Data Mining: Concepts and Techniques, Jiawei Han and Micheline Kamber About data mining and data warehousing; Mining of Massive. No part of this ebook may be reproduced in any form, by photostat, microfilm, Tiered Data Warehousing Architectures The enterprise is free to mix and match. Hello Friends, Here is the Notes for Computer Science and Engineering - Data Mining & Data Warehousing in PDF Format. It Contains.
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Computer Science Engineering Ebooks Download/ Computer Science Data Mining and Data Warehousing Lecture nNotes Free Download. Hi Fellows, Check out the ebook of Data mining & warehousing for engineering students & BCA/MCA students. I will also share the lecture. Data Mining: Practical Machine Learning Tools and Techniques with Java . Chapter 4 Data Warehousing and Online Analytical Processing
No part of this ebook may be reproduced in any form, by photostat, microfilm, xerography, or any other means, or incorporated into any information retrieval system, electronic or mechanical, without the written permission of the publisher. This book also aims at providing fundamental techniques of KDD and Data Mining as well as issues in practical use of Mining tools. Far from being just a passing fad, data warehousing technology has grown much in scale and reputation in the past few years, as evidenced by the increasing number of products, vendors, organizations, and yes, even books, devoted to the subject. Enterprises that have successfully implemented data warehouses find it strategic and often wonder how they ever managed to survive without it in the past. Also Knowledge Discovery and Data Mining KDD has emerged as a rapidly growing interdisciplinary field that merges together databases, statistics, machine learning and related areas in order to extract valuable information and knowledge in large volumes of data. The first section introduces the Enterprise Architecture and Data Warehouse concepts, the basis of the reasons for writing this book. The second section focuses on three of the key People in any data warehousing initiative:
Components of this type can and should be used across the various layers of the Enterprise Architecture and, therefore, are not described here as a separate item. Which are recommended for fulfilling those needs. Legacy Integration The Need The integration of new and legacy systems is a constant challenge because of the architectural templates upon which legacy systems were built. Legacy systems often attempt to meet all types of information requirements through a single architectural component; consequently, these systems are brittle and resistant to evolution.
Despite attempts to replace them with new applications, many legacy systems remain in use because they continue to meet a set of business requirements: Legacy programs that produce and maintain summary information are migrated to the data warehouse. Historical data are likewise migrated to the data warehouse.
Data required for operational monitoring are moved to the Operational Data Store. Table 1. The Operational Data Store and the data warehouse present IT professionals with a natural migration path for legacy migration. By migrating legacy systems to these two components, enterprises can gain a measure of independence from legacy components that were designed with old, possibly obsolete, technology. Legacy systems are typically structured around functional or organizational areas, in contrast to the cross-functional view required by operations monitoring.
Different and potentially incompatible technology platforms may have been used for different systems. Data may be available in legacy databases but are not extracted in the format required by business users. Or data may be available but may be too raw to be of use for operational decision-making further summarization, calculation, or conversion is required.
And lastly, several systems may contain data about the same item but may examine the data from different viewpoints or at different time frames, therefore requiring reconciliation. Legacy Integration: Architectural View The Recommended Approach An integrated view of current, operational information is required for the successful monitoring of operations.
Instead, an Operational Data Store, coupled with flash monitoring and reporting tools, as shown in Figure 1. Like a dashboard on a car, flash monitoring and reporting tools keep business users apprised of the latest cross-functional status of operations. These tools obtain data from the Operational Data Store, which is regularly refreshed with the latest information from legacy and other operational systems.
Business users are consequently able to step in and correct problems in operations while they are still smaller or better, to prevent problems from occurring altogether. Once alerted of a potential problem, the business user can manually intervene or make use of automated tools i.
Operational Monitoring: Technology advances have made it possible to build and modify systems quickly in response to changes in business processes. New policies, procedures and controls are supported and enforced by the systems.
In addition, workflow management systems can be used to supplement OLTP applications. A workflow management system converts business activities into a goal-directed process that flows through the enterprise in an orderly fashion see Figure 1. The workflow management system alerts users through the automatic generation of notification messages or reminders and routes work so that the desired business result is achieved in an expedited manner.
Process Implementation Decision Support The Need It is not possible to anticipate the information requirements of decision makers for the simple reason that their needs depend on the business situation that they face. Decisionmakers need to review enterprise data from different dimensions and at different levels of detail to find the source of a business problem before they can attack it.
