Business Intelligence Plan Essays

Business Intelligence Plan Essays Business Intelligence Plan Essay Business Intelligence Plan Essay Business Intelligence Plan Executive Summary The purpose of this report is to explain the importance of Business Intelligence and all of its components for implementation into the business structure. During the recent years obtaining useful information in real time has become something that is extremely important, if not even a critical, factor of success for companies. The time managers have available for use in making business decisions has been reduced dramatically. Competitive pressures are now requiring that businesses make intelligent decisions based on their incoming business data, and these decisions ust be made immediately (Business Intelligence and Data Warehousing, 2005, p. 5; Hocevar Jaklic, 2010, p. 91). Businesses are looking at tools that will enable them to keep up with technology and its swift movement throughout the business environment. The tool that will enable those managers to do this is called business intelligence. Due to the swift pace of todays business environment, these systems of business intelligence have become an almost indispensable part of the success of many organizations. With the aid of business intelligence, managers are able to fficiently and effectively detect important trends, analyze the behavior of customers and facilitate expedient decision-making (Hocevar Jaklic, 2010, p. 91). Business Intelligence is defined as a broad concept which includes the appropriate orientation of the entire organization. It deals with the acquisition, management and analysis of large amounts of data about business partners, products, services, customers and suppliers, activities and transactions between them (Lu Zhou, 2000, p. 3; Hocevar Jaklic, 2010, p. 92). The implementation plan provided will allow the stakeholders to nalyze the many different ways in which business intelligence will help the company grow. In this implementation there are several different structures that will assist the company with business intelligence. Areas covered will include a comprehensive review ot Business Intelligence, data sources and characteristics in the organization, data governance and quality overview, the selection of a data mining vendor, business analytics summary along with the selected vendor, Bl service provider value and provider selection, and lastly a business dashboard with the vendor comparison. Introduction to Business Intelligence According to Skriletz (2002) reviewing the principles behind Bl best practices and technology innovations reveals some interesting points about managing Bl strategically. The principles are to: Create stability in the basic structures of data fundamental for providing Bl and running the business. Ensure that each data element stands on its own as a fact or attribute. Keep an enterprise-wide focus, not a departmental, regional or other category focus. Make Bl not simply the analytical report, but the information a manager or executive needs in order to make an informed decision. Use several different Bl technologies that play well together. The organizational roles that will benefit from business intelligence (81) would be marketing managers, medical professionals, Logistic managers, campaign managers, HR managers, and also office managers. When effectively integrated into processes, Bl can help an organization meet mission-critical goals such as improving sales results, growing customer satisfaction, workflow processes, advancing corporate goals including sustainability management, cultivating return on investment, and saving lives (Felix, 2009). A component of Bl would be Data. This component is considered to be one of the key components of business intelligence. When implementing business strategies an important component most times is the data. Data is always created in order to derive at business decisions that are being made. The source of the data will also be extremely important and will aid in understanding trends and issues that exists for the business. Another component is the data quality. This is an essential component of Bl for arriving at valid business decisions. Similar data can be stored in multiple repositories within a single business; one example would be he inventory of items for sale. When considering data sources, it is important to understand if there is a recognized authoritative source for a specific type of data. In many enterprises, the data required for making business decisions is created by wide ranging applications, perhaps from separate lines of business (Theme 1). Bl systems today have the capacity to work with numerous types of data such as numerical or non-numerical data. The quality ot this data is as important as any other data. The difference in the level of data quality is one of the many factors that ay explain why some organizations are successful with their Bl initiative while some are not so successful (Isik, 0. , Jones, M. C. , Sidoroya, A. 2011). Component three is Data Analytics. This component is used for the analysis of data. Data analytics refers to the business intelligence technologies that are grounded for the most part in data mining and statistical analysis. Due to the success that has been achieved overall by the data mining and statistical analysis community, data analytics