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Archive for August, 2009

Understanding the Business Cycle

Aug 31

Posted by Tod McKenna in Decision Support

There’s been a lot of talk lately about how the US economy is crawling out of recession. You may have heard terms like “bottom” and “trough”, seen graphs of GDP growth, and read articles referencing NBER.

You may be highly skeptical and unwilling to believe that the latest recession is a thing of the past; after all, you likely know someone still looking for a job and you likely know someone else who recently lost a home. Or, you may be very optimistic, and knowing how the business cycle revolves, you are beginning to invest and think about growth. Understanding the business cycle will help you make better decisions — individually and in business.

Off the bat, I should state that economics is not an exact science (but you likely knew that already). And while there are a few axioms (supply/demand, money supply) and indicators (GDP, unemployment) that can help paint a realistic picture, most of what you see and read is based on analyst insight and experience, backed up with historical data and predictive models. There are faults all along the way. When you consider the complexity of the global economy, it should be clear that economics is more of an art than a science. The Business Cycle, though, seems to be something you can count on.

What is the Business Cycle?

Plainly put, the Business Cycle represents 4 phases of aggregate economic movement, ranging from periods of high growth to recession and back again. John Maynard Keynes (d. 1946) referred to these cycles as “waves of optimism and pessimism”, or to put it another way, waves of expansion and contraction. These phases were first written about more than 50 years ago by Arthur Burns in his book “Measuring Business Cycles”.

The four phases

  1. Peak - The highest point of economic output just before a downturn
  2. Recession - When the economy actually shrinks, or contracts
  3. Trough - The “bottom”
  4. Recovery - The economy has stopped shrinking is growing once more

Last 5 US Business Cycles

Peak YYYY-MM Recession Period Trough YYYY-MM Recovery Period
1980-01 6 1980-07 12
1981-07 16 1982-11 93
1990-07 8 1991-03 120
2001-03 8 2001-11 73
2007-12 ? ? ?

Data Source: NBER

Notice that peaks and troughs are represented by month and year, while the other two phases are measured over a number of months. You’ll also notice that recession and recovery periods can vary greatly.

Who determines when each phase begins and ends?

In the US, the task is managed by the National Bureau of Economic Research (NBER). NBER is a private, nonprofit, and nonpartisan research organization (stocked with Nobel Prize winning economists). They work on many economics projects and work closely with businesses and universities. They self-proclaim their dedication to promote “a greater understanding of how the economy works”.

For example, NBER most recently concluded that “the last [US economic] expansion ended in December 2007″. We know that after such expansion, according to the Business Cycle, will come a period of recession, followed by a bottom, leading to a new period of expansion.

Does everyone agree?

In short, nope. Milton Friedman, to name one prominent example, believed that the economy fluctuates rather than cycles. The new classical framework states that the economy is much more flexible than that which is implied by the business cycle framework.

There’s also an issue of market equilibrium. Having a somewhat predictable business cycle implies that the markets will be out of sync quite a lot — allowing some speculators and investors to take advantage of price differences at different phases of the cycle (this is called arbitrage, take a look into Rational Expectations Theory as well). Other differing methodologies include the credit/debt cycle, political cycles, and Marxian cycles.

Final thoughts

Even against dissenter argument and alternate viewpoints, the Business Cycle framework still works and is easily observable. Economists at NBER continue to assign dates to peaks and troughs. Individuals, businesses and organizations still base many of their purchasing and hiring decisions on what phase we’re currently in. This likely won’t change any time soon.

The only problem with relying on NBER is that they lag reality. For example, we have very likely reached the bottom of the current cycle and are now in a phase of recovery. NBER might make it official at some point this year, maybe next year. Investors waiting for official announcements will find that they’re missing the boat.

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Data Warehousing ETL Checklist

Aug 27

Posted by Tod McKenna in Data Management

IT Business Edge writer Michael Stevens recently published a document called the “Data Warehousing ETL Checklist” (link to article with document). To get the file you’ll need to register with ITBusinessEdge (if you don’t already have an account).

The document isn’t a development checklist, but rather “a high-level checklist of the important topics”. Stevens has made a good effort to distill the various data integration topics and issues you’ll need to consider before you start your project.

Highlights and feedback

Mind scope and do not “try to boil the ocean”. Good advice. But I disagree with Stevens on how to approach one aspect of the scoping issue. He suggests that “in some cases, it may be better for the business as a whole to leave certain data sources untouched”. As I wrote here, I don’t think that scoping to the data source is a great approach. But you can decide.

I would also add that instead of looking at “Target System Content”, you should focus specifically on Business Processes — and this is regardless of the type of system your integrating into (MDM, DW, OLAP, etc.).

Data Profiling is a very important topic, and I’m ecstatic to see it on the list. But if it were up to me, I would have made it second or third. Almost every single failed or over budget ETL project can be linked to data source woes (quality, data availability, inadequate documentation, etc.). Most of these woes can be relieved by conducting good data profiles — a process that can begin during the initial requirement gathering phase of the lifecycle.

