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Business Intelligence Critique Essay

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A Seminar Report on BUSINESS INTELLIGENCE Prepared by: Guided By: Arpan Solanki Prof.Yagnik A.Rathod 100410107063 Assistant professor TY C.

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E SVIT-VASAD Certificate Date: /11/12 This is to certify that Mr.

Arpan Solanki ID No: 10- CEG-66En No. 100410107063 of programme Computer Engineering [Third Year,5th Semester] has satisfactorily completed his term work in course Seminar [150705] for the term ending in November,2012. Staff in-charge: Head of Department: Mr Yagnik A. Rathod Mrs Bijal Talati Asst. Professor HOD Computer EngineeringComputer Engineering C. E. DepartmentC. E. Department SARDAR VALLABHBHAI PATEL INSTITUTE OF TECHNOLOGY VASAD-388306, GUJARAT–INDIA ACKNOWLEDGEMENT

Every work owes its success to many people. Likewise, the successful completion of our Project Report could not have been possible without the co-ordination and support of our college SVIT. I am thankful to Mrs Bijal Talati (HOD of CE department) for his constant inspiration and valuable guidance which helped us to complete the Project satisfactorily. His inspirational remarks from time to time enabled us to complete the report in stipulated time period. He provides us needed help and facilities for carrying out test for our program.

I am thankful to Mr Yagnik Rathod for constantly inspiring us and providing us required details and help on regular intervals, which helped us to reach our goal on time. I am also thankful to the whole Computer Department for their unbounded cooperation and support. ABSTRACT Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes.

BI technologies provide historical, current, and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, text mining, and predictive analytics. Business intelligence aims to support better business decision-making. Thus a BI system can be called a decision support system (DSS).

Though the term business intelligence is sometimes used as a synonym for competitive intelligence, because they both support decision making, BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors. INDEX CONTENTS PAGE NO. 1.

Defination………………………………………………………………………………………………. 1 2. History…………………………………………………………………………………………………… 2 3. Business Intelligence and Data Warehousing……………………………………………… 3 4. Business Intelligence Tools………………………………………………………………………. 4 Fig 4. Architecture Of BI…………………………………………………………………… 5. Success Factor Of Implemention………………………………………………………………. 6 5. 1 Business Sponsership……………………………………………………………………. 6 5. 2 Business Needs…………………………………………………………………………… 7 5. 3 Amount and Quality Of Availabel Data…………………………………………. 7 6. User Aspect……………………………………………………………………………………………. 7. Market Place………………………………………………………………………………………….. 11 7. 1 Industry-Specific………………………………………………………………………… 11 8. Semi-structured or Unstructured data……………………………………………………….. 12 8. 1 Semi-structured vs Unstructured data…………………………………………….. 12 8. 2 Problems With Semi-structured or Unstructured data………………………. 13 8. The Use Of Matadata………………………………………………………………….. 14 9. Uses and Examples BI……………………………………………………………………………. 15 9. 1 Which Type Of Company Use It?………………………………………………… 15 9. 2 Examples Of BI………………………………………………………………… ………. 15 10. Benifits and Disadvantages…………………………………………………………………… 16 10. Benifits……………………………………………………………………………………. 16 10. 2 Disadvantages………………………………………………………………………….. 16 11. Future………………………………………………………………………………………………… 17 12. Conclusion …… ………………………………………………………………………………….. 19 References………………………………………………………………………………………………. 0 1. DEFINATION Business intelligence (BI) is the ability of an organization to collect, maintain, and organize knowledge. This produces large amounts of information that can help develop new opportunities. Identifying these opportunities, and implementing an effective strategy, can provide a competitive market advantage and long-term stability. [ The goal of modern business intelligence deployments is to support better business decision-making. Thus a BI system can be called decesion support system(DSS).

Though the term business intelligence is sometimes a synonym for competative intelegence(because they both support decision making), BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors. If understood broadly, business intelligence can include the subset of competitive intelligence. BI is broad category of applications, which include the activities of • decision support systems query and reporting • online analytical processing (OLAP) • statistical analysis, forecasting, and data mining. 2. HISTORY In a 1958 article, IBM researcher Hans peter luhn used the term business intelligence. He defined intelligence as: “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal. ” Business intelligence as it is understood today is said to have evolved from the decision support systems that began in the 1960s and developed throughout the mid-1980s.

DSS originated in the computer-aided models created to assist with decesion making and planning. From DSS, data warehouses, Executive information system, OLAP and business intelligence came into focus beginning in the late 80s. In 1989, Howard Dresner proposed “business intelligence” as an umbrella term to describe “concepts and methods to improve business decision making by using fact-based support systems. ” It was not until the late 1990s that this usage was widespread. 3. BUSINESS INTELLIGENCE AND DATA WAREHOUSING Often BI applications use data gathered from a data ware house or data mart.