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They likewise need information for detecting business opportunities to exploit. Decision-makers also need to analyze trends in the performance of the enterprise. Rather than waiting for problems to present themselves, decision-makers need to proactively mobilize the resources of the enterprise in anticipation of a business situation.
Process Implementation: Alternatively, the IT professional is pressured to produce an ad hoc report from legacy systems as quickly as possible.
If unlucky, the IT professional will find the data needed for the report are scattered throughout different legacy systems. An even unluckier may find that the processing required to produce the report will have a toll on the operations of the enterprise. These delays are not only frustrating both for the decision-maker and the IT professional, but also dangerous for the enterprise.
The information that eventually reaches the decisionmaker may be inconsistent, inaccurate, worse, or obsolete. The Recommended Approach Decision support applications or OLAP that obtain data from the data warehouse are recommended for this particular need.
The data warehouse holds transformed and integrated enterprise-wide operational data appropriate for strategic decision-making, as shown in Figure 1. The data warehouse also contains data obtained from external-sources, whenever this data is relevant to decision-making. Decision Support Decision support applications analyze and make data warehouse information available in formats that are readily understandable by decision-makers. Hyperdata Distribution The Need Past informational requirements were met by making data available in physical form through reports, memos, and company manuals.
This practice resulted in an overflow of documents providing much data and not enough information.
Enterprises encountered problems in keeping different versions of related items synchronized. There was a constant need to update, republish and redistribute documents. Decision Support: Architectural View In response to this problem, enterprises made data available to users over a network to eliminate the paper.
It was hoped that users could selectively view the data whenever they needed it. This approach likewise proved to be insufficient because users still had to navigate through a sea of data to locate the specific item of information that was needed. The Recommended Approach Users need the ability to browse through nonlinear presentations of data.
Web technology is particularly suitable to this need because of its extremely flexible and highly visual method of organizing information see Figure 1.
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Users are therefore able to locate information with relative ease. Hyperdata Distribution: Architectural View Virtual Corporation The Need A virtual corporation is an enterprise that has extended its business processes to encompass both its key customers and suppliers.
Its business processes are newly redesigned; its product development or service delivery is accelerated to better meet customer needs and preferences; its management practices promote new alignments between management and labor, as well as new linkages among enterprise, supplier and customer.
A new level of cooperation and openness is created and encouraged between the enterprise and its key business partners.
The Recommended Approach Partnerships at the enterprise level translate into technological links between the enterprise and its key suppliers or customers see Figure 1.
Information required by each party is identified, and steps are taken to ensure that this data crosses organizational boundaries properly. Some organizations seek to establish a higher level of cooperation with their key business partners by jointly redesigning their business processes to provide greater value to the customer.
Internet and web technologies are well suited to support redesigned, transactional processes. Thanks to decreasing Internet costs, improved security measures, improved userfriendliness, and navigability.
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Virtual Corporation: Architectural View 1. The strategies presented in the previous section enable organizations to move from their current technology architectures into the InfoMotion Enterprise Architecture. This section describes the tasks for any migration effort. Review the Current Enterprise Architecture As simple as this may sound, the starting point is a review of the current Enterprise Architecture. It is important to have an idea of whatever that is already available before planning for further achievements.
The IT department or division should have this information readily available, although it may not necessarily be expressed in terms of the architectural components identified above. A short and simple exercise of mapping the current architecture of an enterprise to the architecture described above should quickly highlight any gaps in the current architecture.
Gaps should cause concern only if the absence of an architectural component prevents the IT infrastructure from meeting present requirements or from supporting long-term strategies. For example, if transactional web scripts are not critical to an enterprise given its current needs and strategies, there should be no cause for concern. Develop a Migration Plan Based on Requirements It is not advisable for an enterprise to use this list of architectural gaps to justify a dramatic overhaul of its IT infrastructure; such an undertaking would be expensive and would cause unnecessary disruption of business operations.
Instead, the enterprise would do well to develop a migration plan that consciously maps coming IT projects to the InfoMotion Enterprise Architecture.
The Natural Migration Path While developing the migration plan, the enterprise should consider the natural migration path that the InfoMotion architecture implies, as illustrated in Figure 1.
For most companies, this core layer is where the majority of technology investments have been made. It should also be the starting point of any architecture migration effort, i. It also provides the succeeding technology layers with a more stable foundation for future evolution. The scenarios provide generic roadmaps that address typical architectural needs.
The migration plan, however, must be customized to address the specific needs of the enterprise. Each project defined in the plan must individually contribute to the enterprise in the short term, while laying the groundwork for achieving long-term enterprise and IT objectives. By incrementally migrating its IT infrastructure one component and one project at a time , the enterprise will find itself slowly but surely moving towards a modern, resilient Enterprise Architecture, with minimal and acceptable levels of disruption in operations.
