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November 29, 2009

Consume: The 4th C

A data warehouse, as with other technology solutions, is a partnership of those who build it and those who use it. Both have a responsibility for the success of the data warehouse. Therefore, consume is the fourth "C" of data warehousing.

If the sound of a falling tree occurs in the woods and no one is there, did the tree fall?

The data warehouse is fundamentally a communication tool. The information that it conveys through its data and the representation it provides of business activities can be incredibly valuable. All the work that goes into collecting, correlating, and conveying information in the data warehouse is wasted if the right decision makers are not consuming the information. Consumption is not only about the technical ability for some abstract decision maker to use the data warehouse for analysis. Rather, it is about ensuing that each enhancement to the data warehouse helps to drive adoption and increase both strategic and operational use of the system. As users have a positive experience that aligns to their immediate business need, they're more likely to come back to the data warehouse with their next question, rather than striking off on a journey to collect information from various systems, correlate that data together, and then convey that information to decision makers and senior leaders.

(Repost from: Sharpening Stones)

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Posted by Paul Boal at 9:45 PM | Comments (0)

November 28, 2009

Convey: The 3rd C

If a tree falls in the woods and no one is there, does it make a sound?

One of the primary responsibilities of the data warehouse is to take information that would otherwise not be available and deliver it to decision makers and analysts who can do something valuable with the information and make the world a better place. Perhaps you might think that's a bit of a stretch, or perhaps I'm being overly optimistic. However, my position is that we are more likely to make decisions that move society forward if we have more knowledge about the context in which we're making those decisions. So, even if we don't always use better information to make better decisions, we couldn't even have the opportunity to try to make decisions if not for tools like the data warehouse.

Effectively conveying information is a factor of the format of the information, performance of the system, the accuracy of the content, and the ease of use with tools that will be used to access the information. Most importantly, the data warehouse has to be able convey to users, in a natural way, how to find the information they need and what aspects of the physical implementation impact how the system can and can't be used.

(Repost from: Sharpening Stones)

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Posted by Paul Boal at 9:30 PM | Comments (6)

November 26, 2009

Correlate: The 2nd C

Collecting information from a variety of sources is powerful in and of itself, but having an integrated collection of data makes analysis across business processes, subject areas, and source systems many times more efficient. The data warehouse must provide an access layer that allows users to easily and naturally merge together pieces of business data that are naturally related in the business model. There are some aspects of correlation that are business driven and the data warehouse will be a victim or beneficiary of. For instance, the consistent assignment of employee numbers across applications or an enforced uniqueness of cost centers across multiple financial systems or the corporate data governance decision that only one systems can be considered the authoritative source for any particular data element.

Other correlation activities are ones that can be supported directly within the data warehouse environment:

* Code set translations ensure that a user can reference any data source using the approved corporate standard code set for some particular attribute.
* Cross-reference translates that allow two different identifiers for the same particular entity to be combined together.
* Transformation of one representation of a particular identifier into a matching representation somewhere else. For instance, if one system uses a 15 digit cost center that includes accounting unit, account, and sub account, the data warehouse should take that 15 digit string, break it into components, and match those against the information from the finance system.

Having a data warehouse to preexecute these kinds of correlation activities once and store the results for convenient use in tens of thousands of queries every month or even every day is a huge benefit to analysts through clear communication and improved performance.

(Repost from: Sharpening Stones)

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Posted by Paul Boal at 9:30 PM | Comments (0)

November 24, 2009

Collect: The 1st C

The 4 C's of Practical Data Warehousing take a very open approach to describing what a data warehouse is responsible for doing. Perhaps, the function that can be interpretted most broadly from a technical perspective is the first C: Collect.

The first job of a data warehouse is to bring together, in one business tool, the information that an analyst, knowledge worker, or decision maker needs to understand business operations. Whether that collection of information is a literal copy between data stores, a federation of databases, or a more complicated transformation of transactional data into a dimensional model is merely an implementation detail. Some of those choices are more or less practical depending on the underlying systems and data (which we'll discuss later), but to be of any value, the data warehouse has to do th collecting of information together so that users don't have to. One of the key benefits of data warehousing is the ability of an analyst to have a one-stop-shop for most of the information they need to do their analysis. In analytically immature organizations, analyst will typically spend 80% of their time collecting data and putting it into some kind of local data store (which might anything from flat files to MS Access to small databases) and only 20% of their time doing analysis on that data. One of the goals of the data warehouse is to flip that ratio so that knowledge workers are able to spend 80% of their time analyzing business operations and only 20% of their time retrieving data from as few different sources as necessary.

When various analysts from different departments (marketing, strategic planning, sales, finance, etc) all ask the same people for the same data on a continual basis, it also prevents the application teams from having time to make improvements or plan upgrades to the applications themselves. There are still organizations that have multiple staff members dedicated to the work of fulfilling data extract requests to support internal analytical needs. A data warehouse that collects the information together once, into a common enterprise model of business activities, satisfies all of those individual departments with one extract from each source system and a consolidated environment in which to do their analysis.

(Repost from: Sharpening Stones)

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Posted by Paul Boal at 9:30 PM | Comments (0)

November 21, 2009

The 4 C's of a Practical Data Warehouse

Anyone shopping for an engagement ring is familiar with the 4 C's of diamond quality: cut, clarity, color, and carats. While there are other things that make one diamond better or worse than another, these are the most commonly used.

So, what are the 4 C's of data warehouse success:

* Collect
* Correlate
* Convey
* Consume

These are the 4 things that a data warehouse has to be most effective at to achieve success. Over the next four posts, I'll describe what each of these represents and ways that you can measure the quality of your own data warehouse implementation against these.

(Repost from: Sharpening Stones)

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Posted by Paul Boal at 9:30 PM | Comments (0)