2.3. Product and Service Related Information of Morrisons Plc 5 2.4. Data Related To Financial Performance of Morrisons Plc 5 3. Summarization of Literature and Its Integration with the Chosen Business Process 6 3.1. Literature Review 6 3.2. Integration of Data Warehousing With the Available Information of Morrisons Plc 8 4. Issues and Procedures Necessary for Implementation of Data Warehouse System within Morrisons Plc 10 4.1. Potential Issues 10 4.2. System Implementation Steps 12 5. Implementation of Business Analytics Tools 15 5.1. Regression 15 5.2. Optimization 16 5.3. Data Mining 17 References 19 1. Project Overview Enterprise Data Warehousing (EDW) in the modern marketing scenario has become a credential element that assures appropriate survival and development of every business process. The concept acts like a central repository where data from multiple sources are accumulated and then analysed for reporting purpose. In this context, every functional department of an organization can upload their recent annual or quarterly basis data or can even analyse the existing data in case an organization is subjected to any form of operational issues or undertakes a decision plan for new product development (StatSoft, Inc., 2000). The concept also encompasses multiple other functional aspects such as data mining and process optimization (Kearney, n.d.). Apart from these, the system design of the data warehouse also varies depending on an organization’s functional needs. The detailed elaborations regarding such system variations are discussed in relation to which data warehousing can be performed effectively. Moreover, the present scenario also illustrates the structuring and implementation of a data warehousing solution within Morrison Plc, a leading supermarket chain currently functional within the UK. The company has been performing operations with the intention of meeting the needs of customers in an effective manner. In this regard, the company has adopted and implemented different strategies that are centred on the customers by providing distinctive products that .include food, integration with manufacturing business and craft skills. Subsequently, the company with the aim of attaining a competitive position has focused on value and quality of products and/or services provided to the customers (Kimballb, n.d.). The objective also encompasses the formation of a ‘business analytics department’ within the ‘corporate headquarter of Morrisons Plc’. Likewise, the discussion entails an in-depth elaboration of all the potential issues that may emerge during planning, implementation and systematic utilization of the data warehouse system (Kimballb, n.d.). 2. Data Availability for the Warehousing Solution 2.1. Geography and Market Share Related Information of Morrisons Plc Morrisons Plc was founded during the period of 1899 as a small retail business process within the domestic markets in the UK. However, over time, this brand has projected drastic levels of expansion within its functional structure and thus, has eventually transformed itself as the fourth largest supermarket business within the UK. Morrisons plc currently holds a market share of approximately 11 per cent of the total supermarket retail business of the UK (Wm Morrison Supermarkets plc, 2014). Morrisons Plc also endorses as a New York stock exchange enlisted company and currently administers a total count of 515 supermarket stores spread out all across England. In addition, the brand projects keen interest within the manufacturing and selling of a wide base of retail products that majorly encompasses fashionable garments and food items. With due course of time, Morrisons Plc also acquired multiple other retail brands such as Safeway with the prime intention of expanding its business boundaries. In this context, the brand was also subjected to multiple financial obligations due to sub-brand acquirements (Wm Morrison Supermarkets plc, 2014). 2.2. Operations Related Information of Morrisons Plc From a general perspective, one can categorise Morrisons Plc as a successful retail chain within the UK that aims towards expanding its business process through continued attainment of other small and medium scale retail business processes. In addition, the brand keeps a close eye on the product pricing rates as structured by multiple other retail chain giants such as Tesco and Aldi. Based on the observed variations, Morrisons Plc brings in considerable level of changes within its product pricing structures with the prime intention of attracting higher percentage of customers and improve market share within the UK’s retail sector. Morrisons Plc also focuses on the implementation of a unique ‘Match & More points’ attainment strategy within which, it provides specific points to the customers for every purchase they make. Rest apart, the brand has also brought in high level of diversification within its operational areas. Contextually, apart from just remaining confined to the ‘traditional supermarket stores’, Morrisons Plc over time has also introduced ‘Kiddicare’, ‘Morrisons Cellar’, ‘Nutmeg clothing’ and ‘Morrisons Online’ as appropriate reflection of its operational diversification. 