Perspectives on Objectives

And I don’t want a pickerel.  /   I just want to ride on my motorsickerel.

Conducting a statistical analysis can be like traveling to a foreign country that you’ve never been to before. You had better have a map and some idea of what you want to do there or you might end up wasting a lot of time, get totally lost, or worse, get mugged and end up in the gutter. In a data analysis project, as with any kind of project, you need to be sure you’re clear on your objectives. These define where you’re starting and where you want to end up.

Project goals are usually set out by the client but may be based on regulatory requirements or guidance. Goals may involve:

• Conducting a Specific Analysis — Some clients want to interpret their own data but lack the expertise or resources to conduct the analysis. All you might be asked to do is run the software. These assignments are common. Search monster.com for “SAS programmer” and you’ll see what I mean. Sometimes limiting the scope in this manner is a way to simplify a large and complex project. Sometimes, it is used as a way to provide security because no one data analyst would see all the results. Sometimes, it is a way to evade having to share credit with a colleague. Sometimes it’s just what your dissertation adviser wants you to do. Make sure you understand what you’re getting into before you commit.
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  Answering a Specific Question — Some clients only want one specific thing. Is their new product better than the old product or their competitor’s product? They don’t usually care about what you do so long as you answer the question. Sometimes a client will know what needs to be done, like improve a manufacturing process, but not know where or how to look for solutions. These projects are usually fairly straightforward especially if the requirements are spelled out in some government regulation or guidance document. Just be sure that that’s all you really need to do so you don’t leave your client in the lurch if they aren’t as well acquainted with the requirements as you are.

• Addressing a General Need — Some clients have a general notion about what they want but can’t distill it into a specific question or requirement. These cases can be a bit more challenging because you not only have to ascertain what a client thinks they want but also what you believe they need. Projects with general goals often involve model building. You have to establish whether they need a single forecast, map or model, or a tool that can be used again in the future. If the client is looking for a tool, be sure you are clear on the limits of the model’s applicability so there are no misunderstandings or misapplications.
• Exploring the Unknown — Every once in a while, a client will have nothing specific in mind, but will want to know whatever can be determined from the dataset. Usually, these projects involve examining large datasets that have been compiled, sometimes over long periods, but never analyzed in total. These projects can be two-edged swords. You can really delve into a dataset and try some of the more esoteric techniques without too much fear of backlash from hostile reviewers. On the other hand, there is usually quite a bit of pressure to come up with something no matter how messy the dataset is. There is also the danger of not being clear on budgets and schedules. Is this a job for statistical modeling, data mining, or just descriptive statistics?

Whatever you do, make sure the goals are SMART — specific, measurable, attainable, relevant, timely — and agreed to by all parties directly involved in the project. There are three situations that can muddy the waters of your objective:

• Changing goals are common when the client has only a general goal to begin with. As you find meaning in a dataset during your analysis, the goals may shift or crystallize into something specific. That’s fine. It’s the reason for doing the analysis. Just watch the budget if the redefined objective takes you way beyond your original scope of work.
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  Multiple goals aren’t uncommon, either. Sometimes, a client might clearly instruct you to consider two or more goals. No problem. Beware of clients looking to get two for the price of one, though. A simple sounding objective might be saddled with additional effort not in your budget; a freebie perhaps only mentioned informally (oh, by the way, can you …), that can substantially affect your performance. A typical example might be something like “conduct this analysis for us, and by the way, can you give us the spreadsheet when you’re done.” You might not even have used a spreadsheet if they hadn’t asked. And it’s one thing if the client just wants the spreadsheet for documentation, but quite another if they plan on using it on a different data set. Doing the calculation might be easy but setting up a spreadsheet to handle the different kinds and amounts of data the client might have in the future would be a much larger effort. You also have to consider what professional liability you may have in such an instance.

• Proposals are the third special situation to watch for. Some clients will ask for detailed proposals then say they decided not to do the work. In fact, they just needed a plan for doing the work themselves. They get their cake and eat yours too. You can’t obsess about this. If a client is going to do this, your only option is to decline the work which consultants rarely do unless they know something is afoot. At the same time, you don’t have to give detailed procedures and references for every analysis you might plan to conduct.

If the client isn’t entirely clear about their objectives, it may be that they are unable to articulate their goals in your language of quantitative analysis. So start at the very end. Try asking them what decisions they will need to make based on the results of your analysis. They’ll understand and be able to articulate those decision points. Then you can translate the decisions into the statistical hypotheses you’ll need to evaluate, identify the data you’ll need, and select the appropriate statistical methods.

This is where I wanted to end up.

So if you are contemplating doing a statistical analysis, know where you’re going but be prepared for where you might eventually end up.

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