However, not all data warehouses are used for business intelligence, nor do all business intelligence applications require a data warehouse. To distinguish between the concepts of business intelligence and data warehouses, Research often defines business intelligence in one of two ways: Using a broad defination: “Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making. When using this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master data management, text and content analytics, and many others that the market sometimes lumps into the information management segment. Therefore, Forrester refers to data preparation and data usage as two separate, but closely linked segments of the business intelligence architectural stack. Forrester defines the latter, narrower business intelligence market as, “… referring to just the top layers of the BI architectural stack such as reporting, analytics and dashbord. 4. BUSINESS INTELLIGENCE TOOLS Operational Data Source: Business Intelligence system collects data from various sources including operation database, ERP, legacy apps, external database and etc.

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ETL tools (Extract, Transform, Load) are used to pull data from source database, transform the data so that it is compatible with the data warehouse and then load it into data warehouse. A Data Warehouse is a “Subject-Oriented, Integrated, Time-Variant, Nonvolatile collection of data in support of decision making”.

Data Warehouses tend to have these distinguishing features: (1) Use a subject oriented dimensional data model; (2) Contain publishable data from potentially multiple sources and; (3) Contain integrated reporting tools. A data mart is a repository of data gathered from operational data and other sources that is designed to serve a particular community of knowledge workers. The data may derive from an enterprise-wide database or data warehouse or be more specialized. A data mart is a repository of data gathered from operational data and other sources that is designed to serve a particular community of knowledge workers.

The data may derive from an enterprise-wide database or data warehouse or be more specialized. Literally, On-Line Analytical Processing. Designates a category of applications and technologies that allow the collection, storage, manipulation and reproduction of multidimensional data, with the goal of analysis. * A pivot table is a great reporting tool that allows for “slicing and dicing” data. * REPORT: It gives brief report about output Business Intelligence ETL tools Data Warehouse Marketing Data Mart Finance Data Mart Distribution Data Mart BI OLAP Reports

Pivot Table Data gethered FIG 4. ARCHITECTURE OF BI 5. SUCCESS FACTOR OF IMPLEMENTATION Before implementing a BI solution, it is worth taking different factors into consideration before proceeding. According to Kimball et al. , these are the three critical areas that you need to assess within your organization before getting ready to do a BI project: 1. The level of commitment and sponsorship of the project from senior management 2. The level of business need for creating a BI implementation 3. The amount and quality of business data available . 1: BUSINESS SPONSERSHIP The commitment and sponsorship of senior management is according to Kimball et al. , the most important criteria for assessment. This is because having strong management backing helps overcome shortcomings elsewhere in the project. However, as Kimball et al. state: “even the most elegantly designed DW/BI system cannot overcome a lack of business [management] sponsorship”. It is important that management personnel who participate in the project have a vision and an idea of the benefits and drawbacks of implementing a BI system.

The best business sponsor should have organizational clout and should be well connected within the organization. It is ideal that the business sponsor is demanding but also able to be realistic and supportive if the implementation runs into delays or drawbacks. The management sponsor also needs to be able to assume accountability and to take responsibility for failures and setbacks on the project. Support from multiple members of the management ensures the project does not fail if one person leaves the steering group.

However, having many managers work together on the project can also mean that there are several different interests that attempt to pull the project in different directions, such as if different departments want to put more emphasis on their usage. This issue can be countered by an early and specific analysis of the business areas that benefit the most from the implementation. All stakeholders in project should participate in this analysis in order for them to feel ownership of the project and to find common ground.

Another management problem that should be encountered before start of implementation is if the business sponsor is overly aggressive. If the management individual gets carried away by the possibilities of using BI and starts wanting the DW or BI implementation to include several different sets of data that were not included in the original planning phase. However, since extra implementations of extra data may add many months to the original plan, it’s wise to make sure the person from management is aware of his actions. 5. 2: BUSINESS NEEDS

Because of the close relationship with senior management, another critical thing that must be assessed before the project begins is whether or not there is a business need and whether there is a clear business benefit by doing the implementation. [15] The needs and benefits of the implementation are sometimes driven by competition and the need to gain an advantage in the market. Another reason for a business-driven approach to implementation of BI is the acquisition of other organizations that enlarge the original organization it can sometimes be beneficial to implement DW or BI in order to create more oversight.