2.3. Product and Service Related Information of Morrisons Plc Morrisons Plc projects equal involvement within both the products manufacturing and service areas. The brand has been majorly focussed towards manufacturing and selling of multiple kinds of cooked, ready to eat and frozen food items. Apart from these, the brand also categorises dairy products as a prime component of its product base. Certain sub-units administered by Morrisons Plc also projects keen involvement towards the manufacturing and selling of kids garments. Moreover, Morrisons Plc aims towards bringing in a diversified base of cooked food products during joyful events such as Christmas or Thanksgiving. 2.4. Data Related To Financial Performance of Morrisons Plc Apart from the operational and product/service areas, Morrisons Plc has also witnessed intensive levels of fluctuations within its financial performance as a result of the changing economic scenario. For instance, the 2013-14 financial year brought in considerable amount of complicacies within the financial growth of this brand in comparison to that of the growth figures attained during the 2012-2013 financial year. The specific comparative details have been provided below in a tabular manner. Financial Period Total Turnover in (£) million Profit Before Tax in (£) million Profit After Tax in (£) million 2012-2013 18,116 879.0 647.0 2013-2014 17,680 (176.0) (238.0) From this tabular representation, it can clearly interpreted that there has been a decrease within the total profit attainment of Morrisons Plc as a result of stiff competition imposed on it by other competitive brands currently functioning within the domestic market space of the UK. As a matter of fact, this can be considered as a credential data that should form a part of the data warehouse solution. 3. Summarization of Literature and Its Integration with the Chosen Business Process 3.1. Literature Review Manjunath & et. al. (2012) noted that from a technical perspective, data warehousing concept as a repository setup where every single aspect of business process can be stored for future usage. Manjunath & et. al. (2012) focussed on the wider applicability of this concept within the profit making organizations, which often requires simplified and specific access to previously accumulated data for making strategic decisions (Manjunath & et. al., 2012). In alignment to all these, Joseph (2013) illustrated a similar kind of thoughts regarding the implementation of data warehousing model within large-scale retail business organizations. According to Joseph (2013), data warehouse model establishes a bridge between the past and current recorded business events that can be utilized for making future decisions for the effective growth and survival of a business process (Joseph, 2013). In this regard, Ramageri & Desai (2013) illustrated thoughts on the utilization of data warehousing solutions within retail business sector. As per Ramageri & Desai (2013), retail business processes are subjected to intensive competition due to the rapid increase within their count. Thus, appropriate utilization of data mining techniques gradually helps in analysing the previous business related data based on which new strategies can be formulated. The authors also focussed on how data mining and data warehousing concepts gradually helps in providing the retail business processes with dynamic transformational capabilities through which the organisations can easily adapt to the changing external environment (Ramageri & Desai, 2013). In contrary to the above-discussed facts, Sharma & Jain (2013) elaborated the concepts of data warehousing and data mining from a technical perspective. According to Sharma & Jain (2013), the technique through which data warehousing solution supports the establishment of a close link between the static and dynamic data are stored within the databases on regular basis. Specific level of focus was also provided on the utilization of such dynamically stored data in context to periodically tracking the growth rate of business process within a competitive market space (Sharma & Jain, 2013). Likewise, Merritt & et. al. (2004) projected thoughts regarding how the retail business organizations in the present market space can use data warehousing and data mining solutions as a competitive advantage for sustaining intensive competition laid down by other major retail players. Merritt & et. al. (2004) noted that small, medium and large scale business processes have started ignoring technological implementation within their functionality as a mandate factor rather than considering it as an expensive initiative (Merritt & et. al., 2004). Boateng & et. al. (2011), elaborated about the possible types of data warehouse systems that can find effective integration with the existing systems of the retail brands such as Morrisons Plc. According to the study of Boateng & et. al. (2011), the possible advantages and disadvantages that Morrisons Plc may be subjected to as a result of its data warehouse solution implementation. Facts mentioned by Boateng & et. al. (2011) also projected about the diagrammatic representation of data warehouse implementation within the existing IT framework of the retail business organizations (Boateng & et. al., 2011). In a similar perspective, Oueslati & Akaichi (2007) stated about the evolutionary pattern of data warehousing techniques and support provided to the retail and other business sectors in the present competitive global market. Specific amount of preference has been provided on the relational schemas that are meant to create and maintain the integrated databases of the data warehousing models (Oueslati & Akaichi, 2007), Reddy & et. al. (2010) illustrated regarding how data mining along with OLAP technological concepts can be utilized for supporting the business decision-making procedures undertaken by firms in case they subjected to any sort of competitive fluctuations within the market. As per the study of Reddy & et. al. (2010), the necessity of creating a separate storage module for the data warehouse solution rather than just integrating it with the dynamic information storage facility of the retail organizations (Reddy & et. al., 2010). 