continues to be an active area of research (Hsinchun, Chiang, Storey, 2012). Data analytics are tructured in statistical theories and models, multivariate statistical analysis. It also covers other analytical techniques such as regression, factor analysis, clustering, and discriminant analysis that have been used and have been successful in innumerable business applications (Hsinchun, Chiang, Storey, 2012). Also, in order to create a Business Intelligence environment you would need to build an analytical data warehouse for managers. In many institutions, the most important decision metrics are calculated based on information obtained from numerous systems (Mircea Andreescu, 2009). An industry that could and probably does benefit from the application of business intelligence would be Industrial-organizational (1/0) Psychology. Psychological research is riddled with data that is essential to the outcome of the research being done. Data analytics and data quality is essential to research. In doing research there is much value that can be derived from the use of business intelligence. Because one of the components is data analytics this adds direct value to the research. Research is a compilation of data that is used for validating theories. Industrial-organizational psychologists help companies bring bout compliance and raise employees productivity in the workplace. They will also focus on the operation and design of organizations (Feldman, 2013). The scientific method in psychology require that the approach used by psychologists to systematically acquire knowledge and understanding about behavior and other phenomena of interest is done in a way that can be explained, seen, and proven with more accuracy (Feldman, 2013). The potential value that could be derived from its use is that you can achieve valid data, as well as being able to use the statistical data that s derived using methods such as factor analysis, clustering, and regression in a way that aids in achieving reliable data. For instance, if they are doing research on emotional intelligence because of using data quality and data analytics researchers may be able to find out how to have increased efficiency in communication that will lead to the resulting ability to establish mutually beneficial working relationships (Bennett, 2009). Business Intelligence adds value to the data that is being gathered by providing the 1/0 psychologists with a tool for providing the companies that they re working for researched data and valuable information that can aid in the enhancement of employee/employer relationships. It can also lead to improved decision making for the companies also. In the next section of this paper we will be discussing the data sources and also characteristics in the organization. This section will give some clarity on data and how it is used in Business Intelligence. Data Sources and Characteristics in the Organization The Term big data has emerged to describe the growth ot data along with systems and technology required to leverage it. As with many new technologies, the term has yet to be universally defined, but generally speaking, big data represents data sets such as structured and unstructured data that can no longer be easily managed or analyzed with traditional or common data management tools, methods and infrastructures (Rogers, 2011). Even today, the scope of big data is growing so rapidly. It is growing beyond niche sources to include sensor and machine data, transactional data, metadata, social network data and consumer authored information (Rogers, 2011). There is a lot of room for research opportunities that xists for Business Intelligence. It can be used for managing semi-structured information (Negash, 2004). Data Sources Structured data is going to be produced by a generally large amount of information that includes a vast amount of raw data. Structured data is anything that has an enforced composition to the atomic data types. Structured data is going to be managed by technology and it allows for querying and reporting against predetermined data types and understood relationships (Weglarz, 2004). Structured data typically resides in databases. Such data is organized into tables with columns nd rows of defined data types; relationships between various data fields and tables are clearly defined. Some of the most common are relational database management systems (RDBMS) that are capable of handling large volumes of data such as Oracle, IBM DB2, MS SQL Server, Sybase, and Teradata (Brannon, 2010). Unstructured data is going to consists of any data that is stored in an unstructured format at an atomic level. That is, in the unstructured content, there is a no conceptual definition and no data type definition in textual documents, a word is simply a word (Weglarz, 2004). Unstructured data is going to reside outside of structured databases. This data includes electronic documents, PowerPoint presentations, spreadsheets, email, images, schedules, IM logs, and Multimedia files, etc. This data will also usually reside on individual computers or on file servers (Brannon, 2010). HR Data Sources Human Resource (HR) collects and utilizes data as a direct result of their daily activities. They utilize unstructured data at a high volume and also have a high volume of structured data that they have to handle. They are collecting data on prospective