Practical Advice About Data Warehousing
A data warehousing effort requires both theory and discovery. Although the theory associated with building a data warehouse could be considered a rather well understood topic today, practical experience still has much to offer. In this section, we’ll look at a few of the data warehousing best practices that we have found most valuable.
Anticipating and Rewarding Operational Process Change
It is almost certain that a data warehousing effort will identify data elements and relationships essential to realizing the enterprise goals that are not currently captured in the operational processes. It is also likely that those who would be most directly affected in their day-to-day work by addressing this will feel that they have nothing to gain by doing so, and often something to lose. For example, an enterprise goal might be to capture which sales groups should get credit, and in what proportion, for working together to make a sale happen—the better to apportion the bonus pool of the sales force. Enabling this requires capturing information about which sales groups were involved at the time the sales transaction is recorded. This is information that is likely not currently available in the workflow of the back-office staff who record the transaction, and moreover, even if it is (or is made to be), the extra time it would take them to record it will reduce the number of transactions they can process per hour. They will most likely resist, given the impact on their productivity, unless this effort is officially recognized and proper incentives are put in place to motivate their cooperation.
Rewarding Giving Up Control
As suggested earlier in this chapter in the section “Business Intelligence,” a successful data warehousing/BI effort often requires those who have traditionally been in control of key data to relinquish that control in the interest of the greater good. Any organizational change effort will threaten those who perceive themselves the losers in some way (often correctly), and it is only natural for them to resist the change. If the enterprise recognizes this and provides positive motivators to take this risk, the chances of success are increased. How feasible this is, of course, depends greatly on the organizational culture. The BSC approach can be particularly valuable in this regard.
A Prototype Might Not Work to Sell the Vision
Building a prototype or proof of concept (POC) for a data warehousing/BI approach is often recommended as a way to achieve buy-in from important stakeholders. It is easy to assume that a representative POC will do the trick. By representative, we mean that the important technical capabilities are demonstrated as feasible (such as whether particular relationships can be modeled successfully), even if this is illustrated with fictitious data such as the AdventureWorksDW2008 database.
What you might not realize until it is too late is that stakeholders can find it difficult to appreciate such an approach, particularly when the POC is not based on measures they recognize or the values used are not realistic. If you hear people in your audience calling out “Hey, that number isn’t right!” while you are demonstrating the POC, that’s exactly what’s happening. Logically, in a POC, it might not matter whether the data is accurate, but once your stakeholders lose interest or faith, it can be very difficult to regain. Focusing on such issues is also a favored tactic of those who oppose the data warehouse for whatever reason.
For a POC to have the best chance of success, it should be as realistic and as attuned to the work of the stakeholders who will be judging it as possible. This often runs counter to the idea that a POC requires a minimal investment, which is exactly why we are making this point. The data warehousing project can get shot down before it even gets off the ground with an ill-conceived POC.
Surrogate Key Issues
The value of using integer surrogate keys in a data warehouse was discussed earlier in this chapter in the section “Data Warehouse Design.” But their use is not without issues, as described here:
In general, surrogate keys should not be “smart”—that is, they should not have any significant meaning encoded in their values. However, an exception might be worth considering for the Time dimension. At the physical level, there can be value in the Time surrogate key taking the form YYYYMMDD, YYYYMM, or YYYYWW (where Y, M, D, and W are year, month, day, and week values), all of which are easily represented as an integer. Two reasons justify this violation of the normal best practice. First, if the Time surrogate key column is the first in the composite primary key of the fact table (as it usually should be) and the primary key has a clustered index, the fact data will be optimally organized for the Time constraint of the typical query—which is usually either a point in time or a range. Second, such a smart Time key will make it much easier to implement and maintain physical partitioning of the Time dimension at the relational database level.
Surrogate keys can be generated in several ways, two principal ones being IDENTITY columns or a row-by-row assignment facility—for example, SELECT MAX(Id) + 1—using appropriate locking mechanisms. Regardless of the method, complications can arise in the typical multienvironment setting—that is, development, quality assurance (QA), and production. Assume that at the start of a development cycle, your development environment is refreshed from production. Then you also copy over ETL input files from production and run the ETL process in development (perhaps as part of a parallel test). Depending on how surrogate keys are assigned, there can be a good chance that the same data (from a business key perspective) is assigned different surrogate keys in development and production. This can greatly complicate reconciliation between the two.
Currency Conversion Issues
Particularly in larger, multinational enterprises, financial applications usually require currency conversion in order to compare similar items (apples to apples). Be aware that this is a subject fraught with business rule and design conundrums. Since SQL Server 2005, Analysis Services has provided features that can make implementation of currency conversion calculations in the cube easier.
But this does not address the issues we want to highlight here, which relate to the tension between designing for ad hoc, not-known-in-advance queries and needing to know something, possibly a lot, about likely queries, if a suitable design is to be derived. Issues around currency conversion illustrate this particularly well. There are no “right” answers to the following questions, but you would do well to consider all of them if currency conversion is in any way a part of your business perspective:
What flexibility is required? Will there be one master currency in which all comparisons are expressed, several standard currencies, or in any existing currency?
Closely related to the preceding questions, does it make sense to precalculate and store converted amounts, or must this be done on the fly?
As with all rates and ratios, care must be taken where aggregation is involved to force the currency conversion to be at the appropriate leaf level of detail, followed by aggregation to the required summary level. The capabilities of your OLAP tool influence this greatly.
Are converted amounts to be at the rate in effect at their original point in time only, or should amounts also be convertible based on the rates at any point in time?
At what rates should future values (for example, Budget) be converted: the rates in effect when the budget is finalized, never after to be adjusted? Or should current rates be used, adjusting the projections every period? Must you be able to distinguish how much of a variance between Actual and Budget is due to currency conversion vs. changes in the Budget measure itself?
The design driven by answers to these business questions has profound effects on both the questions that can be answered later and the technical complexity required.
Events vs. Snapshots
There are two complementary approaches to data warehouse logical design: the event-driven approach and the snapshot approach. Both involve tradeoffs in complexity and in the sort of inquiries they can support.
On the one hand, it can be argued that everything of analytical interest in an enterprise can be represented as an event. Events are items like a payment or an order being received or a shipment getting delivered. Events by definition occur asynchronously at points in time. In principle at least, if all relevant events can be identified and captured, it is possible to deduce the state of affairs at any point in time, as well as how that state came to be. For some informational applications, this is critical. Constructing the point in time from events can, however, be exceedingly complex.
On the other hand, a snapshot-based approach does not record events at all. Instead, it simply periodically records the aggregate effect of events. Answering queries about the points in time where snapshots were taken is obviously much easier than it would be with a purely event-based approach, where the state at the point in time would need to be reconstructed.
These approaches sometimes need to be combined. For example, with an Account entity, often the only thing of interest is the account balance at periodic points in time, such as month-end. On the other hand, it is also imperative to be able to query each and every event (debit or credit) that affected the balance since the previous snapshot.
Events and snapshots have considerations in addition to which functional questions they support. There is the question of what the source system can provide in terms of either events or snapshots, which has an impact on how much work must be done in the data warehouse ETL to create one or the other. Also, a snapshot approach that takes a snapshot of everything, regardless of how much or little has changed since the last snapshot can lead to data proliferation and can be inefficient compared with an event-based approach when changes are relatively few—although this can be addressed with techniques such as Change Data Capture, detailed later in this chapter.
It is well worth spending considerable time during the design phase thinking through the implications of both approaches before determining the best choices for your requirements.