Monitor and Update the Migration Plan The migration plan must be monitored, and the progress of the different projects fed back into the planning task. One must not lose sight of the fact that a modern Enterprise Architecture is a moving target; inevitable new technology renders continuous evolution of the Enterprise Architecture.
IN Summary An enterprise has longevity in the business arena only when its products and services are perceived by its customers to be of value.
Likewise, Information Technology has value in an enterprise only when its cost is outweighed by its ability to increase and guarantee quality, improve service, cut costs or reduce cycle time, as depicted in Figure 1.
The Enterprise Architecture is the foundation for all Information Technology efforts. It therefore must provide the enterprise with the ability to: These requirements form the basis for the InfoMotion equation, shown in Figure 1.
It also defines data warehousing concepts and cites the typical reasons for building data warehouses. The differences in operational and decisional information requirements presented new challenges that old computing practices could not meet. Below, we elaborate on how this change in computing focus became the impetus for the development of data warehousing technologies. The Business Cycle 24 Policy In Chapter 1, it is noted that much of the effort and money in computing has been focused on meeting the operational business requirements of enterprises.
After all, without the OLTP applications that records thousands, even millions of discrete transactions each day, it would not be possible for any enterprise to meet customer needs while enforcing business policies consistently. Nor would it be possible for an enterprise to grow without significantly expanding its manpower base. With operational systems deployed and day-to-day information needs being met by the OLTP systems, the focus of computing has over the recent years shifted naturally to meeting the decisional business requirements of an enterprise.
Figure 2. Decision-makers themselves cannot be expected to know their information requirements ahead of time; they review enterprise data from different perspectives and at different levels of detail to find and address business problems as the problems arise.
Decision-makers also need to look through business data to identify opportunities that can be exploited. They examine performance trends to identify business situations that can provide competitive advantage, improve profits, or reduce costs. They analyze market data and make the tactical as well as strategic decisions that determine the course of the enterprise. Operational Systems Fail to Provide Decisional Information Since these information requirements cannot be anticipated, operational systems which correctly focus on recording and completing different types of business transactions are unable to provide decision-makers with the information they need.
As a result, business managers fall back on the time-consuming, and often frustrating process of going through operational inquiries or reports already supported by operational systems in an attempt to find or derive the information they really need. Alternatively, IT professionals are pressured to produce an adhoc report from the operational systems as quickly as possible.
It will not be unusual for the IT professional to find that the data needed to produce the report are scattered throughout different operational systems and must first be carefully integrated. Worse, it is likely that the processing required to extract the data from each operational system will demand so much of the system resources that the IT professional must wait until non-operational hours before running the queries required to produce the report.
Those delays are not only time-consuming and frustrating both for the IT professionals and the decision-makers, but also dangerous for the enterprise. When the report is finally produced, the data may be inconsistent, inaccurate, or obsolete. There is also the very real possibility that this new report will trigger the request for another adhoc report. Decisional Systems have Evolved to Meet Decisional Requirements Over the years, decisional systems have been developed and implemented in the hope of meeting these information needs.
Most decisional systems, however, have failed to deliver on their promises. Each query frequently results in a large results set and involves frequent full table scan and multi-table joins.
What is a data warehouse? William H. Integrated A data warehouse contains data extracted from the many operational systems of the enterprise, possibly supplemented by external data. For example, a typical banking data warehouse will require the integration of data drawn from the deposit systems, loan systems, and the general ledger. Each of these operational systems records different types of business transactions and enforces the policies of the enterprise regarding these transactions.
If each of the operational systems has been custom built or an integrated system is not implemented as a solution, then it is unlikely that these systems are integrated.
Thus, Customer A in the deposit system and Customer B in the loan system may be one and the same person, but there is no automated way for anyone in the bank to know this. Customer relationships are managed informally through relationships with bank officers. A data warehouse brings together data from the various operational systems to provide an integrated view of the customer and the full scope of his or her relationship with the bank.
Modern operational systems, in turn, have shifted their focus to the operational requirements of an entire business process and aim to support the execution of the business process from start to finish. A data warehouse goes beyond traditional information views by focusing on enterprisewide subjects such as customers, sales, and profits. These subjects span both organizational and process boundaries and require information from multiple sources to provide a complete picture.