employees, current employees, and even former employees. Information such as payroll, hours worked, vacation time, sick time, days absent, performance evaluations, pay grade, raises, age, date of birth, start date, end date, Job title, etc. need to be input and stored into a myriad of spreadsheets that exists within a database that is designated for that specific information. This activity of collecting information and inputting information leads to high volumes of workflows and massive amounts of related data (PemmaraJu, 2007). As HR is called on as a partner in corporate decision making, it must first began by taking inventory of what is being easured and how. HR first needs to make sure the right applications and technologies are in place. Even though human resource information systems are increasingly implemented, if the proper tools are not available to access the data in a meaningful way, the data collected is of little use. An abundance of data should never be confused with implementing true workforce analytics (PemmaraJu, 2007). Importance of Data Characteristics Business intelligence can attord an organization wit n the means to an end. There is so much data that needs to be taken into account when conducting business that it is rucial that a business has a way in which to maintain all information needed. When using Bl data characteristics will aid in the success of Bl. When making decisions in regards to business matters data characteristics will weigh heavily on the outcome of the decision. Having sound and effective data is going to be imperative. According to Sauter (2011, p. 3) good decision making means we are informed and have relevant and appropriate information on which to base our choices among alternatives. Data Characteristics Relevance One characteristic is the relevance of the data. Relevance of the data can be defined s a function of the choices and alternatives available to the decision makers (Sauter, 2011, p. 78). Relevance is going to be of value to the HR department because of the amount of information that they will have stored on each employee. Maintaining only needed information according to relevance is going to be essential to quality, timeliness, and storage space. Reliability Many will assume that the data is correct if they are included in the database; designers therefore need to ensure that they are accurate. They should always verify the input of the data and the integrity of the database (Sauter, 2011, p. 0). The value of data reliability is that there is time saved on time being spent with the periodical reports (report collecting, diverse consolidations and adjustments, reducing the amount of time spent on repetitive activities, reducing the part played by the IT department in generating reports in favor of the end-user and , the most important thing, reducing the time for decision making (Luminita Magdalena, 2009). Understandability According to Sauter (2011, p. 76) if decision makers cannot understand what is in the database and if the database lends itself to perceptual errors then the decision akers cannot use it effectively. The key is going to be simplifying the representation in the database without losing the meaning of the data. The value that this will add to HR is that it will save time when they are attempting to make decisions when hiring, firing and going over information that is being used for assessments and benefits. Summary These data sources and characteristics can provide the HR industry with tools that will assist them in effectively using Business Intelligence. Because there are many different sources and characteristics it is essential that businesses know the value of having this information. There is so much information out there and so much information and data that we have to rely on. The importance of data and its structure should always be utilized with reverence to the business. Protection from too much information and providing access to useful data without overwhelming or misleading is going to be a valuable asset to any business (Sauter, 2011, p. 85). Having useful information is key to helping the decision makers make sound decisions for the business. This next section will discuss data governance and quality overview. The importance of both will be seen in this section providing the rganization with valuable information in regards to this area of Business Intelligence. Data Governance and Quality ta governance will assist the company in ascertaining power over now intormation is handled. This will give assurance to those within the organization that the company is being managed in the proper manner when it comes to processing and handling information. When data governance is incorporated into the business process the company can better monitor issues. Data governance is also about teamwork and helping those within the company to work together in a more consistent and orderly method. According to Sarsfield, (2009, p. 153) the choice about data governance is one about hearing the voices of your people within the organization. The sound of bad harmonization and discord can be heard loudly in companies that dont listen to it. Its only when you harmonize the voices of technologists, executives and business teams that it allows you to product a beautiful song; one that can bring your company teamwork, strategic direction and profit. Data