SQL Server Reference Guide

Mar 28, 2003

␡

⎙ Print

< Back Page 331 of 365 Next >

It might seem a bit strange to begin talking about data elements this far along in my series on creating a Business Intelligence landscape. I've already covered the concepts of the sources where the data originates to the Operational Data Store (ODS), using the already explained the Extract, Transform and Load process, and also how to think about your Data Marts. All of those systems contain tables with multiple columns. Aren't they full of data elements already?

The answer is that these storage areas do have data in them, and you should have created their structures using the design information from the requirements gathering phase. But the data elements I'm talking about now are those that you will use in a Data Warehouse, or the collection of Data Warehouses that comprise an Enterprise Data Warehouse. The processes you use to design these elements are different than those used in a transactional reporting system.

It may be helpful to describe exactly what the Data Warehouse should store. There are two schools of thought on the purpose for the Data Warehouse. The first is that it should be exactly what it sounds like – a single place to house all of the data a company has.

The issue with this view is what to do with the system when you're done. In the last tutorial I explained that you will need to combine data from various systems into the Data Warehouse to be relevant. In a system that tracks clothing sales, your data might have the value "Jacket, Men's" in one source and "Men's Coats" in another. Often it's not a simple matter of making the values equal, because in certain cases they aren't. The end result of storing both descriptions is that your users would have to manually tally each set to come up with a grand total, assuming they knew the two were the same. That's just not possible, even though you've met the goal of storing all the company's data in one place.

So in the first system, you have to begin the arduous task of mapping or transforming the detailed data to come up with a single set of data that is relevant to the users. Even then, storing so much detail makes the system very large and slow to process queries. Not only that, this is a redundant use of data. You already have all that detail out in the Data Marts and the Operational Data Store. If you need a detailed report, you can always get it from there.

The second definition of a Data Warehouse is exemplified in the goal for a Business Intelligence landscape that I've been repeating in the previous tutorials. What we're trying to create is a set of consolidated, aggregated, strategic data presented in an analytical format to upper management. The Data Warehouse part of that system provides the aggregated, strategic and analytical parts of the definition. The sources, ODS and Data Marts are how we get the data staged in a meaningful way.

So that is how we'll proceed with the data element design process for the Data Warehouse. The outline for the process is to find out what aggregated strategic data we need, and the best way to format it. From our definition, we also have to think like upper-level management. What do they really want to see? Do they need to know how many paperclips someone in a regional office bought, or whether that office is adhering to the business supplies budget? Do they care about what supplier we used for parts, or that we are getting a 10 per cent reduction in costs for the same quality? And how are we measuring quality?

What this means is that we are really after only two types of data: Things that we want to measure, and the measurements. I'll explain the star schema that uses these concepts in the terms dimensions (for the things we want to measure) and facts (for the numbers showing the measurements) in the next tutorial, but for now, we need to concentrate on getting this data from our Data Marts.

So to begin our design we first ask the managers the question, what do we want to measure? I've given you a couple of examples already, and you can probably think of more for your own organization. For a pharmaceutical firm you might find dimensions such as chemicals, trials, marketing, efficacy, lifespan, sales and so forth. For a marketing company dimensions might include region, time, advertising method and the like. In all cases you care about time, since strategic analysis is rarely useful without it.

For the data involving the facts, examples include the number of items sold, moved, changed or produced. Other numbers might be customers contacted, amount donated, total fees charged and so forth. Any numerical aggregate that applies to the dimensions I mentioned a moment ago are applicable to the facts data.

Now that you know the type of data you're after, how do you find out what it is at your organization? You can use the same process as you did when you perform any requirements analysis, with a slight twist. In most requirements gathering exercise, the form is quite rigid. You ask the users what kind of reports they want, how they want them formatted and so on. You then decompose those requirements into tables and views. This is the process you followed for the ODS and perhaps even the Data Mart.

In the Data Warehouse, however, you're going to provide a tool to upper management that is more freeform in nature. You may still produce reports, but the main power of this type of system is in the ability to ask questions and flip them around, on the fly. Interview your upper-level management and ask them what they want to know. What are the reports that they ask their staff to produce every month? Once they get those reports, what do they do with them? You'll find that most of the time they are culling through the reports trying to ferret out one or two answers, so that they can base their strategy on sound numbers.

Rather than manually collating this data, wouldn't it be better for managers to ask the question they are really after, and be able to look at the number right away? That's what you need to design as the data element.

Once you have those dimensions and facts, you'll need to determine where they are buried in all the data sources you have. If you designed your ODS and Data Marts properly, you should be able to get the measurements from there, after summing and grouping the data.

But what if the elements aren't easily discerned from the data you already have? What if the manager is asking for something that you don't store directly? In the last tutorial, I mentioned that when you begin to combine and transform data, you are assigning meaning. That meaning should be defined by the business, not the technical staff. This is the crux of the design effort. You'll need to involve managers from the level just below the top and then work your way down to the line managers. Along the way, each will give you a more detailed description of the measures the top managers are looking for. From there, you can build your model.

While this sounds simple, there are a few problems you'll encounter along the way. The first is that aggregation and combination problem we discussed earlier. In reality, the primary issue there is one of definition, and you can overcome that by asking the managers to set a business owner for the element you're designing. Only that manager can state what the element means, which will give you the description of how to aggregate it.

The second problem comes when top management isn't aware that regions, locations or plants are doing things differently than other places. As the "data detective" you'll uncover all manner of unseemliness, and the only thing you can do in that case is have your Business Analyst diagram the business processes used at each location to vet the information from a business perspective. Let the managers sort that out; that's what they get paid for. Your job is to report the data, set up the elements in a cohesive fashion. Present the business process analysis to the managers, and explain the difficulty in combining disparate processes into a single meaning. Ask them how to proceed, get a Business Owner to sign off, and design the element with that definition.

In the next tutorial I'll show you a little more about dimensions and facts, and how you can design your Data Warehouse.

InformIT Articles and Sample Chapters

Jill Dyché has a great book in the bookstore called e-Data: Turning Data Into Information With Data Warehousing. It has a very clear description of Business Intelligence and how various firms can apply the data they've collected.

Online Resources

Microsoft has a great resource for Data Warehousing over at MSDN. You can read more here.

< Back Page 331 of 365 Next >

🔖 Save To Your Account

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Privacy Notice

Overview

Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information

To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information

Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security

Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children

This site is not directed to children under the age of 13.

Marketing

Pearson may send or direct marketing communications to users, provided that

Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
Such marketing is consistent with applicable law and Pearson's legal obligations.
Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information

If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out

Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information

Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents

California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure

Pearson may disclose personal information, as follows:

As required by law.
With the consent of the individual (or their parent, if the individual is a minor)
In response to a subpoena, court order or legal process, to the extent permitted or required by law
To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
To investigate or address actual or suspected fraud or other illegal activities
To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links

This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact

Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice

We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020

Email Address