Companies that implement BI are often large, multinational organizations with diverse subsidiaries. A well-designed BI solution provides a consolidated view of key business data not available anywhere else in the organization, giving management visibility and control over measures that otherwise would not exist. 5. 3: AMOUNT AND QUALITY OF AVAILABLE DATA Without good data, it does not matter how good the management sponsorship or business-driven motivation is. Without proper data, or with too little quality data, any BI implementation fails. Before implementation it is a ood idea to do data profiling. This analysis identifies the “content, consistency and structure ”of the data. This should be done as early as possible in the process and if the analysis shows that data is lacking, put the project on the shelf temporarily while the IT department figures out how to properly collect data. When planning for business data and business intelligence requirements, it is always advisable to consider specific scenarios that apply to a particular organization, and then select the business intelligence features best suited for the scenario.

Often, scenarios revolve around distinct business processes, each built on one or more data sources. These sources are used by features that present that data as information to knowledge workers, who subsequently act on that information. The business needs of the organization for each business process adopted correspond to the essential steps of business intelligence. These essential steps of business intelligence includes but not limited to: 1. Go through business data sources in order to collect needed data 2.

Convert business data to information and present appropriately 3. Query and analyze data 4. Act on those data collected 6. USER ASPECT Some considerations must be made in order to successfully integrate the usage of business intelligence systems in a company. Ultimately the BI system must be accepted and utilized by the users in order for it to add value to the organization. If the usability of the system is poor, the users may become frustrated and spend a considerable amount of time figuring out how to use the system or may not be able to really use the system.

If the system does not add value to the users? mission, they simply don’t use it. To increase user acceptance of a BI system, it can be advisable to consult business users at an early stage of the DW/BI lifecycle, for example at the requirements gathering phase. This can provide an insight into the business process and what the users need from the BI system. There are several methods for gathering this information, such as questionnaires and interview sessions. When gathering the requirements from the business users, the local

IT department should also be consulted in order to determine to which degree it is possible to fulfill the business’s needs based on the available data. Taking on a user-centered approach throughout the design and development stage may further increase the chance of rapid user adoption of the BI system. Besides focusing on the user experience offered by the BI applications, it may also possibly motivate the users to utilize the system by adding an element of competition. Kimball suggests implementing a function on the business intellegence website where reports on system usage can be found.

By doing so, managers can see how well their departments are doing and compare themselves to others and this may spur them to encourage their staff to utilize the BI system even more. In a 2007 article, H. J. Watson gives an example of how the competitive element can act as an incentive. ] Watson describes how a large call centre implemented performance dashboards for all call agents, with monthly incentive bonuses tied to performance metrics. Also, agents could compare their performance to other team members. The implementation of this type of performance measurement and competition significantly improved agent performance.

BI chances of success can be improved by involving senior management to help make BI a part of the organizational culture, and by providing the users with necessary tools, training, and support. Training encourages more people to use the BI application. Providing user support is necessary to maintain the BI system and resolve user problems. User support can be incorporated in many ways, for example by creating a website. The website should contain great content and tools for finding the necessary information. Furthermore, helpdesk support can be used. The help desk can be manned by power users or the DW/BI project team. . MARKET PLACE There are a number of business intelligence vendors, often categorized into the remaining independent “pure-play” vendors and consolidated “megavendors” that have entered the market through a recent trend of acquisitions in the BI industry. Some companies adopting BI software decide to pick and choose from different product offerings (best-of-breed) rather than purchase one comprehensive integrated solution (full-service). 7. 1:INDUSTRY-SPECIFIC Specific considerations for business intelligence systems have to be taken in some sectors such as government, banking, hospitality, hotel chain.

The information collected by banking institutions and analyzed with BI software must be protected from some groups or individuals, while being fully available to other groups or individuals. Therefore BI solutions must be sensitive to those needs and be flexible enough to adapt to new regulations and changes to existing law. 8. SEMI-STRUCTURED OR UNSTRUCTURED DATA Businesses create a huge amount of valuable information in the form of e-mails, memos, notes from call-centers, news, user groups, chats, reports, web-pages, presentations, image-files, video-files, and marketing material and news.

According to Merrill Lynch, more than 85% of all business information exists in these forms. These information types are called either semi-structured or unstructured data. However, organizations often only use these documents once. The management of semi-structured data is recognized as a major unsolved problem in the information technology industry. According to projections from Gartner (2003), white collar workers spend anywhere from 30 to 40 percent of their time searching, finding and assessing unstructured data.

BI uses both structured and unstructured data, but the former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision making. Because of the difficulty of properly searching, finding and assessing unstructured or semi-structured data, organizations may not draw upon these vast reservoirs of information, which could influence a particular decision, task or project. This can ultimately lead to poorly informed decision making.