governance is going to be important to the existence of the company. It is going to help the company more effectively manage our data and treat it like the valuable nterprise asset it is. In all actuality effective data governance isnt going to be Just about data at all. Its also about changing how the company views its data (Griffin, 2011, p. 11-12). Data governance will assist with the guarantee that our data can be trusted and that people can be made accountable for any adverse event that happens because of poor data quality. It is about the company putting people in charge of fixing and preventing issues with data, so that the enterprise can become more efficient (Sarsfield, 2009, p. 38). Having said that governance of company stakeholders will be essential to the company and its goals. In order to effectively implement governance there needs to be a governance team in place. One critical step in establishing a data governance program is identifying the owner of each type of data. Once you can link data quality to the processes and people that generate the data, you can get accountability for on-going maintenance (Badrakhan, 2010, p. 36). Organizational Roles The organization roles will include a cross-functional team along with the Board of Directors, CEO/President, COO, VP Finance and Operations, and a chief data office (CDO). Board of Directors The role of the board should be set down in the organization by-laws. So too must the policies and procedures be established and documented. These will differ between organizations, but it is important that all board members be informed of their expected roles and responsibilities at the beginning of their term. CEO/President The CEOs main duty is setting strategy and vision. The CEO ultimately sets the direction of the organization. The CEO decides, sets budgets, forms partnerships, and hires a team to steer the company accordingly. COO Through a respectful, constructive and energetic style, guided by the objectives of company, the COO provides the leadership, management and vision ecessary to ensure that the company has the proper operational controls, administrative and reporting procedures, and people systems in place to effectively grow the organization and to ensure financial strength and operating efficiency (www. shrm. org). VP Finance and Operations In this role the VP Finance and Operations will advise the president and other key members of senior management on financial planning, budgeting, cash flow, investment priorities, and policy matters. They will also serve as the management liaison to the board and audit committee, while continuing to ettectively communicate and present critical financial matters at elect board of directors and committee meetings (www. bridgespan. org). The CDO or equivalent position should have real accountability and may exist at either or both of the group and the divisional levels depending on the culture and strength of the bonds between the business units. There will also be principles set forth and they will become the key performance indicators (KPIs). The role and the budgets should include an element expressed in terms of the selected information currencies. Apart from the authority that the role needs to carry though a senior executive reporting ine, the office of the CDO should have direct accountability to the board and specifically the audit subcommittee or its equivalent (Hillard, 2010, p. 24). Data governance for the stakeholders can lead to effective compliance with government regulation, improved customer satisfaction, improved market position, cost reduction, improved business intelligence reporting, and the ability to respond faster to business change (Waddington, 2010, p. 5). Data quality is going to be an essential aspect of data governance; therefore the company is going to need to place a great amount of emphasis on measuring data quality. Success in a Business Intelligence environment will require that people in the organization and the team trust the results they receive. This can only be accomplished if the data meets quality expectations set forth by the data governance team and it is clearly understood by the business and its community (Geiger, 2010, p. 37). A detriment of poor data quality might be when information is put into the computer by someone for the purpose of obtaining a number that provides them with how much to charge someone for something like insurance. If the age is incorrect then that could cause the rates to be ither higher or lower because age is an important part of the quoting process. So for instance if someone was to put 21 instead of 41 in the system for the persons age that could cause their rates to go up because of the level of experience that is sometimes equated to the age. Not knowing that there is an error in the data could cause the company to lose business because the person feels that the quote is too high. Data governance will provide standards that will be set forth in regards to the data quality. There are going to be three key elements that exist for data quality measurements. They should be applied to the data residing in the database. They are completeness, compliance, and accuracy. The completeness measure provides a count of how many records in a data set are missing one or more details. The compliance counts the records that fail to meet business rules on each