3.2. Integration of Data Warehousing With the Available Information of Morrisons Plc Selection of the Data Warehousing System In technical terms, data warehouse system can generally be of two specific types that include normalised and dimensional. Both of these systems projects vast variations within their implementation, data input and maintenance techniques. In this context, data inputting and upgradation of normalised data warehouse system is much easier in comparison to that of the dimensional data warehouse system. However, dimensional data warehouse system possesses the potency of handling complex data that are subjected to continuous upgradation (Singh & Singh, 2012). Considering these aspect, it is worth mentioning that Morrisons Plc should implement the dimensional data-warehousing model within its functional structure. Justification regarding such selection can be provided based on the fact that being a retail brand and an administrator of multiple stores, Morrisons Plc is subjected to considerable alternations within its inventory levels and revenue generation rate, which can be utilised for making future strategic plans (Singh, & Singh, 2012). Moreover, it is suggestive for Morrisons Plc to implement ‘data mart system’ within the data warehouse solutions of its individual retail outlets. Whereas, the ‘predictive analysis system’ should be implemented within the data warehouse solution of Morrisons Plc’s corporate headquarters. The prime intention to utilize the ‘predictive analysis system’ for making suitable strategies all focussed towards promoting appropriate business growth (Conjecture Corporation, 2014). Integration of Data Warehousing Models with the Available Data of Morrison’s Plc Assuming the entire system to be of centralised design, it is suggestible for Morrisons Plc to accumulate the available data from all their functional areas within the central data warehouse repository located within their headquarters. The standalone functional units will specifically comprise normalised data warehouse systems equipped with data mart concepts capable of handling regular transactions and changes within financial, operations, products sales and service provision related aspects (StatSoft Inc., 2014). Whereas, the data warehouse repository system that will be established within the headquarters should be of dimensional type and equipped with predictive analysis concepts. Using such concepts, the decision makers associated with the brand can easily implement the data mining techniques with the prime intention of evaluating the dynamically stored data for making future functional strategies (StatSoft Inc., 2014). The regular data transmitted by the individual retail outlets will be transformed in the form of Meta data and provided to the data analysts administering the EDW. Specific focus should be provided on the frequent alternations within the product sales and service related activities (StatSoft Inc., 2014). Justification for this emphasis can be provided based on the fact that Morrisons Plc being a retail brand will structure all its strategies towards intensifying their percentage of goods sales and revenue attainment. Furthermore, the designated business analytics department should implement data analysis mechanisms such as OLAP and data mining for making appropriate functional predictions related to Morrisons plc (ZenTut, 2014). 4. Issues and Procedures Necessary for Implementation of Data Warehouse System within Morrisons Plc 4.1. Potential Issues Issues Related to Planning Establishment of a data warehouse solution within a large-scale retail business is a complex process and requires implementation of multiple complex planning procedures. Thus, for attaining high levels of efficiency within the established system, the architects will have to structure appropriate strategies regarding every functional dimension to which this system will be catering. Any sort of lag within the planning stage will hamper the precise system setup. Moreover, predictions attained from such an inefficient system will also be flawed. Inappropriate planning regarding repository setup may also pose the risk of data damage that has been attained from the individual retail outlets (Al-Debei, 2011). Issues Related to Resource Attainment Certain large-scale retail business processes such as Morrisons plc may require huge amount of financial, and networking and hardware resources for establishing a data warehouse system equipped with varied data mining and analysis tools. If seen from a general perspective, it can be stated that such massive resource requirements may incur high percentage of financial investment for the brand. As a matter of fact, it can be categorised as a potential issue that Morrisons Plc may face during the establishment of its data warehouse system (Al-Debei, 2011). Issues Related to Appropriate Attainment and Maintenance