Therefore, when designing a business intelligence/DW-solution, the specific problems associated with semi-structured and unstructured data must be accommodated for as well as those for the structured data 8. 1: SEMI-STRUCTURED VS UNSTRUCTURED DATA Unstructured and semi-structured data have different meanings depending on their context. In the context of relational database systems, unstructured data cannot be stored in predictably ordered colums and rows. One type of unstructured data is typically stored in a BLOB(binary large object), a catch-all data type available in most relation database management systems.

Unstructured data may also refer to irregularly or randomly repeated column patterns that vary from row to row within each file or document. Many of these data types, however, like e-mails, word processing text files, PPTs, image-files, and video-files conform to a standard that offers the possibility of metadata. Metadata can include information such as author and time of creation, and this can be stored in a relational database. Therefore it may be more accurate to talk about this as semi-structured documents or data, but no specific consensus seems to have been reached.

Unstructured data can also simply be the knowledge that business users have about future business trends. Business forecasting naturally aligns with the BI system because business users think of their business in aggregate terms. Capturing the business knowledge that may only exist in the minds of business users provides some of the most important data points for a complete BI solution. 8. 2: PROBLEMS WITH SEMI-STRUCTURED OR UNSTRUCTURED DATA There are several challenges to developing BI with semi-structured data. According to Inmon ; Nesavich, some of those are: 1.

Physically accessing unstructured textual data – unstructured data is stored in a huge variety of formats. 2. Terminology– Among researchers and analysts, there is a need to develop a standardized terminology. 3. Volume of data – As stated earlier, up to 85% of all data exists as semi-structured data. Couple that with the need for word-to-word and semantic analysis. 4. Searchability of unstructured textual data – A simple search on some data, e. g. apple, results in links where there is a reference to that precise search term. (Inmon ; Nesavich, 2008)[25] gives an example: “a search is made on the term felony.

In a simple search, the term felony is used, and everywhere there is a reference to felony, a hit to an unstructured document is made. But a simple search is crude. It does not find references to crime, arson, murder, embezzlement, vehicular homicide, and such, even though these crimes are types of felonies. ” 8. 3: THE USE OF MATADATA To solve problems with searchability and assessment of data, it is necessary to know something about the content. This can be done by adding context through the use of metedataMany systems already capture some metadata (e. g. filename, author, size, etc. , but more useful would be metadata about the actual content – e. g. summaries, topics, people or companies mentioned. Two technologies designed for generating metadata about content are automatic catagorision and information extraction. 9. USES AND EXAMPLES OF BI 9. 1 WHICH TYPE OF COMPANY USE IT? * Hotel/restaurant chain. They use for prediction of menu,from that they know that which dishes customer wants regularly or ocasanaly,they know that which restaurant not working properly and which in lost so they will close that and they know that which restaurant in profit so they expand it. Food chain/Retail stores They use BI tool for better market place. they use it for better supply chain management and efficient transportation and warehousing. By this tool authority knows about stocks in warehouse, which product have good response at which shop so provide a better stock their. they know about various product stock by just clicking away not to check for it. Wall mart, Relience fresh use business intelligence. For better profit and selling. 9. 2 EXAMPLES OF BI : 1 . Microsoft business intelligence 2. pantaho 3. oracle business intelligence 10.

BENIFITS AND DISADVANTAGES 10. 1 BENIFITS 1. Continuous improvement of design making capabilities used to increase revenue ; reduce cost 2. Better tools for knowledge worker 3. Leverage the amount of captured transactions ; operation data 4. Multidimensional analysis 5. Ad-hoc status reporting ; what-if scenarios 6. Intuitive user interface 7. Customer behavior 8. Sales force analysis 9. Market ; customer penetration 10. Product ; service life cycle analysis 11. Budgeting ; planning 12. Business performance 13. Customer click stream information 4. Integration of traditional business ; e-business 15. HR performance evaluation 16. Compression analysis 17. Workforce planning ; optimization 10. 2 DISADVANTAGES : 1. Cost 2. Pilling of historical data 3. Complexity 4. Limited use 11. FUTURE A 2009 Gartner paper predicted these developments in the business intelligence market: * Because of lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies regularly fail to make insightful decisions about significant changes in their business and markets. By 2012, business units will control at least 40 percent of the total budget for business intelligence. * By 2012, one-third of analytic applications applied to business processes will be delivered through coarse graind application mashups. A 2009 Information Management special report predicted the top BI trends: “cloud computing, social networking, data visulization, mobili BI, predictive analitic, cloud compiting and multitouch. ” Other business intelligence trends include the following:

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