Databases Although the term data warehousing technologies is used to refer to the gamut of technology components that are required to plan, develop, manage, implement, and use a data warehouse, the term data warehouse itself refers to a large, read-only repository of data.
At the very heart of every data warehouse lie the large databases that store the integrated data of the enterprise, obtained from both internal and external data sources.
The term internal data refers to all data that are extracted from the operational systems of the enterprise. External data are data provided by third-party organizations, including business partners, customers, government bodies, and organizations that choose to make a profit by selling their data e.
Also stored in the databases are the metadata that describe the contents of the data warehouse. A more thorough discussion on metadata and their role in data warehousing is provided in Chapter 3.
Required for Decision-Making Unlike the databases of operational systems, which are often normalized to preserve and maintain data integrity, a data warehouse is designed and structured in a demoralized manner to better support the usability of the data warehouse. Users are better able to examine, derive, summarize, and analyze data at various levels of detail, over different periods of time, when using a demoralized data structure. For example, while a finance manager is interested in the profitability of the various products of a company, a product manager will be more interested in the sales of the product in the various sales regions.
In this manner, a decision-maker can start with a high-level view of the business, then drill down to get more detail on the areas that require his attention, or vice versa. The time-stamping of each fact also makes it possible for decision-makers to recognize trends and patterns in customer or market behavior over time.
Data at the most detailed level, i. Aggregates presummarized data are stored in the warehouse to speed up responses to queries at higher levels of granularity. If the data warehouse stores data only at summarized levels, its users will not be able to drill down on data items to get more detailed information. However, the storage of very detailed data results in larger space requirements. The term dynamic report refers to a report that can be quickly modified by its user to present either greater or lesser detail, without any additional programming required.
Dynamic reports are the only kind of reports that provide true, adhoc reporting capabilities. When the summary calls attention to an area that bears closer inspecting, the decision-maker should be able to point to that portion of the report, then obtain greater detail on it dynamically, on an as-needed basis, with no further programming. To Provide Business Users with Access to Data The data warehouse provides access to integrated enterprise data previously locked away in unfriendly, difficult-to-access environments.
Business users can now establish, with minimal effort, a secured connection to the warehouse through their desktop PC. Security is enforced either by the warehouse front-end application, or by the server database, or by the both.
Because of its integrated nature, a data warehouse spares business users from the need to learn, understand, or access operational data in their native environments and data structures. To Provide One Version of the Truth The data in the data warehouse are consistent and quality assured before being released to business users.
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 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: 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. Another consideration against independent data marts is related to the potential scalability problem: 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.
The business drivers underlying such developments include: To address data integration issues associated with data marts, the recommended approach proposed by Ralph Kimball is as follows. For any two data mart in an enterprise, the common dimensions must conform to the equality and roll-up rule, which states that these dimensions are either the same or that one is a strict roll-up of another.
The time dimensions from both data marts might be at the individual day level, or, conversely, one time dimension is at the day level but the other is at the week level. Because days roll up to weeks, the two time dimensions are conformed.
The time dimensions would not be conformed if one time dimension were weeks and the other time dimension, a fiscal quarter. The resulting data marts could not usefully coexist in the same application.
In summary, data marts present two problems: Therefore, when designing data marts, the organizations should pay close attention to system scalability, data consistency, and manageability issues. The key to a successful data mart strategy is the development of overall scalable data warehouse architecture; and key step in that architecture is identifying and implementing the common dimensions.
A number of misconceptions exist about data marts and their relationships to data warehouses discuss two of those misconceptions below. Data Marts can be Built Independently of One Another Some enterprises find it easier to deploy multiple data marts independently of one another. At the first glance, such an approach is indeed easier since there are no integration issues.
Different groups of users are involved with each data mart, which implies fewer conflicts about the use of terms and about business rules.
Each data mart is free to exist within its own isolated world, and all the users are happy. Unfortunately, that enterprises fail to realize until much later is that by deploying one isolated data mart after another, the enterprise has actually created new islands of automation. 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.
Multiple data marts are definitely appropriate within an organization, but these should be implemented only under the integrating framework of an enterprise-wide data warehouse. Each data mart is developed as an extension of the data warehouse and is fed by the data warehouse. The data warehouses enforces a consistent set of business rules and ensures the consistent use of terms and definitions.
Although both technologies support decisional information needs of enterprise decisionmakers, the two are distinctly different and are deployed to meet different types of decisional information needs. Inmon, C. Imhoff, and G.
Unlike the databases of OLTP applications that are operational or function oriented , the Operational Data Store contains subject-oriented, enterprise-wide data. However, unlike data warehouses, the data in Operational Data Stores are volatile, current and detailed.