record. The accuracy measure estimates using statistical and other techniques whether there are likely to be errors in the data set (Hilliard, 2010, p. 160). These elements will aid in assisting the team with data governance. The goal of data governance is not Just to clarify who owns data but also to optimize its value. The data itself is merely the means to the desired end of improved business performance. Accordingly, the responsibility for data governance efforts should fall at least as much on the business as it does on IT?and preferably more (wmw. nformatica. com). Data governance will provide the business with proper guidelines in order to effectually operate. Data governance is also key to providing sufficient data quality. The implementation of data governance through business intelligence will ultimately provide the company with a tool to monitor and sustain efficient data quality. The next section will iscuss data mining and also obtain a choice tor a vendor that will provide the organization with what it needs in order to effectively launch a successful Bl implementation. Data Mining Data Mining is considered to be a deeper search in the source data. It is also known as knowledge discovery in large databases. It is very a very powerful instrument that can often be used to extract useful information. Many times the knowledge can be considered to be previously unknown valid and the same time operational. The extracted knowledge has to be translated and applied in reality. Data Mining is going o be different from some of the other data processing for data analysis, such as data query, reports, OLAP etc. Information obtained through Data Mining techniques can be predictive or descriptive. Predictive information is used to describe an event, such as the possibility of fraud (Emil Claudia, 2010, p. 806). Given a data set, the data mining process is going to start off with elementary data analysis. It will allow the analyst to understand the characteristic of the attributes of the data set (dependency, ranges, max, min, count, average, standard deviation, quartiles, outliers, etc. ). The data set is then going to be divided into a training set and a testing and validation data set (holdout). The training data set is used to build the mining structure and associated mining models. If a model is valid and its accuracy is acceptable, it is then used for prediction Oafar, 2010, p. 17). Data mining is also a valuable tool in decision report and uses algorithms and statistics to analyze large data sets. Data mining is going to connect data to the business practice. It can also be used in business to understand customers. Data mining is going to measure what the business knows and what they should know. Data Mining has proven to be highly effective in addressing business problems. By taking advantage of the information treasures in a data mine companies are able to fit pieces of information together to solve their most challenging business puzzles and those of their clients. If you use data mining to support business planning and decision making you are able to put together the vast amount of data in order to see the big picture (Mehok, 2013, p. 83). The two data mining vendors to choose from are Weka (Pentaho) and RapidMining. Both will offer some great qualities that will only add to the success of the rganization. RapidMining The first vendor is RapidMining. RapidMining is a product of Rapid-I. Rapid-I offers software and services for business analytics. The company was founded in 2006, and is headquartered in Dortmund, Germany. It has been in development since 2001. The company has more than 30 partners on all continents, 3 million production downloads, more than 35,000 production deployments and more than 400 customers in more than 40 countries (rapid-i. om). RapidMining is data mining software that offers a complete package to its users. The software is easy to use and install. Installation usually takes about five minutes. It is considered open source software. It is one of the most comprehensive data mining solutions that offer data integration, transformation, and modeling methods. It is a complete business intelligence solution. RapidMiner is a complete business analytics workbench with strong foc us on data mining, text mining, and predictive analytics. It uses a wide variety of descriptive and predictive techniques to give you the insight to make profitable decisions. There are no software license fees and it offers flexible and affordable support options. Fast development is available even when highly complex data mining processes exist. RapidMiner also offers guaranteed operational reliability. RapidMining offers one tool for all task involved allowing the processes to interact with one another and be transformed for integration with a few clicks ( rapid-i. com). Some of the mining methods offered are stream mining, In-database-mining, and Radoop. For stream mining instead of holding complete data sets in the memory, only parts of the data are taken through an analysis process and the part results aggregated in suitable location later on. Instead of taking the data to the algorithm n-database-mining supports taking the algorithms to the data. Radoop is the worlds first graphical connection of Hadoop for the handling of big data analytics, meaning that even terabytes and petabytes of data can be