Business Intelligence


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Business Intelligence

  2. 2. 2 ACKNOWLEDGEMENT We present before you our ISAS Project on BUSINESS INTELLIGENCE. I would like to thank my teacher for giving the idea about the project. I would like to thank my parents for their physical and financial help and support. I would definitely include our sources of information – internet and various books. Without whom we could never complete this Project.
  3. 3. 3 TABLE OF CONTENTS: Definition of Business Intelligence………………..P-4 Terms that enables Business Intelligence: Data Mining…………………………………………P-5-6 Definition What is data warehouse? What can data mining do? Importance of data mining. Data Warehouse…………………………………..P-7-8 Definition Data warehouse models. Advantages and disadvantages. Data Analysis…………………………………….P-9-12 Definition The process of data analysis  Data cleaning  Running Analysis  Presenting Result  Protecting Files Types of data analysis  Reductive Analysis  Mathematical Analysis  Protecting Files Conclusion………………………………………….P-12
  4. 4. 4 BUSINESS INTELLIGENCE Definition: Business intelligence is the processes, technologies & tools that help us change data into information, information into knowledge and knowledge into plans that guide organization. Technologies for gathering, storing, analyzing & providing access to data to help enterprise users make better business decisions. BI technologies provide historical, current, and predictive views of business operations. Common functions of business intelligence technologies are data reporting, data mining, data warehouse, data analysis, business performance management, benchmarking, text mining, and predictive analytics. By extension, “business intelligence” may refer to the collected information itself of the explicit knowledge developed from the information.
  5. 5. 5 DATA MINING Definition: Data mining is “the extraction of hidden predictive information from large database”. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational database. Every company or organization needs data mining services to increase their profitability. Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvement with data access. Data mining is a powerful new technology with great potential to help companies focus on the most important information in their data warehouse. What is data warehouse? Data warehouse focuses on data storage. However, the means to retrieve and analyze data, to extract, transform and load data, and to
  6. 6. 6 manage the data dictionary are also considered essential components of a data warehouse system. What can data mining do? Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Importance of data mining: Data mining is primarily used today by companies with a strong customer focus – retail, financial, communication and marketing organizations. Data mining is having lot of importance because of its huge applicability. It is being used increasingly in business applications for understanding and then predicting valuable data, like consumer buying actions and buying tendency, profiles of customers, industry analysis, etc. Data Mining is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, e-commerce, customer relationship management and financial services.
  7. 7. 7 DATA WAREHOUSE Definition: A data warehouse is a place where data is stored for archival, analysis and security purposes. Usually a data warehouse is either a single computer or many computers (servers) tied together to create one giant computer system. Data can consist of raw data or formatted data. It can be on various types of topics including organization's sales, salaries, operational data, summaries of data including reports, copies of data, human resource data, inventory data, external data to provide simulations and analysis, etc. Data warehouse models: There are many different models of data warehouses. Online Transaction Processing, which is a data warehouse model, is built for speed and ease of use. Another type of data warehouse model is called Online Analytical processing, which is more difficult to use and adds an extra step of analysis within the data. Usually it requires more steps which slows the process down and requires much more data in order to analyze certain queries. One of the more common data warehouse models include a data warehouse
  8. 8. 8 Advantages and disadvantages of data warehouse: Advantages: - The number one reason why user should implement a data warehouse is so that employees can access the data warehouse and use the data for reports, analysis and decision making. Using the data in a warehouse can help user locate trends, focus on relationships and help user understand more about the environment that your business operates in. Data warehouses also increase the consistency of the data and allow it to be checked over and over to determine how relevant it is. Because most data warehouses are integrated, user can pull data from many different areas of business, for instance human resources, finance, IT, accounting, etc. Disadvantages: - While there are plenty of reasons why you should have a data warehouse, it should be noted that there are a few negatives of having a data warehouse including the fact that it is time consuming to create and to keep operating. User might also have a problem with current systems being incompatible with user’s data.
  9. 9. 9 Data Analysis Definition: Data Analysis is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. Data mining descriptive statistics is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide data analysis into descriptive statistics exploratory data analysis (EDA) and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical or structural models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.
  10. 10. 10 Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization, which is unrelated to the subject of this article. The Process of Data Analysis: Data analysis is a process, within which several phases can be distinguished: - 1. Data cleaning. 2. Running Analysis. 3. Presenting Results. 4. Protecting Files. Data Cleaning Before substantive analysis begins, we need to verify that out data are accurate and that the variables are well named and property labeled. That is, we clean the data. First we must bring our data into state. If received the data in state format, this is as simple as a single use command. If the data arrived in another format, we need to verify that they were imported correctly into state. We should also evaluate the variables name and labels. Awkward names make it more difficult to analyze the data and can lead to mistakes. Likewise, incomplete or poorly designed labels make the output difficult to read and lead to mistakes. Next we verify that the sample and variables are what they should be.
  11. 11. 11 Running Analysis Once the data are cleaned, fitting out models and computing the graphs and tables for our paper or book are often the simplest part of the workflow. Indeed, this part of the book is relatively short. Although I do not discuss specific types of analysis, later I talk about ways to ensure the accuracy of our result, to facilitate later replication, and to keep track out do files, data files, and log files regardless of the statistical methods we are using. Presenting Result Once the analyses are complete, we want to present them. I consider several issues in the workflow of the presentation. An efficient workflow can automate much of this work. Second, we need to document the provenance of all findings that we present. If our presentation does not preserve the source of our results, it can be very difficult to track them down later. Finally, there are number of simple things that we can do to make our presentation more effective. Protecting Files When we are cleaning our data, running analyses and writing, we need to protect our files to prevent loss due to hardware failure, file corruption, or unintentional deletions. There are number of simple things we can do to make it easier to routinely save our work. With backup software readily available and the cost of disk storage so cheap, the hardest parts of making backups are keeping track of what we have.
  12. 12. 12 Types of Data Analysis Reductive Analysis It is a methodology in which individual facts, or aggregations of those facts, are used as the basis for analysis. The methodology can be argued not to be analysis at all. When used alone, it results in a simple reduction of the data set to one or more statistics which often, do not adequately represent the underlying phenomena. Mathematical Analysis It sometimes referred to as classical, data analysis is a methodology in which mathematical models are applied to the data and used as the basis for analysis. Mathematical modeling is an important technique in the study of data because it lets us reduce unmanageable masses of data to models that can be used to make prediction about the underlying phenomena and understand such attributes of the data as normality and linearity. Visual Analysis It is a methodology in which the data, as a whole, is used as the basis for analysis. The data is presented visually and any modeling that occurs is done as a result of the analysis of those visuals. It is especially powerful because it matches our natural abilities to interpret data holistically and exposes attributes of the data, which can be hidden in models.