Data Warehouse Lutfi Freij Konstantin Rimarchuk Vasken Chamlaian John Sahakian Suzan Ton
Inmon • Father of the data warehouse • Co-creator of the Corporate Information Factory. • He has 35 years of experience in database technology management and data warehouse design.
Inmon-Cont’d • Bill has written about a variety of topics on the building, usage, & maintenance of the data warehouse & the Corporate Information Factory. • He has written more than 650 articles (Datamation, ComputerWorld, and Byte Magazine). • Inmon has published 45 books. • Many of books has been translated to Chinese, Dutch, French, German, Japanese, Korean, Portuguese, Russian, and Spanish.
Introduction • What is Data Warehouse? A data warehouse is a collection of integrated databases designed to support a DSS. • According to Inmon’s (father of data warehousing) definition(Inmon,1992a,p.5): • It is a collection of integrated, subject-oriented databases designed to support the DSS function, where each unit of data is non-volatile and relevant to some moment in time.
Introduction-Cont’d. • Where is it used? It is used for evaluating future strategy. • It needs a successful technician: • Flexible. • Team player. • Good balance of business and technical understanding.
Introduction-Cont’d. • The ultimate use of data warehouse is Mass Customization. • For example, it increased Capital One’s customers from 1 million to approximately 9 millions in 8 years. • Just like a muscle: DW increases in strength with active use. • With each new test and product, valuable information is added to the DW, allowing the analyst to learn from the success and failure of the past. • The key to survival: • Is the ability to analyze, plan, and react to changing business conditions in a much more rapid fashion.
Data Warehouse • In order for data to be effective, DW must be: • Consistent. • Well integrated. • Well defined. • Time stamped. • DW environment: • The data store, data mart & the metadata.
The Data Store • An operational data store (ODS) stores data for a specific application. It feeds the data warehouse a stream of desired raw data. • Is the most common component of DW environment. • Data store is generally subject oriented, volatile, current commonly focused on customers, products, orders, policies, claims, etc…
Data Store & Data Warehouse • Data store & Data warehouse, table 10-1 page 296
The data store-Cont’d. • Its day-to-day function is to store the data for a single specific set of operational application. • Its function is to feed the data warehouse data for the purpose of analysis.
The Data Mart • It is lower-cost, scaled down version of the DW. • Data Mart offer a targeted and less costly method of gaining the advantages associated with data warehousing and can be scaled up to a full DW environment over time.
The Meta Data • Last component of DW environments. • It is information that is kept about the warehouse rather than information kept within the warehouse. • Legacy systems generally don’t keep a record of characteristics of the data (such as what pieces of data exist and where they are located). • The metadata is simply data about data.
Conclusion • A Data Warehouse is a collection of integrated subject-oriented databases designed to support a DSS. • Each unit of data is non-volatile and relevant to some moment in time. • An operational data store (ODS) stores data for a specific application. It feeds the data warehouse a stream of desired raw data. • A data mart is a lower-cost, scaled-down version of a data warehouse, usually designed to support a small group of users (rather than the entire firm). • The metadata is information that is kept about the warehouse.
Data Warehouse • Subject oriented • Data integrated • Time variant • Nonvolatile
Characteristics of Data Warehouse • Subject oriented. Data are organized based on how the users refer to them. • Integrated. All inconsistencies regarding naming convention and value representations are removed. • Nonvolatile. Data are stored in read-only format and do not change over time. • Time variant. Data are not current but normally time series.
Characteristics of Data Warehouse • SummarizedOperational data are mapped into a decision-usable format • Large volume. Time series data sets are normally quite large. • Not normalized. DW data can be, and often are, redundant. • Metadata. Data about data are stored. • Data sources. Data come from internal and external unintegrated operational systems.