However, some significant challenges of the ODS still remain. Table 2. The ODS provides an integrated view of the data in the operational systems. Data are transformed and integrated into a consistent, unified whole as they are obtained from legacy and other operational systems to provide business users with an integrated and current view of operations.
Flash Monitoring and Reporting Tools As mentioned in Chapter 1, flash monitoring and reporting tools are like a dashboard that provides meaningful online information on the operational status of the enterprise. Operational Monitoring Relationship of Operational Data Stores to Data Warehouse Enterprises with Operational Data Stores find themselves in the enviable position of being able to deploy data warehouses with considerable ease.
Since operational data stores are integrated, many of the issues related to extracting, transforming, and transporting data from legacy systems have been addressed by the ODS, as illustrated in Figure 2. The ODS is free to focus only on the current state of operations and is constantly updated in real time. Although the task of calculating ROI for data warehousing initiatives is unique to each enterprise, it is possible to classify the type of benefits and costs that are associated with data warehousing.
Benefits Data warehousing benefits can be expected from the following areas: The quantification of such costs in terms of staff hours and erroneous data may yield surprising results. Benefits of this nature, however, are typically minimal, since warehouse maintenance and enhancements require staff as well. At best, staff will be redeployed to more productive tasks. Analysts go through several steps in their day-to-day work: Unfortunately, much of the time sometimes up to 40 percent spent by enterprise analysts on a typical day is devoted to locating and retrieving data.
The availability of integrated, readily accessible data in the data warehouse should significantly reduce the time that analysts spend with data collection tasks and increase the time available to actually analyze the data they have collected.
This leads either to shorter decision cycle times or improvements in the quality of the analysis. The most significant business improvements in warehousing result from the analysis of warehouse data, especially if the easy availability of information yields insights here before unknown to the enterprise. The goal of the data warehouse is to meet decisional information needs, therefore it follows naturally the greatest benefits of warehousing that are obtained when decisional information needs are actually met and sound business decisions are made both at the tactical and strategic level.
Understandably, such benefits are more significant and therefore, more difficult to project and quantify. Costs Data warehousing costs typically fall into one of the four categories. These are: This item refers to the costs associated with setting up the hardware and operating environment required by the data warehouse. In many instances, this setup may require the acquisition of new equipment or the upgrade of existing equipment. Larger warehouse implementations naturally imply higher hardware costs.
This item refers to the costs of purchasing the licenses to use software products that automate the extraction, cleansing, loading, retrieval, and presentation of warehouse data.
This item refers to services provided by systems integrators, consultants, and trainers during the course of a data warehouse project.
This item refers to costs incurred by assigning internal staff to the data warehousing effort, as well as to costs associated with training internal staff on new technologies and techniques. ROI Considerations The costs and benefits associated with data warehousing vary significantly from one enterprise to another. The effect of data warehousing on the tactical and strategic management of an enterprise is often likened to cleaning the muddy windshield of a car.
It is difficult to quantify the value of driving a car with a cleaner windshield. Similarly, it is difficult to quantify the value of managing an organization with better information and insight.
Lastly, it is important to note that data warehouse justification is often complicated by the fact that much of the benefit may take sometime to realize and therefore is difficult to quantify in advance.
In Summary Data warehousing technologies have evolved as a result of the unsatisfied decisional information needs of enterprises. With the increased stability of operational systems, information technology professionals have increasingly turned their attention to meeting the decisional requirements of the enterprise. A data warehouse, according to Bill Inmon, is a collection of integrated, subject-oriented databases designed to supply the information required for decision-making.
Each data item in the data warehouse is relevant to some moment in time. A data mart has traditionally been defined as a subset of the enterprise-wide data warehouse. Many enterprises, upon realizing the complexity involved in deploying a data warehouse, will opt to deploy data marts instead. Although data marts are able to meet the immediate needs of a targeted group of users, the enterprise should shy away from deploying multiple, unrelated data marts.
The presence of such islands of information will only result in data management and synchronization problems. Like data warehouses, Operational Data Stores are integrated and subject-oriented. However, an ODS is always current and is constantly updated ideally in real time. The Operational Data Store is the ideal data source for a data warehouse, since it already contains integrated operational data as of a given point in time.
Although data warehouses have proven to have significant returns on investment, particularly when they are meeting a specific, targeted business need, it is extremely difficult to quantify the expected benefits of a data warehouse.
