Q&A: Chosing the right time dimension for a Snapshot

In this Q&A, a reader asks how to designate a time dimension for a fact table that is a snapshot.

Q: We are designing a new star. The fact table is a monthly snapshot. It will capture several metrics each month for each open account. My question is how to handle the time dimension. We cannot agree whether it makes more sense to have a month dimension, or to use a month end date from a day dimension. What makes the most sense?

- Name withheld by request

First, I want to commend you for stating the grain of your fact table, and stating it clearly. Grain confusion can cause many problems, and makes a question like yours even more difficult to answer. Snapshots always have a time component to their grain; in your case it is monthly. Your fact table will have one row per account per month.

Now to your question: I strongly prefer the use of a month dimension for a monthly snapshot. This approach is non-ambiguous. It is self-evident what each row represents; the chance for any confusion is minimal.

When a monthly snapshot is designed with a day dimension, the designers are generally clear on what each row represents. However, report developers or other users may misinterpret the data now or in the future.

Designers who use a day dimension with a monthly snapshot generally choose the last day of the period represented, as you suggested. The problem with this is that there are many elements of a typical day dimension that simply do not apply to a monthly summary. Tor example, a snapshot representing March 2009 would have the day 3/31/09 associated with it. The day dimension has a lot of information about March 31, 2009 that does not apply to the snapshot, such as what day of the week it was, whether it was a holiday, and so forth. It would be all too easy to misinterpret this information as being applicable to the facts.

Even if everyone is clear on this at the start, a year or two down the road there are likely to be new members of your development team who may misinterpret the data. The presence of a date can also lead to confusion about what a row represents. If your monthly summary
contains an average daily balance, for example, a user may confuse this for a balance as of the day in question.

Confusion will also arise over what date to use for the current period. During March 2009, you may be updating the snapshot rows for each account on a daily basis. Using a day dimension, ETL developers may assign the particular date in March, which now serves double duty -- designating the snapshot period and the date loaded.

Sometimes, there is a strong desire among technical team members to use a day dimension with a monthly snapshot. If you cannot convince them otherwise, then you can avoid some confusion by creating a view for use with the monthly snapshot that hides the day-level attributes. You may be able to do the same kind of “hiding” of non-applicable dimension attributes within your BI tool. Use the month-end date for each fact table row. If the ETL team wants to track the day each row was loaded, they can do this with a second relationship to the day dimension. (This trick works nicely even if you do go with a month dimension.)

Regardless of your approach, I also advise you to name the facts carefully. If you want to include a balance as of the end of the period, name it period_end_balance. An average daily balance for the period in question should be called average_daily_balance. A column name like balance is simply too vague, and bound to be misused.

Thanks for your question!

- Chris

If you have a question, click my profile picture at the top of the page for my address. I try to respond to all messages, and may even post an answer here.

Q&A: Customers with No Sales in Fact Table

In this post, Chris answer a reader's question about reporting on customers with no sales in the fact table.

New Q&A Feature

I get a lot of questions about dimensional modeling from readers of the blog, my books, and people who attend my classes. I try to answer all of them. I thought it would be interesting to post some of them here, for all to read.

This week, I'm answering a question about reporting on customers with no sales.

In general what is the recommended approach for reporting customers that have no sales in the fact table for MTD or YTD? Or, for that matter, any dimension member where no fact exists for some time period?

- J. Dolan

Usually, something like “customers with no sales for a period” is computed as part of the reporting process. Developers often bristle at this kind of report, because it requires using a correlated subquery. This makes it difficult to do with a SQL-generating BI tool, and can be slow. And if you want to use the list of customers with no sales as a filter for another query, things can get really hairy.

An alternative is to pre-compute this information, storing the results in a table that summarizes reporting periods. This is likely to be a factless fact table, with foreign keys to dimension tables representing the period summarized and the dimension in question--customers in your case. If you require monthly and yearly slices, you will want to multiple summaries. This approach may simplify reporting, but adds complexity to the ETL process.

If current period is the only period of concern, as in your question, then it is also possible to pre-compute the information and store the result directly to the customer dimension table, as a flag. The flag would specify whether the customer has placed an order during the current period. In your case, it would be two flags -- one for month to date and one for year to date.

This kind of attribute is called a behavioral dimension because it takes behavioral activity recorded in the fact table and transforms it into a dimension. Behavioral dimensions are very powerful, because they can be used as simple filters for other queries.

Again, this concept trades ETL complexity for reporting flexibility and performance. But isn't that what data warehousing is all about?

Thanks to J. Dolan for agreeing to have his question posted. If you have a question, send it in. I try to answer all my email, though it sometimes takes a while!

Chris

Coming to TDWI New Orleans? Vote First!

If you'll be joining me at the TDWI conference in New Orleans during election week, make sure you vote ahead of time. Here's how.

This election day, I'll be teaching Advanced Dimensional Modeling at The Data Warehouse Institute World Conference in New Orleans. I was a bit reluctant to accept the invitation at first, out of fear that it would prevent my vote from being counted. But after doing a little bit of research, I accepted with confidence.

You can attend with confidence as well. All you need is an absentee ballot.
Absentee voting procedures vary by locality. To find out how it works where you live, you can go to GoVoteAbsentee.org.

Plug in your zip code, and they'll provide you with information on how to apply for an absentee ballot. They'll even give you a link to download the ballot application. If you prefer, you can contact your county or city government for information.

The process is simple, and you can be sure your vote will be heard. Some localities even permit residents to cast absentee ballots in person. In Fairfax county Virgina, where I live, you can personally feed your ballot into the optical scanner that tallies the votes.

And as an added bonus, you won't have to stand in line on election day!

I have already cast my absentee vote. If you will be traveling to The Data Warehouse Institute Conference in New Orleans, I urge you to do the same. Go to GoVoteAbsentee.org now, and find out how.

- Chris