The data profile will reveal which source systems are useful. There may be redundancy, data quality, and data access issues compelling you to exclude a particular source. This isn’t a scoping issue, though. Go back to the project sponsor and to the board; report your findings. You may have other issues in your organization to resolve first.

In addition to Data profiling, I’m very happy to see that Naming Conventions made the list. Tied in closely with naming is the concept of conformity. I’ve written about this before, so please check here. In general, a label and its data must conform and be consistent throughout the entire system. And as Stevens points out, they must be agreed upon by the users.

For metadata, I also consider a third type: Process Metadata. This is technical data about all the processes (ETL, jobs, automated processes, reports, etc.). This data is then utilized directly by business users and administrators to monitor and audit the data warehouse. Is my report ready? consult the process metadata. Did that job finish? consult the process metadata.

Lastly, don’t forget to think about auditing, compliance, and lineage. If you’re making decisions based on the data retrieved from your data warehouse, you’ll need ways to trace data lineage. You’ll need to know when data was loaded, what version of the ETL system and business logic was used, and even why a particular data item might have been changed in transit.

Conclusion

This is a good high level checklist that you can use before you begin to dig deeper. It gives you a snapshot of some of the high-level decisions you’ll need to make before you get started with your next data warehousing project.

Perhaps Stevens will incorporate some of my feedback into a version 2!

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Scoping Data Warehouse Initiatives

Aug 12

Posted by Tod McKenna in Business & IT Issues

focus Scoping Data Warehouse InitiativesData warehousing is a complex operation. From start to finish (if there is a finish), project teams are faced with many challenges. In all phases of the lifecycle, there are opportunities for derailment. The best way to mitigate potential issues and stay on time and within budget is to carefully define and manage scope. Managing scope can be an ongoing struggle (especially if requirements are not clearly defined or justified). While this is really a PM101-type of topic, I feel there are some fine points in a DW/BI environment that are not mentioned enough.

Consider the following:

Programs verses projects

I won’t get into a deep PM discussion here, but it is important to point out that data warehousing (or business intelligence, master data management, etc.) initiatives should be thought of as programs and not projects. This mindset will help in scoping.

A program (which might also be called a “project portfolio” in some circles) is basically just a set of related projects. With a program, the emphasis is on organizing, prioritizing, and allocating resources to the right projects. Program scope is more strategic, and answers long-term questions about what type of value the organization hopes to achieve from the initiative.

A project, on the other hand, is much more specific — with a set number of deliverables and goals that have a high immediate impact. The scope at the project level is therefore more tactical in nature: high impact, fast delivery. Be aware that some projects may never be given the green light (for example, if there is a low business impact or if there is a low feasibility rating because of data source or data quality complications).

What I find odd is that organizations still choose to tackle immense data warehousing initiatives in one or two shots, trying to deliver everything at once over a period of 18 or more months. This is the wrong approach (here’s why). Break this large initiative into individual projects and try to deliver functionality every 6 to 8 weeks.

The business process

The best way to break down data warehousing programs into high-impact projects is along business process lines. A business process, as defined here, is:

The complete response that a business makes to an event. A business process entails the execution of a sequence of one or more process steps. It has a clearly defined deliverable or outcome. A Business Process is defined by the business event that triggers the process, the inputs and outputs, all the operational steps required to produce the output, the sequential relationship between the process steps, the business decisions that are part of the event response, and the flow of material and/or information between process steps.

Some example of the above: inventory tracking, Internet sales, retail sales, marketing, tax assessment, tax collection, pitching, batting.

In any data warehousing environment, you can expect to have several business processes to model. Each business process you tackle will have elements touching upon different aspects of the data warehouse, including infrastructure, middleware, data modeling, ETL, business logic development, presentation elements, and so on. If you scope each project to the business process, you can deliver complete solutions in the shortest amount of time. (It should be obvious that the very first business process you implement will take the longest, as the team works out the core infrastructure. Most of this infrastructure will be reused by other business processes.)

Avoid scoping to a data source

Do not fall into the trap of scoping to a data source. Scoping to a data source is almost guaranteed to deliver mediocre outcomes. These projects typically include many unfinished or inadequate business processes all delivered at once some time in the distant future and long after the excitement over the initiative has subsided.

While it is true that only one or two data sources might exist in some organizations, it is not true that inventory, customers, sales, procurement, shipping, and other business processes need to be taken on at once. Create a single project for each business process, prioritize based on impact and feasibility, and then badabing badaboom, you deliver. Next.

Along the same lines, do not adjust your scope if the data source is unavailable, uncooperative, or lacking in quality. Instead, bring the fight to the data source (here is where a good, preferable C-Leveled, business sponsor can come in handy) and set things right. This is obviously a project risk, and also an organizational risk. If you are having problems extracting inventory data then maybe its time to put down your data warehousing gloves and get a new inventory system.

Last thoughts

Scoping the data warehouse is a difficult problem. Troubles start early on with the initial idea, it moves on through requirement gathering, and finally into the development phase of the lifecycle. There is not a lot of good advice in this area for data warehousing (if you happen to know of a good source, please send me a link or title). But I do find that if you work towards business processes, think in terms of programs and projects, and avoid the data source trap, scoping decisions will settle into the real needs of the business.

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