Alex Berson; Stephen J Smith. This reference provides strategic, theoretical and practical insight into three information management technologies: data warehousing, online analytical processing (OLAP), and data mining. Add tags for "Data warehousing, data mining, and OLAP". Rent and save from the world's largest eBookstore. Read, highlight, and take notes, across Data Warehousing, Data Mining, and OLAP Snippet view - . This reference provides strategic, theoretical and practical insight into three information management technologies: data warehousing, online analytical.
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Optimize your organization's data delivery system! Improving data delivery is a top priority in business computing today. This comprehensive, cutting-edge guide . Data Warehousing, Data Mining, and OLAP by Alex Berson, , available at Book Depository with free delivery worldwide. DATA WAREHOUSING, OLAP AND DATA MINING THE PRESENT: THE IT PROFESSIONAL'S RESPONSIBILITY Today, the IT professional continues to.
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The data warehouse becomes the common information resource for decisional purposes throughout the organization. While these differences may seem trivial at the first glance, the subtle nuances that exist depending on the context may result in misleading numbers and ill-informed decisions. The operational systems will not be able to meet this kind of information need for a good reason. A data warehouse should be used to record the past accurately, leaving the OLTP systems free to focus on recording current transactions and balances.
Instead, historical data are loaded and integrated with other data in the warehouse for quick access.
To Slice and Dice Through Data As stated earlier in this chapter, dynamic reports allow users to view warehouse data from different angles, at different levels of detail business users with the means and the ability to slice and dice through warehouse data can actively meet their own information needs.
The ready availability of different data views also improves business analysis by reducing the time and effort required to collect, format, and distill information from data.
To Separate Analytical and Operational Processing Decisional processing and operational information processing have totally divergent architectural requirements.
Attempts to meet both decisional and operational information needs through the same system or through the same system architecture merely increase the brittleness of the IT architecture and will create system maintenance nightmares. Data warehousing disentangles analytical from operational processing by providing a separate system architecture for decisional implementations.
This makes the overall IT architecture of the enterprise more resilient to changing requirements. To Support the Reengineering of Decisional Processes At the end of each BPR initiative come the projects required to establish the technological and organizational systems to support the newly reengineered business process.
Although reengineering projects have traditionally focused on operational processes, data warehousing technologies make it possible to reengineer decisional business processes as well.
Data warehouses, with their focus on meeting decisional business requirements, are the ideal systems for supporting reengineered decisional business processes.
The concept of the data mart is causing a lot of excitement and attracts much attention in the data warehouse industry. Mostly, data marts are presented as an inexpensive alternative to a data warehouse that takes significantly less time and money to build. However, the term data mart means different things to different people. A rigorous definition of this term is a data store that is subsidiary to a data warehouse of integrated data.
The data mart is directed at a partition of data often called a subject area that is created for the use of a dedicated group of users. A data mart might, in fact, be a set of denormalized, summarized, or aggregated data. Sometimes, such a set could be placed on the data warehouse database rather than a physically separate store of data.
In most instances, however, the data mart is a physically separate store of data and is normally resident on a separate database server, often on the local area enterprises relational OLAP technology which creates highly denormalized star schema relational designs or hypercubes of data for analysis by groups of users with a common interest in a limited portion of the database.
All these type of data marts, called dependent data marts because their data content is sourced from the data warehouse, have a high value because no matter how many are deployed and no matter how many different enabling technologies are used, the different users are all accessing the information views derived from the same single integrated version of the data.
Unfortunately, the misleading statements about the simplicity and low cost of data marts sometimes result in organizations or vendors incorrectly positioning them as an alternative to the data warehouse. This viewpoint defines independent data marts that in fact represent fragmented point solutions to a range of business problems in the enterprise.
This type of implementation should rarely be deployed in the context of an overall technology of applications architecture. Indeed, it is missing the ingredient that is at the heart of the data warehousing concept: data integration. Each independent data mart makes its own assumptions about how to consolidate the data, and the data across several data marts may not be consistent. As a result, an environment is created in which multiple operational systems feed multiple non-integrated data marts that are often overlapping in data content, job scheduling, connectivity, and management.
In other words, a complex many-to-one problem of building a data warehouse is transformed from operational and external data sources to a many-to-many sourcing and management nightmare.
But, as usage begets usage, the initial small data mart needs to grow i. It is clear that the point-solution-independent data mart is not necessarily a bad thing, and it is often a necessary and valid solution to a pressing business problem, thus achieving the goal of rapid delivery of enhanced decision support functionality to end users.
Reporting and Query Tools and Applications. Patterns and Models. Artificial Intelligence.
Part IV: Data Mining. Introduction to Data Mining. Decision Trees. Neural Networks. Nearest Neighbor and Clustering.
Genetic Algorithms. Rule Induction. Selecting and Using the Right Technique. Part V: Data Visualization and Overall Perspective. Data Visualization.
Putting It All Together. Big Data--Better Returns: Mistakes for Data Warehousing Managers to Avoid. Berson holds a Ph. Introduction to LU6.
Data Warehousing, Data Mining, and OLAP
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Close X.While at the onset those data marts are certainly easier to develop, the task of maintaining many unrelated data marts is exceedingly complex and will create data management, synchronization, and consistency issues.
Poor Data Quality of Operational Systems When the data quality of the operational systems is suspect, the team will, by necessity, devote much of its time and effort to data scrubbing and data quality checking. But, as usage begets usage, the initial small data mart needs to grow i. Since a common source of information is now used, the data warehouse puts to rest all debates about the veracity of data used or cited in meetings.
The workload of the architect is heavier at the start of each rollout, when most of the design decisions are made. Allocate sufficient time for team orientation and training prior to and during the course of the project to ensure that everyone remains aligned.