As a data miner, your place in the organizational chart may be in a special group devoted to analytics, or within any conventional business unit. No matter where you’re placed, whether you’re dabbling in data mining or making a full-time job of it, you will be most productive if you are familiar with the roles of other business units and on good terms with appropriate staff members.
Marketing and sales
When businesses decide to give data mining a try, the driving force usually comes from Marketing. For most data miners, the first project is a marketing project. (And marketing is not just for business. Nonprofits and government agencies all have similar roles.)
Get to know the scope of the marketing and sales functions where you work. In most cases, marketers are responsible for converting members of the general public into sales leads, and salespeople are responsible for converting those leads into closed sales. You can find variations, though, especially for companies involved in online or catalog sales.
Many marketers have some experience with traditional data analysis, so it won’t be a big leap for them to understand the important concepts of data mining. They’ll understand your questions and the reasoning behind them. And they will likely point out some issues that you would have missed. You can expect them to ask challenging questions about your processes, and you should give those questions serious consideration.
Marketers are usually good communicators, too, so you stand to discover a lot about the business from them.
Business administration and finance
Finance people don’t get close to data mining nearly as often as marketers, but when they do, pay attention. These are the people who control the flow of money in the organization.
Finance experts can be particularly valuable for helping you understand which potential solutions to a problem are feasible, and which aren’t. They can spot problems with cash flow, accounting, and legal concerns that might not be obvious to others.
Although finance and other administration units are not often the executive sponsors of data-mining projects, they do care about the results. Chief financial officers (CFOs) can be powerful advocates for data mining if they see a concrete connection to increased revenues, cost savings, or better cash flow.
Product development
Product developers can be makers of physical or virtual products, even services. They may be engineers, programmers, designers, product managers, or any of a long list of other specialties.
Product developers possess invaluable knowledge! They know what they can and can’t make and why. They know how long it takes to produce things. They know how much labor is involved and what skills are required. They know about union and other work rules. They know why things have been done in certain ways and whether changes are technically feasible.
You’ll also find that members of the development team have knowledge of data that you’re not aware of. The product engineer may have devoted hours to reviewing warranty claims. A designer may have interviewed (or even video recorded) users in the field. A software engineer may be keeping a personal file of new feature requests.
Information technology
Data miners absolutely, positively must have a constructive working partnership with the Information Technology (IT) team to do the job right. So it’s sad that in many cases, these two roles don’t work well together. It’s not unusual for information technology and data mining (or any type of data analysis team) to have a downright adversarial relationship.
People resist working through IT for a number of reasons. Access to data through approved channels is often slower than analysts would like. IT may impose rules on how data can be accessed, used, or shared. And they may require some electronic paperwork. It’s all stuff that many data analysts perceive as a waste of time.
And IT isn’t always dying to deal with us, either. Data mining sometimes requires a lot of data (a customer service representative opens one case at a time, while a data miner might use thousands or more). One big query from a data miner can grind everyday operations to a standstill.
So data miners around the world resort to work-arounds to avoid dealing with IT. They obtain data from any source they can, often without clearly understanding the source or quality issues. Then they don’t share results. Why not? They don’t want anyone asking questions. How is this behavior supporting data-driven decision making? Poorly, very poorly.
If data mining is to have a truly meaningful impact, data miners must make nice with IT, because
Management can’t put the results of an analysis into action if the details aren’t available to the folks who manage your IT.
Data and analysis aren’t your personal property. They belong to your organization. You have to share.
Data stores are growing large. You may need a sheer quantity of data that can’t be kept surreptitiously in a personal file.
Management (at least in some places) is getting smart enough to ask details. You’ll need to document where, when, and how your data was obtained.
Sidestepping IT puts you at risk of violating data privacy laws or failing to meet other important business obligations.
When you plan data-mining projects, talk with IT up front about what you’re trying to accomplish. Get feedback on the data management issues you will face and your obligations regarding data privacy and other matters.
If someone in IT tells you that a problem exists with obtaining the data you need, ask about the reasons and listen carefully to the replies. You may be asking to do something that violates a law or contractual obligation. Explain your goals and ask about possible alternatives. Get these conversations going early in the process so that you won’t find yourself making commitments that you’ll later find out you cannot keep.