BI ppt.ppt

CHAPTER-03 Business Intelligence  Definition of Business Intelligence  Challenges in building Business Intelligence  Consolidating Data from Multiple Sources  ETL  Components of Data Warehouse  Identifying Elements to Support Analysis  Advantages of Data Warehousin...

CHAPTER-03 Business Intelligence  Definition of Business Intelligence  Challenges in building Business Intelligence  Consolidating Data from Multiple Sources  ETL  Components of Data Warehouse  Identifying Elements to Support Analysis  Advantages of Data Warehousing  Business Intelligence Products  Business Intelligence (BI)  “The processes, technologies and tools needed to turn data into information and information into knowledge and knowledge into plans that drive profitable business action. BI encompasses data warehousing, business analytics and knowledge management. The Challenges of Building BI Solutions  Data exists in multiple places  Data is not formatted to support complex analysis  Different kinds of workers have different data needs  What data should be examined and in what detail  How will users interact with that data Consolidation of Data  The process of consolidating data means moving it, making it consistent, and cleaning up the data as much as possible Extraction, Transformation, and Loading (ETL)  The process of data consolidation is often called Extraction, Transformation, and Loading (ETL)  The ETL process extracts data from the various source systems  Data is then transformed to make it consistent and improve data quality  The consolidated, consistent, and cleaned data is then loaded into a data repository  Developing the ETL process often consumes 80% of the development time Extraction, Transformation, and Loading (ETL) Tools  Some ETL Tools  Oracle Data Integrator (ODI)  Informatica  IBM Ascential  Abinitio Business Issues with Data Consolidation  Business users must drive what should be in the data warehouse  Someone in the business must decide how to consolidate inconsistent data  The business must decide how to handle other necessary items - such as currency conversions  The Users of Business Intelligence Executives and business decision makers: Look at the business from a high level, performing limited analysis  Analysts: Perform complex, detailed data analysis  Information workers Need static reports or limited analytic power  Line workers: need no analytic capabilities as BI is presented to them as part of their job The Components of a Data Warehouse  There are several items that make up a data warehouse  Cubes  Measures  Key Performance Indicators  Dimensions  Attributes  Hierarchies Asking a BI Question  Humans tend to think in a multidimensional way, even if they don’t realize it  We often want to see a particular value in a certain context  Show me sales by month by product for North America  “What” you want to see (sales in this case) is called a measure  How you want to see it (month, product, and North America) is called a dimension  Cubes  Cubes are the structures in which data is stored  Users access data in the cubes by navigating through various dimensions Measures  Measures are what you want to see  They are almost always numeric  They are often additive  Dollar sales, unit sales, profit, expenses, and more KPI (Key Indicator Performance) Inventory Accuracy Efficiency of receiving Picking and packing cost Inventory turnover Customer cycle order time Dimensions  Dimensions are how you want to see the data  You usually want to see data by time, geography, product, account, employee, …  Dimensions are made up of attributes and may or may not include hierarchies  Year – Semester – Quarter – Month – Day  Product Category – Product Subcategory - Product Attributes  Attributes are individual values that make up dimensions  A Time dimension may have a Month attribute, a Year attribute, and so forth  A Geography dimension may have a Country attribute, a Region attribute, a City attribute, and so on  A Product dimension may have a Part Number attribute, a size attribute, a color attribute, a manufacturer attribute, and more Hierarchies  You can put attributes into a hierarchical structure to assist user analysis  One of the most common functions in BI is to “drill down” to a more detailed level  For example, Time hierarchy might be to go from Year to Quarter to Month to Day  Another Time hierarchy might go from Year to Month to Week to Day to Hour Summary  The ETL process extracts data from source systems, transforms it and then loads it to a data warehouse or a data mart.  Using reports and dashboards, BI looks at data as a collection of measures and KPIs viewed by dimensions. Advantages of Data Warehousing Consistent, accurate, and up-to-date data is maintained. Provides complete and comprehensive view of data, making it easier for businesses to see trends, patterns, and opportunities and enhance overall decision-making. Quicker information access to make informed decisions immediately. It lowers the cost of data analysis.  Business Intelligence Products  SAP business objects  Oracle Business Intelligence  Microstrategy  SAS Business Intelligence  Zoho Analytics  Microsoft Power BI