Data warehousing

DATA WAREHOUSING

•      Data Warehouse is the extension of database

•      Data   warehouse   is   the   main   repository   of   c

•      Data in the data warehouse are processed (i.e., EFL) therefore is more integrated and consistent .

•      While the information in the database tends to be real-time, the information in the data warehouse can be updated regularly.

•      While database focuses on automating the process of collecting and customers information, data warehouse looks more at assisting managers in performing more advanced analysis and thus making better decisions.

What is Data warehousing?

A series of analytical tools works with data stored in databases to find patterns and insights for helping managers and employees make better decisions to improve organizational performance.

1.DATA MARTS

•      DATA MARTS

•      Companies often build enterprise-wide data warehouses, where a central data warehouse serves the entire organization, or they create smaller, decentralized warehouses called data marts.

•      A data mart is a subset of a data warehouse in which a summarized or highly focused portion of the customers data is placed in a separate database for a specific population of users.

•      For example, a company might develop marketing and sales data marts to deal with customer info.

•      A data mart typically focuses on a single subject area or line of business, so it usually can be constructed more rapidly and at lower cost than an enterprise-wide data warehouse.

•      However, complexity, costs, and management problems will rise if an organization creates too many data marts.

•      Sourcing, Acquisition, Cleanup and Transformation Tools

•      The functionality includes:

– Removing unwanted data from operational databases

– Converting to common data names and definitions

– Calculating summaries and derived data

– Establishing defaults for missing data

– Accommodating source data definition changes

•      The data sourcing, cleanup, extract, transformation and migration tools have to deal with some significant issues, as follows:

– Database heterogeneity.

– Data heterogeneity

•      Metadata

•      Metadata is data about data that describes the data warehouse.

•      It is used for building, maintaining, managing, and using the data warehouse.

•      Metadata can be classified into the following:

– Technical metadata

– Business metadata

– Data warehouse operational information such as data history (snapshots, versions), ownership, extract audit trail, usage data

•      The non-trivial extraction of novel, implicit, and actionable knowledge from large datasets.

– Extremely large datasets

– Discovery of the non-obvious

– Useful knowledge that can improve processes

– Cannot be done manually

•      Technology to enable data exploration, data analysis, and data visualization of very large databases at a high level of abstraction, without a specific hypothesis in mind.

•      Sophisticated data search capability that uses statistical algorithms to discover patterns and correlations in data.

•      Data Mining is a step of Knowledge Discovery in Databases (KDD) Process

– Data Warehousing

– Data Selection

– Data Preprocessing

– Data Transformation

– Data Mining

– Interpretation/Evaluation

•      Data Mining is sometimes referred to as KDD and DM and KDD tend to be used as synonyms

•      Data Mining Evaluation

•      Data   Mining   is   Not   …

•      Data warehousing

•      SQL / Ad Hoc Queries / Reporting

•      Software Agents

•      Online Analytical Processing (OLAP)

•      Data Visualization

•      Data Mining Applications

 2.DATA MINING IN CRM:

•      Customer Life Cycle

– The stages in the relationship between a customer and a business

•      Key stages in the customer lifecycle

– Prospects: people who are not yet customers but are in the target market

– Responders: prospects who show an interest in a product or service

– Active Customers: people who are currently using the product or service

– Former Customers: may be ―bad‖ customers who did incurred high costs

•       It‘s important to know life cycle events (e.g. retirement)

•      Customer Life Cycle

•      What marketers want: Increasing customer revenue and customer profitability

– Up-sell

– Cross-sell

– Keeping the customers for a longer period of time

•      Solution: Applying data mining

•      DM helps to

– Determine the behavior surrounding a particular lifecycle event

– Find other people in similar life stages and determine which customers are following similar behavior patterns