The costs are easier to calculate, as these break down simply into hardware, software, services, and in-house staffing costs. PEOPLE Although a number of people are involved in a single data warehousing project, there are three key roles that carry enormous responsibilities.
Negligence in carrying out any of these three roles can easily derail a well-planned data warehousing initiative. This section of the book therefore focuses on the Project Sponsor, the Chief Information Officer, and the Project Manager and seeks to answer the questions frequently asked by individuals who have accepted the responsibilities that come with these roles. Every data warehouse initiative has a Project Sponsor-a high-level executive who provides strategic guidance, support, and direction to the data warehousing project.
The Project Sponsor ensures that project objectives are aligned with enterprise objectives, resolves organizational issues, and usually obtains funding for the project.
The CIO is responsible for the effective deployment of information technology resources and staff to meet the strategic, decisional, and operational information requirements of the enterprise. Data warehousing, with its accompanying array of new technology and its dependence on operational systems, naturally makes strong demands on the physical and human resources under the jurisdiction of the CIO, not only during design and development but also during maintenance and subsequent evolution.
The warehouse Project Manager is responsible for all technical activities related to implementing a data warehouse. Ideally, an IT professional from the enterprise fulfills this critical role. It is not unusual, however, for this role to be outsourced for early or pilot projects, because of the newness of warehousing technologies and techniques.
This chapter attempts to provide answers to questions frequently asked by Project Sponsors. It is naive to expect an immediate change to the decision-making processes in an organization when a data warehouse first goes into production. End users will initially be occupied more with learning how to use the data warehouse than with changing the way they obtain information and make decisions.
It is also likely that the first set of predefined reports and queries supported by the data warehouse will differ little from existing reports.
Decision-makers will experience varying levels of initial difficulty with the use of the data warehouse; proper usage assumes a level of desktop computing skills, data knowledge, and business knowledge. Desktop Computing Skills Not all business users are familiar and comfortable with the desktop computers, and it is unrealistic to expect all the business users in an organization to make direct, personal use of the front-end warehouse tools.
On the other hand, there are power users within the organization who enjoy using computers, love spreadsheets, and will quickly push the tools to the limit with their queries and reporting requirements.
Data Knowledge It is critical that business users be familiar with the contents of the data warehouse before they make use of it. In many cases, this requirement entails extensive communication on two levels. First, the scope of the warehouse must be clearly communicated to property manage user expectations about the type of information they can retrieve, particularly in the earlier rollouts of the warehouse. Second, business users who will have direct access to the data warehouse must be trained on the use of the selected front-end tools and on the meaning of the warehouse contents.
The answers that the warehouse will provide are only as good as the questions that are directed to it. As end users gain confidence both in their own skills and in the veracity of the warehouse contents, data warehouse usage and overall support of the warehousing initiative will increase. As the data scope of the warehouse increases and additional standard reports are produced from the warehouse data, decision-makers will start feeling overwhelmed by the number of standard reports that they receive.
Decision-makers either gradually want to lessen their dependence on the regular reports or want to start relying on exception reporting or highlighting, and alert systems. For example, instead of receiving sales reports per region for all regions within the company, a sales executive may instead prefer to receive sales reports for areas where actual sales figures are either 10 percent more or less than the budgeted figures.
Alert Systems Alert systems also follow the same principle, that of highlighting or bringing to the fore areas or items that require managerial attention and action. However, instead of reports, decision-makers will receive notification of exceptions through other means, for example, an e-mail message.
As the warehouse gains acceptance, decision-making styles will evolve from the current practice of waiting for regular reports from IT or MIS to using the data warehouse to understand the current status of operations and, further, to using the data warehouse as the basis for strategic decision-making. At the most sophisticated level of usage, a data warehouse will allow senior management to understand and drive the business changes needed by the enterprise. A successful enterprise-wide data warehouse effort will improve financial, marketing and operational processes through the simple availability of integrated data views.
Previously unavailable perspectives of the enterprise will increase understanding of cross-functional operations. The integration of enterprise data results in standardized terms across organizational units e. A common set of metrics for measuring performance will emerge from the data warehousing effort.
Communication among these different groups will also improve. The very process of consolidation requires the use of a common vocabulary and increased understanding of operations across different groups in the organization. While financial processes will improve because of the newly available information, it is important to note that the warehouse can provide information based only on available data. For example, one of the most popular banking applications for data warehousing is profitability analysis.
