Metadata is data about data and Metadata tools are used for gathering, storing, updating, and for retrieving the business and technical metadata of an organization.
Metadata is data about data. Metadata comes into picture when we need to know about how data is stored and where it is stored. Metadata tools are helpful in capturing business metadata and the following section explains business metadata.
Technical metadata describes information about technology such as the ownership of the database, physical characteristics of a database (in oracle, table space, extents, segments, blocks, partitions etc), performance tuning (processors, indexing), table name, column name, data type, relationship between the tables, constraints, abbreviations, derivation rules, glossary, data dictionary, etc., and is used by the technical team. In Technical metadata, derivation rules are important when formulae or calculations are applied on a column.
Metadata & ETL
When you deal with a data warehouse, various phases like Business Process Modeling, Data Modeling, ETL, Reporting etc., are inter-related with each other and they do contain their own metadata. For example in ETL, it will be very difficult for one to extract, transform and load source data into a data warehouse, if there is no metadata available for the source like where and how to get the source data.
Let us explain the role of metadata in the ETL process with the help of an example table shown below which contains information about an organisation's employees.
Metadata stored in a repository can be produced in the form of reports for easy understanding and these reports are very useful in explaining about the various objects or data structures and the relationship between these objects. The following products like Schema Logic Enterprise Suite, Rochade, Metatrieve, Datamapper, Metacenter, Metadata Integration Frame work stores and handles metadata in an efficient and effective manner.
Business Intelligence is a technology based on customer and profit oriented models that reduces operating costs and provide increased profitability by improving productivity, sales, service and helps to make decision making capabilities at no time. Business Intelligence Models are based on multi dimensional analysis and key performance indicators (KPI) of an enterprise.
Business Intelligence Tools help to gather, store, access and analyze corporate data to aid in decision-making. Generally these systems will illustrate business intelligence in the areas of customer profiling, customer support, market research, market segmentation, product profitability, statistical analysis, inventory and distribution analysis.
OLAP & Hybrids
OLAP, an acronym for Online Analytical Processing is an approach that helps organization to take advantages of DATA. Popular OLAP tools are Cognos, Business Objects, Micro Strategy etc. OLAP cubes provide the insight into data and helps the topmost executives of an organization to take decisions in an efficient manner.
Imagine an organization that manufactures and sells goods in several States of USA which employs hundreds of employees in its manufacturing, sales and marketing division etc. In order to manufacture and sell this product in profitable manner, the executives need to analyse(OLAP analysis) the data on the product and think about various possibilities and causes for a particular event like loss in sales, less productivity or increase in sales over a particular period of the year.
OLAP Database - Multidimensional
This is a type of database that is optimized for data warehouse, data mart and online analytical processing (OLAP) applications. The main advantage of this database is query performance.
Key Performance Indicators
Key Performance Indicators, commonly referred to as KPIs, are a list of measurements that are identified as critical factors in achieving the organizational goals or mission. KPIs are often identified in a business to help them drive a business towards its success and are associated with a number of business activities like Customer Relationship Management(CRM), Supply Chain Analytics or any other activity that is happening within the organization.
A Business Intelligence Dashboard visually represents the key organizational performance data in a near real time, user friendly manner that can be understood instantaneously. Technically speaking, a Dashboard is a visual representation that reflects the Key Performance Indicators(KPIs) of interest for managerial review and not only that it enables them to drill-down further. Business Intelligence Dashboard is similar in function to a car dashboard in that it displays and provides access to the powerful analytical systems and key performance metrics in a form enabling business executives to analyze trends and more effectively manage their areas of responsibility.
Scorecards are similar to Dashboards in a way that it provides easy-to-understand, summarized, at-a-glance data for the managers and top officials to tell them about their company's present and past performance. Scorecards thus help to monitor the Key Performance Indicators accurately and to communicate the goals and strategies across the organization in an efficient and elegant manner. In a Business Intelligence environment, Scorecards allows managers to set metrics or targets and monitor them to see their impact on every department
What is Data Mining?
Data Mining is a set of processes related to analyzing and discovering useful, actionable knowledge buried deep beneath large volumes of data stores or data sets. This knowledge discovery involves finding patterns or behaviors within the data that lead to some profitable business action. Data Mining requires generally large volumes of data including history data as well as current data to explore the knowledge. Once the required amount of data has been accumulated from various sources, it is cleaned, validated and prepared for storing it in the data warehouse or data mart. BI reporting Tools capture the required facts from these data to be used by the knowledge discovery process. Data Mining can be accomplished by utilizing one or more of the traditional knowledge discovery techniques like Market Basket Analysis, Clustering, Memory Based Reasoning, Link Analysis, Neural Networks and so on.