Data Integrated • Integration –consistency naming conventions and measurement attributers, accuracy, and common aggregation. • Establishment of a common unit of measure for all synonymous data elements from dissimilar database. • The data must be stored in the DW in an integrated, globally acceptable manner
Time Variant • In an operational application system, the expectation is that all data within the database are accurate as of the moment of access. In the DW data are simply assumed to be accurate as of some moment in time and not necessarily right now. • One of the places where DW data display time variance is in the structure of the record key. Every primary key contained within the DW must contain, either implicitly or explicitly an element of time( day, week, month, etc)
Time Variant • Every piece of data contained within the warehouse must be associated with a particular point in time if any useful analysis is to be conducted with it. • Another aspect of time variance in DW data is that, once recorded, data within the warehouse cannot be updated or changed.
Nonvolatility • Typical activities such as deletes, inserts, and changes that are performed in an operational application environment are completely nonexistent in a DW environment. • Only two data operations are ever performed in the DW: data loading and data access
Issues of Data Redundancy between DW and operational environments • The lack of relevancy of issues such as data normalization in the DW environment may suggest that existence of massive data redundancy within the data warehouse and between the operational and DW environments. • Inmon(1992) pointed out and proved that it is not true.
Issues of Data Redundancy between DW and operational environments • The data being loaded into the DW are filtered and “cleansed” as they pass from the operational database to the warehouse. Because of this cleansing numerous data that exists in the operational environment never pass to the data warehouse. Only the data necessary for processing by the DSS or EIS are ever actually loaded into the DW. • The time horizons for warehouse and operational data elements are unique. Data in the operational environment are fresh, whereas warehouse data are generally much older.(so there is minimal opportunity of the data to overlap between two environments ) • The data loaded into the DW often undergo a radical transformation as they pass form operational to the DW environment. So data in DW are not the same. Given this factors, Inmon suggests that data redundancy between the two environments is a rare occurrence with a typical redundancy factor of less than 1 %
The Data Warehouse Architecture The architecture consists of various interconnected elements: • Operational and external database layer – the source data for the DW • Information access layer – the tools the end user access to extract and analyze the data • Data access layer – the interface between the operational and information access layers • Metadata layer – the data directory or repository of metadata information
The Data Warehouse Architecture Additional layers are: • Process management layer – the scheduler or job controller • Application messaging layer – the “middleware” that transports information around the firm • Physical data warehouse layer – where the actual data used in the DSS are located • Data staging layer – all of the processes necessary to select, edit, summarize and load warehouse data from the operational and external data bases
Data Warehousing Typology • The virtual data warehouse – the end users have direct access to the data stores, using tools enabled at the data access layer • The central data warehouse – a single physical database contains all of the data for a specific functional area • The distributed data warehouse – the components are distributed across several physical databases
The Metadata • The name suggests some high-level technological concept, but it really is fairly simple. Metadata is “data about data”. • With the emergence of the data warehouse as a decision support structure, the metadata are considered as much a resource as the business data they describe. • Metadata are abstractions -- they are high level data that provide concise descriptions of lower-level data.
The Metadata For example, a line in a sales database may contain: 4056 KJ596 223.45 This is mostly meaningless until we consult the metadata that tells us it was store number 4056, product KJ596 and sales of $223.45 The metadata are essential ingredients in the transformation of raw data into knowledge. They are the “keys” that allow us to handle the raw data.
General Metadata Issues General metadata issues associated with Data Warehouse use: • What tables, attributes and keys does the DW contain? • Where did each set of data come from? • What transformations were applied with cleansing? • How have the metadata changed over time? • How often do the data get reloaded? • Are there so many data elements that you need to be careful what you ask for?