Unfortunately, enterprises may encounter a rude shock when it becomes apparent that revenues and costs are not tracked at the same level of detail within the organization. Banks frequently track their expenses at the level of branches or organization units but wish to compute profitability on a per customer basis. With profit figures at the customer level and costs at the branch level, there is no direct way to compute profit.
As a result, enterprises may resort to formulas that allow them to compute or derive cost and revenue figures at the same level for comparison purposes. Marketing Data warehousing supports marketing organizations by providing a comprehensive view of each customer and his many relationships with the enterprise.
Over the years, marketing efforts have shifted in focus. Customers are no longer viewed as individual accounts but instead are viewed as individuals with multiple accounts. This change in perspective provides the enterprise with cross-selling opportunities. The notion of customers as individuals also makes possible the segmentation and profiling of customers to improve target-marketing efforts.
The availability of historical data makes it possible to identify trends in customer behavior, hopefully with positive results in revenue.
Operations By providing enterprise management with decisional information, data warehouses have the potential of greatly affecting the operations of an enterprise by highlighting both problems and opportunities that here before went undetected.
Strategic or tactical decisions based on warehouse data will naturally affect the operations of the enterprise. It is in this area that the greatest return on investment and, therefore, greatest improvement can be found. As mentioned in Chapter 2, return on investment ROI from data warehousing projects varies from organization to organization and is quite difficult to quantify prior to a warehousing initiative.
However, a common list of problems encountered by enterprises can be identified as a result of unintegrated customer data and lack of historical data.
A properly deployed data warehouse can solve the problems, as discussed below. Customers are annoyed by requests for the same information by different units within the same enterprise.
The inconsistent use of terms results in different business rules for the same item. Decision-makers have to rely on conflicting data and may lose credibility with customers, suppliers, or partners. Data gathering is ad hoc, inconsistent, and manually performed. There are no formal rules to govern the creation of these reports.
Business analysts within the organization spend more time collecting data instead of analyzing data. Competitors with more sophisticated means of producing similar reports have a considerable advantage.
Analysis for trends and causal relationships are possible. The enterprise is unable to anticipate events and behave proactively or aggressively. Customer demands come as a surprise, and the enterprise must scramble to react.
Marketing campaigns can be predictive in nature, based on historical data. Enterprise Emphasis on Customer and Product Profitability Increase the focus and efficiency of the enterprise by gaining a better understanding of its customers and products. Perceived Need Outside the IT Group Data warehousing is sought and supported by business users who demand integrated data for decision-making. A true business does not need technological experimentation, but drives the initiative. Integrated Data The enterprise lacks a repository of integrated and historical data that are required for decision-making.
Cost of Current Efforts The current cost of producing standard, regular managerial reports is typically hidden within an organization. A study of these costs can yield unexpected results that help justify the data warehouse initiative.
The Competition does it Just because competitors are going into data warehousing, it does not mean that an enterprise should plunge headlong into it. However, the fact that the competition is applying data warehousing technology should make any manager stop and see whether data warehousing is something that his own organization needs.
The costs associated with developing and implementing a data warehouse typically fall into the categories described below: Hardware Warehousing hardware can easily account for up to 50 percent of the costs in a data warehouse pilot project. A separate machine or server is often recommended for data warehousing so as not to burden operational IT environments.
Hardware costs are generally higher at the start of the data warehousing initiative due to the purchase of new hardware. However, data warehouses grow quickly, and subsequent extensions to the warehouse may quickly require hardware upgrades. As the warehouse grows both in volume and in scope, subsequent investments in hardware can be made as needed.
Software Software refers to all the tools required to create, set up, configure, populate, manage, use, and maintain the data warehouse. The data warehousing tools currently available from a variety of vendors are staggering in their features and price range Chapter 11 provides an overview of these tools. Each enterprise will be best served by a combination of tools, the choice of which is determined or influenced not only by the features of the software but also by the current computing environment of the operational system, as well as the intended computing environment of the warehouse.
Services Services from consultants or system integrators are often required to manage and integrate the disparate components of the data warehouse. The use of consultants is also popular, particularly with early warehousing implementations, when the enterprise is just learning about data warehousing technologies and techniques.
Service-related costs can account for roughly 30 percent to 35 percent of the overall cost of a pilot project but may drop as the enterprise decreases its dependence on external resources. Internal Staff Internal staff costs refer to costs incurred as a result of assigning enterprise staff to the warehousing project. The staff could otherwise have been assigned to other activities.