transformed and analyzed (www. Rapid-l. om) Weka (Pentaho) The second vendor is Weka. Weka was originally created in 1993 at the University of Waikato in New Zealand and was available on sourceforge. net since 2000. The Weka project was established by the University of Waikato as a platform for the research and testing of advanced machine learning techniques. Since that time, Weka has eveloped a large and loyal following in both academic and industry c ircles, and has been downloaded more than 600, 000 times (www. pentaho. com). Sometime in 2006 Pentaho Corp. , acquired the Weka open source data mining project. Pentaho is considered to be one of the worlds largest open source business intelligence suites out there. Weka is currently widely used as a great tool for data mining. It has what they call an open source code and it can be used for machine learning and it can also be used for mining large datasets. Machine learning algorithms will aid in finding significant consistencies in large data sets. Weka is easy to use and easily accessible. Weka is considered open source software and it is issued under the GNU General Public License. Organizations use Wekas data mining tools to understand relationships between internal factors like price, product placement, or staff skills as well as external factors like economic indicators, competition, and target market demographics; analyze the impact of potential changes to critical business metrics like sales volumes, customer loyalty, and profitability; and perform business-critical calculations such as market-basket analysis, customer segmentation, pricing ptimization and fraud detection (www. pentaho. com). Weka (Waikato Environment for Knowledge Analysis) is an ensemble of data mining algorithms written in Java. These algorithms can either be applied directly to a dataset using the Weka explorer or called from your own modified Java code. It contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization and can be used to develop new machine learning schemes (NA, Weka, 2010). The main point of integration between WEKA and the Pentaho platform is with Pentaho Data Integration PDI), also known as the Kettle proJect4. PDI is a streaming, engine driven ETL tool. Its rich set of extract and transform operations, combined with support for a large variety of databases, are a natural complement to WEKAS data filters. PDI can easily export data sets in WEKAS native ARFF format to be used immediately for model creation (Hall, M. , Frank, E. , Holmes, G. , Pfahringer, B. , Reutemann, P. , Willten, nd). Vendor Choice The suggested data mining vendor choice is Weka (Pentaho). This would be a great vendor to use tor our organization because ot what it otters in i s sottware. It offers an extensive amount of features and we can expect that any data sets used we will receive a thorough and extensive mining process that will assist the business in looking at customer loyalty, items being used by customer, customer concerns, along with monetary values. All features of business intelligence will be supported including web services, workflow integration, security, auditing, scheduling, navigation, portal integration, workbench-based designer and administration tools Oira. pentaho. com). Business Analytics Summary and Vendor Selection Companies are increasingly delivering value through business analytics (BA), which includes the people, processes and technologies that turn data into the insights that drive business decision and actions. Organizations with enterprise BA capabilities establish a sound foundation of high-quality, usable and integrated data. Business users identify insights from the data, make decisions and solve important business problems, thereby triggering actions that generate a wide range of tangible and intangible business value (Wixom, Yen, Relich, 2013, p. 11). Some vendors that utilize business analytics are SAS and Oracle. Both vendors supply a product that provides businesses with valuable tools that will assist them with queries, reporting and advanced analytics software. Business analytics is the most advanced component of business intelligence. Having an analytic capability will enable fact- based decisions using quantitative models. These models will ultimately draw on statistical and quantitative analysis of large data repositories. An analytic capability is especially critical in healthcare because lives are at stake and there is intense pressure to reduce costs and improve efficiency (Ghost Scott, 2011). An analytic apability drives fact-based management decisions and actions with extensive use of data, statistical and quantitative analysis, explanatory and predictive models [Davenport and Harris, 2007]. Success with advanced analytics is highly dependent on the quality and completeness of the data subject to analysis, as well as the sophistication of the algorithms and models on which analyses depend [Adams et al. 2010]. The availability of high quality data and technology needs to be coupled with organizational routines and individual skills for an analytic capability (Shanks et al. , 2010; Ghost Scott, 2011). There are some key components to todays business analytics solutions they are data and text mining, Data visualization, Forecasting, Operations Research and Analysis, Quality improvement, and Statist ical analysis. Data and text mining is one that e