Components of the Metadata • Transformation maps – records that show what transformations were applied • Extraction & relationship history – records that show what data was analyzed • Algorithms for summarization – methods available for aggregating and summarizing • Data ownership – records that show origin • Patterns of access – records that show what data are accessed and how often
Typical Mapping Metadata Transformation mapping records include: • Identification of original source • Attribute conversions • Physical characteristic conversions • Encoding/reference table conversions • Naming changes • Key changes • Values of default attributes • Logic to choose from multiple sources • Algorithmic changes
Implementing the Data Warehouse Kozar list of “seven deadly sins” of data warehouse implementation: • “If you build it, they will come” – the DW needs to be designed to meet people’s needs • Omission of an architectural framework – you need to consider the number of users, volume of data, update cycle, etc. • Underestimating the importance of documenting assumptions – the assumptions and potential conflicts must be included in the framework
“Seven Deadly Sins”, continued • Failure to use the right tool – a DW project needs different tools than those used to develop an application • Life cycle abuse – in a DW, the life cycle really never ends • Ignorance about data conflicts – resolving these takes a lot more effort than most people realize • Failure to learn from mistakes – since one DW project tends to beget another, learning from the early mistakes will yield higher quality later
Data Warehouse Technologies • No one currently offers an end-to-end DW solution. Organizations buy bits and pieces from a number of vendors and hopefully make them work together. • SAS, IBM, Software AG, Information Builders and Platinum offer solutions that are at least fairly comprehensive. • The market is very competitive. Table 10-6 in the text lists 90 firms that produce DW products.
The Future of Data Warehousing As the DW becomes a standard part of an organization, there will be efforts to find new ways to use the data. This will likely bring with it several new challenges: • Regulatory constraints may limit the ability to combine sources of disparate data. • These disparate sources are likely to contain unstructured data, which is hard to store. • The Internet makes it possible to access data from virtually “anywhere”. Of course, this just increases the disparity.
Interesting Facts Data Can be Used To Robust Infrastructure Success of Data Warehouse Projects Implementing Data Warehouse Real Time Alerts & Integration Identity Theft What Can You Do? Objective
Interesting Facts • Harrah’s Entertainment’s Data Warehouse holds 30 terabytes, or 30 trillion bytes of data, roughly three times the number of printed characters in the Library of Congress • Casinos, retailers, airlines, and banks are piling up data so vast, it would have been unthinkable years ago; result from the curse of cheap storage
Interesting Facts • Storage Shipments as of 2004: 22 exabytes or 22 million trillion bytes of hard disk space, double the amount in 2002. • Equivalent to 4x’s the space needed to store every word ever spoken by every human being who has ever lived. • Should double again in 2006
Data Can be Used To • Quantify the volume impact of vehicles across the marketing matrix • Account for decay and saturation factors in the determination of investment choices and returns • Execute “what-if” simulations of pricing or promotional scenarios before a proposed action is taken • Provide a continuous planning, measurement, analysis and optimization cycle supported by a software structure • Deliver robust data feeds into other systems supporting supply chain, sales, and financial reporting and endeavors
Robust Infrastructure • Data Identification and Acquisition • Data Cleansing, Mapping, and Transformation • Production System Loading and Ongoing Update
Success of Data Warehouse Projects • Over half of Data Warehouse projects are Doomed • Fail due to lack of attention to Data Quality Issues • More than half only have limited acceptance • Consistency and Accuracy of Data • Most businesses fail to use business intelligence (BI) strategically • IT organizations build data warehouses with little to no business involvement
“A real-time enterprise without real-time business intelligence is a real fast, dumb organization.” Stephen Brobst Chief Technology Office Teradata
Success of Data Warehouse Projects • Most challenging type of deployment for an enterprise • Large scale and complex system configurations • Sophisticated data modeling and analysis tools • High visibility in broad range of important business functions within company • Adoption of Linux-Based Platform
Implementing Data Warehouse • Challenges: • Identifying new processes • Assuring there were of real use • Implementing and ensuring cultural shifts • Managing content and New communities towards a common benefit • Linear models • Standards, Governance, Controls, Valuation
Teradata • Division of NCR in Dayton, Ohio • Competitor of IBM and Oracle • Multi-million Dollar Machines to run the world’s biggest data warehouses • Wal-Mart • Bank of America • Verizon Wireless
Teradata’s Success • Conventional IBM or Sun Microsystems overload for a couple hours to days on a few terabytes and/or data queries • IBM cannot return computation on certain complex requests • Equivalent to having data but not able to use it.