In the 3 years that TmF has been online, I’ve never gone this long without posting. Feels weird.

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

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.

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!

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.

The following are what I deem to be essential technology-related aspects, or faces, of a good ETL developer and/or architect (herein referred to as an ETLer for lack of creativity). While you need to consider business and industry knowledge, personality, and experience in your team-building process, you should start by checking off the following on your interview sheet:

First Face: the technologist

Programming must come natural to an ETLer. Objects, logical constructs, expression construction, program flow, and the like, must be well understood. The truth is that no matter how much your vendor proclaims that their tool does it all, chances are excellent that some hand coding will be required. On top of that, ETL tools work a lot like procedural programs. Technologists are very good at putting their right foot forward, and will generally think of things to make the ETL flow perform better. They also think about logging, auditing, and exception handling; all important.

Second Face: the theorist

But a solid programming background is not enough. Knowledge of Data Integration theory and best practices are equally important. While I believe in and use Kimball’s methodologies for integrating data into a dimensional data warehouse, other methodologies exist that may be more suitable to your business and integration needs. Following a proven methodology, with slight modifications to suit your environment will get you further, faster. Having little or no theory behind what you’re doing gets you somewhere, slower. Identify your methodology, and then find someone who understands it.

Third Face: the specialist

Knowing the ins and outs of your ETL tool (SSIS, OWB, Datastage, Talend Open Studio, etc.) is essential. I would venture to guess that a solid programmer who has a great understanding of ETL theory will be able to get by using most tools with little learning curve. What I worry about (and you should too) are the nuances in the tooling that can stump even the best. These nuances (SSIS, my tool of *ehem* choice — sorry, I needed to clear my throat, has many of these nuances) can cost you many project hours and force rewrites if blocking issues are encountered. Tool knowledge is also essential to know when it is appropriate to forgo the tool because of I/O issues, or because hierarchical data is better handled elsewhere, or because business logic is best not bundled within a data flow.

About Face

While junior members of your data integration team can be one or two-faced (that came out funny), senior members and architects must have more meat on the bone.

I suppose this is why good ETLers are difficult to come by. The ETLer needs to have a healthy mix of programming talent, an approach discipline, and tool knowledge. Trained DBAs and software developers might have a lot to offer, as might a troop of certified tool jocks and method junkies, but to get your project in on time and within budget, don’t settle.

1. You shall compile and document all requirements and mappings; segregate the work by business process. You may have more than one of these business processes, some of which may come before others.
2. Do not begin without first conducting a thorough data profile; otherwise, you will be punished for your inequities, as will the generations that come after you.
3. Do not think commandments one or two are in vain, lest you will become overrun by the dead line, scope creepers, and a great exodus of people from your tribe; if this happens to you, do not swear or curse, for you have been warned.
4. Remember that latency and timeliness are equal in importance to non-volatility and having a traceable lineage; a staging area may lead you to this promised land.
5. Honor the rules of data conformance.
6. Do not kill dirty data: you shall clean them, or take them back to their sources for retribution.
7. Do not commit the worst data integration transgression of all and ignore data quality, your ignorance will not be forgiven.
8. Do not be shy about stealing your neighbor’s work, for his trials have led to best practices that you can make equally good use of.
9. Do not rely solely on business keys; surrogates are your friend and will permit you to engage in slowly changing your dimensions.
10. You shall covet a proper audit and log system; for on the day of judgment, you will need proof of your compliance.

But many organizations still struggle to make the connection.

Some suggest varying 3-, 5-, and 7-step approaches for better alignment. They typically consist of diving into new organizational structures, or implementing new frameworks and service models. Other sources say that the business is to blame, that IT is misunderstood and underutilized. While finally, some say it is a dead issue altogether.

I’m not convinced this is a dead issue, nor am I convinced that the business is to blame (especially nowadays). I’m also not convinced that 3-, 5-, and 7- step plans will magically work.

A few basic actions, initiated by IT, must happen first.

The Actions

Business has long ago recognized that better alignment with IT is essential. We can argue about how well they’ve understood IT and how it can best be utilized, but across the board, the recognition is there. IT, after many years of making the case for alignment, seems to be coming up short during this crucial period. I hope that the following actions, from an IT-perspective, can help:

• Understand: Learn the language of the business. Plainly put, this means you need to become more financially intelligent. All businesses exist to earn a profit, and understanding how this works is the first critical step. You must understand where the estimates and assumptions are in the numbers, when you would want to depreciate or amortize, and what constitutes a capital expenditure or operating expense. Among many things, you must understand ROI, cash conversion, and how to use profitability ratios.
• Participate: Take part in strategic and other long-term planning initiatives. IT professionals must be able to see the company’s vision and turn the vision into actionable IT initiatives. If a representative from IT is not present during steering committee and other board meetings, make this a priority. You will need to convince the business that IT is capable of playing an important role in all long-term decision making. Fortunately, businesses already realize this need, but in many cases don’t feel that IT can effectively contribute (perhaps because IT doesn’t understand the business language).
• Contribute: Provide business with the ability to make fast and accurate decisions. This means pioneering smart business intelligence initiatives that can provide decision-makers with the tools and reports — distilled — that business can utilize. You need to prove your flexibility, agility, and ability to understand what the business really needs. Much of this is tied to understanding, and extended with participation. These initiatives also include business process improvements; tighter integration of business, meta, and master data; and managing performance.

If IT can take action, better alignment can be achieved. This isn’t to say that the business can’t do more, but before the business can make IT a full partner, understanding, participation, and contributions from IT are a must.