CISB594 – Business Intelligence

1. CISB594 – Business Intelligence Data Warehousing Part II

2. Reference • Materials used in this presentation are extracted mainly from the following texts, unless stated otherwise.

3. Objectives At the end of this lecture, you should be able to: • Describe the processes used in developing and managing data warehouses • Explain data integration and the extraction, transformation, and load (ETL) processes • Describe real-time (active) data warehousing • Understand data warehouse administration and security issues CISB594 – Business Intelligence

4. Data Integration and the Extraction, Transformation, and Load (ETL) Process Data integrationis an umbrella term that covers three processes which combine to move data from multiple sources into a data warehouse: 1. accessing the data from any data source, 2. combining different views of the data across multiple data source, 3.capturing changes to the data in the data source .

5. Data Integration and the Extraction, Transformation, and Load (ETL) Process Extraction, transformation, and load (ETL) technologies • Fundamental, a DW could not exist without ETL • The ETL process consists of • extraction (reading data from one or more databases), • transformation (converting the extracted data from its previous form into the form in which it needs to be so that it can be placed into a data warehouse or simply another database) • load (putting the data into the data warehouse) • The ETL process also contributes to the quality of the data in a DW, its purpose is to load the data warehouse with integrated and cleansed data

6. Data Integration and the Extraction, Transformation, and Load (ETL) Process The process can either be done through purchasing data transformation tools or through developing the tools using programming languages.

7. Data Transformation Tools – To purchase or to build? • Issues affect whether an organization will purchase data transformation tools or build the transformation process itself • Data transformation tools are expensive • Data transformation tools may have a long learning curve • It is difficult to measure how the IT organization is doing until it has learned to use the data transformation tools • Not an easy decision ! However many believe purchasing the tools should be able to simplify the maintenance of the data warehouse

8. Data Transformation Tools – To purchase or to build? • Important criteria to consider in selecting an ETL tool • Ability to read from and write to an unlimited number of data source architectures • Automatic capturing and delivery of metadata • A history of conforming to open standards • An easy-to-use interface for the developer and the functional user • Various providers – Microsoft, Oracle, IBM etc

9. Data Warehouse Development • Choosing the vendors • Six guidelines to consider a vendor Financial strength Qualified consultants Market share Industry experience Established partnerships • - These indicate that a vendor is likely to be in business for the long term, to have the support capabilities its customers need, and to provide products that interoperate with other products the potential user has or may obtain.

10. Data Warehouse Development • A data warehousing project is a major undertaking • MORE COMPLICATED AS IT COMPRISES AND INFLUENCES MANY DEPARTMENTS, INPUT OUTPUT INTERFACES AND CAN BE PART OF BUSINESS STRATEGY • Data warehouse development approaches • Inmon Model: EDW approach • Kimball Model: Data mart approach • Which model is best? • There is no one-size-fits-all strategy to data warehousing • It depends on the need and the capacity of the organization • For many organizations, data mart approach is a convenient first step in implementing DW

11. Data Warehouse Development Describe the major similarities and differences between the Inmon and Kimball data warehouse development approaches. • Similarities: Both methods can produce an enterprise data warehouse and subset data marts. • Differences: Inmon’s approach starts with an enterprise data warehouse, creating data marts as subsets of that EDW if appropriate. The focus is on proven, traditional methods and technologies. Kimball’s starts with data marts, consolidating them into an EDW later if appropriate. It focuses in creating a useful end-user capability quickly.