of Data Implementation of data warehousing system within Morrisons Plc may increase the effectiveness associated with resource utilizations, appropriate production and precise revenue generation. However, due to its online nature, the data warehouse systems are often subjected to multiple risks related to tampering or theft during transmission to the central repository. Moreover, due to the repeated upgradations of new data on daily basis, the possibilities of data redundancies also increase by considerable rates and thus, pose a significant threat to the authenticity of the data stored within the central database. The organizational data stored within the central repository will also have to be maintained at frequent rates, as failure to which will gradually hamper the data analysis and the strategy structuring techniques (ProjectManagement.com, 2014). Issues Related To System Design of the Data Warehouse Data warehouse systems handling huge loads of analytics and performance related data often project high level of complexities within their functional designs. As a matter of fact, it also becomes highly difficult for the system analysts to manage such systems in the best possible manner. Thus, under excessive operational load, the chance of system failure and human error occurrence maximizes, which raises questions on the system efficiency (DWBIConcepts, 2014). Inappropriate nature of the attained data may also pose considerable amount of risk on the effectiveness of the new data warehouse system established within Morrisons Plc. All these in turn will make it difficult of the data analysts in context to attaining appropriate strategic conclusions in favour of the brand. Thus, it is highly suggestive that the challenges associated with storage of inappropriate data are mitigated in an effective manner (DWBIConcepts, 2014). 4.2. System Implementation Steps The systematic implementation of a data warehouse model within Morrisons Plc will require the adherence of certain specific steps that has been provided below in a diagrammatic manner. The primitive stage of system establishment includes identification of the objective behind such initiative. This stage encompasses aspects such as identification of the lagging areas within the functional structure and profit attainment techniques of Morrisons Plc. In addition, the primary stage encompasses the attainment of appropriate hardware and financial resources that will be required for accomplishing the project within the estimated time period (Dewald, 2002). Rest apart, the primary stage associated with the establishment of the data warehouse system within Morrisons Plc also incorporates primitive planning regarding the type of data mining techniques that will be implemented on the stored data. In this context, the data mart system will be implemented on the transactional data associated with product sales and services, whereas the Online Analytical Processing (OLAP) and Online Transaction Processing (OLTP) will be applied on the data related to operations and revenue generations (Dewald, 2002). Only ‘predictive analysis technique’ will be applied on the hierarchically arranged warehouse data for making suitable predictions about future strategies that will be structured for facilitating the growth of Morrisons Plc (datawarehouse4u.info, 2009). The second data warehouse establishment within Morrisons Plc should encompass the data attainment stage where the routine data from all the functional dimensions of this brand are transacted in the central repository established within the headquarters. This stage also encompasses the establishment of appropriate network setup through which an assurance of appropriate data transmission can be attained. Specific precautionary measures should also be taken regarding data tampering and theft during online transmission (Syntel, n.d.). The third stage of data warehouse establishment within Morrisons Plc will require transformation of the dimensional data. Justification regarding this can be provided based on the fact that ‘predictive analysis technique’ for strategy formulation can only be implemented on the dimensional data model. Following this stage is the fourth stage where the transformed dimensional data is converted into a dimensional model that will find applicability within the data mining techniques (Velicanu & Matei, 2007). The dimensional model will then be uploaded within the data warehouse database. Once this procedure is accomplished, the system will generate certain summarised value for the uploaded data that will be projected to the data analyst. The overall data warehouse system establishment procedure also encapsulated the implementation of specific schemas and system design algorithms with the prime intention of attaining hierarchically arranged data (Microsoft, 2014). 5. Implementation of Business Analytics Tools Implementation of data mining techniques on the functional data warehouse of Morrisons Plc will be considered as a crucial piece of the strategic puzzle that is meant to improvise the business process irrespective of the increasing levels of market competition. This technique encompasses effective evaluation of the organizational data that has been stored within the centralised data warehouse repository established within the headquarters of Morrisons Plc. For the provided context, three specific analytical tools has been chosen that include ‘optimization’, ‘data mining’ and ‘regression’ for evaluating the data (Microsoft, 2014). The detailed elaborations of these three specifically chosen analytical tools have been provided in the undermined sections. 