The heaviest demands are on the time of the IT staff who have the task of planning, designing, building, populating, and managing the warehouse. The participation of end users, typically analysts and managers, is also crucial to a successful warehousing effort. The Project Sponsor, the CIO, and the Project manager will also be heavily involved because of the nature of their roles in the warehousing initiative. Table 3. The typical risks encountered on data warehousing projects fall into the following categories: These risks relate either to the project team structure and composition or to the culture of the enterprise.
These risks relate to the planning, selection, and use of warehousing technologies. Technological risks also arise from the existing computing environment, as well as the manner by which warehousing technologies are integrated into the existing enterprise IT architecture. These risks are true of most technology projects but are particularly dangerous in data warehousing because of the scale and scope of warehousing projects.
Data warehousing requires a new set of design techniques that differ significantly from the well-accepted practices in OLTP system development. Considering its scope and scale, the warehousing initiative should be business driven; otherwise, the organization will view the entire effort as a technology experiment.
A strong Project Sponsor is required to address and resolve organizational issues before these have a choice to derail the project e. The Project Sponsor must be someone who will be a user of the warehouse, someone who can publicly assume responsibility for the warehousing initiative, and someone with sufficient clout. This role cannot be delegated to a committee. Unfortunately, many an organization chooses to establish a data warehouse steering committee to take on the collective responsibility of this role.
If such a committee is established, the head of the committee may by default become the Project Sponsor. Unlike OLTP business requirements, which tend to be exact and transaction based, data warehousing requirements are moving targets and are subject to constant change. Despite this, the intended warehouse end-users should be interviewed to provide an understanding of the types of queries and reports query profiles they require. By talking to the different users, the warehousing team also gains a better understanding of the IT literacy of the users user profiles they will be serving and will better understand the types of data access and retrieval tools that each user will be more likely to use.
The end-user community also provides the team with the security requirements access profiles of the warehouse. These business requirements are critical inputs to the design of the data warehouse.
Senior Management Expectations Not Managed Because of the costs, data warehousing always requires a go-signal from senior management, often obtained after a long, protracted ROI presentation. In their bid to obtain senior management support, warehousing supporters must be careful not to overstate the benefits of the data warehouse, particularly during requests for budgets and business case presentations.
Raising senior management expectations beyond manageable levels is one sure way to court extremely embarrassing and highly visible disasters. End-User Community Expectations not Managed Apart from managing senior management expectations, the warehousing team must, in the same manner, manage the expectations of their end users. Warehouse analysts must bear in mind that the expectations of end users are immediately raised when their requirements are first discussed.
The warehousing team must constantly manage these expectations by emphasizing the phased nature of data warehouse implementation projects and by clearly identifying the intended scope of each data warehouse rollout. Political Issues Attempts to produce integrated views of enterprise data are likely to raise political issues.
In other enterprises, the various units want to have as little to do with warehousing as possible, for fear of having warehousing costs allocated to their units. Understandably, the unique combination of culture and politics within each enterprise will exert its own positive and negative influences on the warehousing effort. A number of factors increase the logistical overhead in data warehousing. A few among are: Highly formal organizations generally have higher logistical overhead because of the need to comply with pre-established methods for getting things done.
Elaborate chains of command likewise may cause delays or may require greater coordination efforts to achieve a given result.
Logistical delays also arise from geographical distribution, as in the case of multi-branch banks, nationwide operations or international corporations. Multiple, stand-alone applications with no centralized data store have the same effect. Stephan Trahasch in his web site. Kx to power analytics for US real estate giant Zillow. Redisconf19, April , San Francisco. Financial Evolution: Concepts and Techniques , Jiawei Han and Micheline Kamber About data mining and data warehousing Mining of Massive Datasets , Jure Leskovec, Anand Rajaraman, Jeff Ullman The focus of this book is provide the necessary tools and knowledge to manage, manipulate and consume large chunks of information into databases.
The Elements of Statistical Learning , Trevor Hastie, Robert Tibshirani, Jerome Friedman This is a conceptual book in terms of data mining and prediction with a statistical point of view.
Covers many machine learning subjects too. An Introduction to Statistical Learning: The exploratory techniques of the data are discussed using the R programming language. Data Science for Business , Foster Provost, Tom Fawcett An introduction to data sciences principles and theory, explaining the necessary analytical thinking to approach these kind of problems.
It discusses various data mining techniques to explore information. Modeling With Data This book focus some processes to solve analytical problems applied to data. In particular explains you the theory to create tools for exploring big datasets of information.
Value Creation for Bus… On this resource the reality of big data is explored, and its benefits, from the marketing point of view. It also explains how to storage these kind of data and algorithms to process it, based on data mining and machine learning.
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