12. Data Warehouse Development

13. Data Warehouse Development • Data warehousing implementation issues • Implementing a data warehouse is generally a massive effort that must be planned and executed according to established methods • There are many facets to the project lifecycle, and no single person can be an expert in each area

14. Establishment of service-level agreements and data-refresh requirements Identification of data sources and their governance policies Data quality planning Data model design ETL tool selection 6. Relational database software and platform selection 7. ETL design 8. Purge and archive planning 9. End-user support Data Warehouse Development Some tasks that could be performed in parallel for successful implementation of a data warehouse (Solomon, 2005) :

15. Data Warehouse Development • Some best practices for implementing a data warehouse (Weir, 2002): • Project must fit with corporate strategy and business objectives • There must be complete buy-in to the project by executives, managers, and users • It is important to manage user expectations about the completed project • The data warehouse must be built incrementally • Build in adaptability

16. Data Warehouse Development • Some best practices for implementing a data warehouse (Weir, 2002): • The project must be managed by both IT and business professionals • Develop a business/supplier relationship • Only load data that have been cleansed and are of a quality understood by the organization • Do not overlook training requirements • Be politically aware

17. Data Warehouse Development • Failure factors in data warehouse projects: • Cultural issues being ignored • Inappropriate architecture • Unclear business objectives • Missing information • Unrealistic expectations • Low data quality

18. Data Warehouse Development • Issues to consider to build a successful data warehouse: • Starting with the wrong sponsorship chain • Setting expectations that you cannot meet and frustrating executives at the moment of truth • Engaging in politically naive behavior • Loading the warehouse with information just because it is available

19. Data Warehouse Development • Issues to consider to build a successful data warehouse: • Believing that data warehousing database design is the same as transactional database design • Choosing a data warehouse manager who is technology oriented rather than user oriented • Focusing on traditional internal record-oriented data and ignoring the value of external data and of text, images, and, perhaps, sound and video

20. Data Warehouse Development • Implementation factors that can be categorized into three criteria • Organizational issues • Project issues • Technical issues • User participation in the development of data and access modeling is a critical success factor in data warehouse development

21. Data Warehouse Development • Other factor to consider - Massive data warehouses and scalability • The main issues pertaining to scalability: • The amount of data in the warehouse • How quickly the warehouse is expected to grow • The number of concurrent users • The complexity of user queries • Good scalability means that queries and other data-access functions will grow linearly with the size of the warehouse

22. Real-Time Data Warehousing • Traditionally, a data warehouse are not business critical, data are commonly updated on a weekly basis – not allowing for responding to transactions in near real time • Today, organizations are facing the need for real-time data warehousing, as decision support has become operational. • The emergence of real-time data warehousing (RDW) or active data warehousing (ADW) • The process of loading and providing data via a data warehouse as they become available

23. Real-Time Data Warehousing • The need for real-time data • A business often cannot afford to wait a whole day for its operational data to load into the data warehouse for analysis • Provides incremental real-time data showing every state change and almost analogous patterns over time • Less costly to develop, maintain, and secure one huge data warehouse so that data are centralized for BI/BA tools • Real-time data collection can reduce or eliminate the nightly batch processes

24. Data Warehouse Administration and Security Issues • Due to its huge size and complicated nature, DW requires strong monitoring and administrating • Needs more than a DBA • Needs data warehouse administrator (DWA) A person responsible for the administration and management of a data warehouse

25. Data Warehouse Administration and Security Issues What skills should a DWA possess? Why? • Familiarity with high-performance hardware, software and networking technologies, since a data warehouse is based on those • Solid business insight, to understand the purpose of the DW and its business justification • Familiarity with business decision, making processes to understand how the DW will be used • Excellent communication skills, to communicate with the rest of the organization

26. Data Warehouse Administration and Security Issues Effective security in a data warehouse should focus on four main areas: 1. Establishing effective corporate and security policies and procedures. An effective security policy should start at the top and be communicated to everyone in the organization. 2. Implementing logical security procedures and techniques to restrict access. This includes user authentication, access controls, and encryption. 3. Limiting physical access to the data center environment. 4. Establishing an effective internal control review process for security and privacy

27. Now ask if .. You are able to: • Describe the processes used in developing and managing data warehouses • Explain data integration and the extraction, transformation, and load (ETL) processes • Describe real-time (active) data warehousing • Understand data warehouse administration and security issues CISB594 – Business Intelligence