5.1. Regression Regression generally refers to an analytical tool that is specifically meant to estimate probabilistic values through evaluation of the dimensional data stored within the centralised data warehouse repository of Morrisons Plc. As a matter of fact, it can be categorised as an element of data mining technique. Moreover, the applicability of this analytical tool can be found for making prediction regarding the percentile growth that Morrisons Plc can receive in the future period. In order to carry out this analysis, the system analyst will first have to evaluate the level of authentication associated with all the hierarchically stored warehouse data with the prime intention of eliminating the possible chances of error occurrence (Oracle, 2014). For effective predictions, the strategists will have to identify the dependent variable and the independent variables, both of which form the most crucial aspects of this analysis. For instance, predictions regarding increase in sales of goods during the period of economic recession (Oracle, 2014). Further recommendation for the data analytics department includes selection of appropriate regression techniques in case the count of analysis coefficients increases. In this context, it is suggestive that the data analytics department should choose the linear regression model in case the prediction specifically depends on two confined coefficients. Whereas, suggestions regarding the use of multivariate nonlinear regression technique can be provided in case the count of coefficient increases (Oracle, 2014). In addition, the strategists will attain the regression outputs in the form probabilistic values between the range of zero and one. Certain specific charts and graphs can also be attained as output based on which suitable strategies for future growth of Morrisons Plc can be formulated. Thus, the contribution made by regression technique within the strategic decision making procedure of Morrisons Plc can be witnessed in a probabilistic manner (Kearney, n.d.). 5.2. Optimization The data warehouse optimization technique projects high level of complexity in comparison to the regression model. This technique specifically emphasizes the refinement of the hierarchical stored data before it is utilized for further analysis. As a matter of fact, specific suggestions can be provided to the ‘business analytics department’, regarding the attainment of a ‘neutral data model’ comprising specific information related to the project objective. Normalization of the tabulated data may prove to be a potential element for the optimization technique that requires the implementation of specifically structured relating to schemas and functional keys (Teradata Corporation, 2008). The business strategists can also implement the data warehousing optimization technique for attaining summarised version of the rationales implemented in a new business or functional growth strategies. Thus, in a cause and effect relationship, the data warehouse optimization technique can also be utilized for minimizing the probable percentage of error occurrence during data storage procedures (Teradata Corporation, 2008). In this context, from a general perspective, it can be noted that the optimization technique will support the business strategists for establishing simplified and transparent relationship between the hierarchically arranged existing data entries. As a result, the technique can predetermine the success rate of a business-oriented strategy that the strategists within Morrisons Plc intend to implement in due course of time for accomplishing the perceived objectives. Associated benefits of data warehouse optimization technique will also help the business strategists for navigating from one data entity to another without being subjected to the associated hassle of data damage (Teradata Corporation, 2008). 5.3. Data Mining Specific elaborations of the data mining techniques have already been provided in the previous sections of the discussion. Irrespective of the effectiveness projected by the two above discussed techniques, it is highly suggestive that the business strategists within Morrisons Plc should implement the data mining technique within its data warehouse database for undertaking appropriate evaluation of data. Justification regarding such suggestion can be provided based on the fact that the data mining technique provides an option of normal system database and the data warehouse database. Thus minimizing the hassle related to data comparisons between both normal system database and the data warehouse database to the minimum possible levels (Kimball, 2008). Moreover, it is advisable that the business strategists should implement the clustering technique of data mining on the data stored within the central repository (Han & Kamber, 2006). In an eventual manner, this will help Morrisons Plc in establishing suitable linkage between the data objects related to similar category. Contextually, Morrisons Plc can utilize the reports attained from data minimum technique within new product development strategy by taking consideration of the sales figure and revenue attainment rates of the previous year. 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