It will likely come as no surprise to you that small company managers make mistakes. Big ones; small ones; important ones and insignificant ones. One of the peculiar quirks of small business is
that every mistake, large or small, tends to be felt across the company in a variety of ways. Just as a small car transfers the jolt of hitting a pothole to every corner of the chassis (while a
large car is better able to absorb the impact), small companies are less equipped to absorb the consequences of the management mistakes they must endure. It’s just a simple case of physics –
you can’t escape it.
So what’s my point? Only this: when small companies do a poor job of managing their data, the tremors that ripple throughout the business can be paralyzing. Recovering from these shocks is
frequently expensive and almost always a major distraction from the core mission of the firm. The irony is that most of these data-centric mistakes are avoidable with only a modest amount of
planning. Here are nine data management mistakes to be watching for and some advice on how to avoid them.
- Good data management practices are expensive and excessively time consuming. This one is, I think, the one that trumps all others for short-sightedness. It is true that small businesses are
almost always fighting a cash flow battle, making it difficult to rationalize any processes or infrastructure which requires cash infusions. But that is like reasoning that, because attending
college is expensive and takes at least four years to complete, that the loss of time and the assumption of large school loan indebtedness just aren’t worth it. Just as an individual must invest
in their own future by taking the time to go to college and pay their student loans, so, too small companies must invest in their future by recognizing the business imperative of sound data
management practices. Good data management is a strategic competitive advantage which is seldom a waste of time and resources. If you are harboring an idea that proactive data governance and
infrastructure management are only for big companies with deep pockets, get over it! Good data management is for everyone.
- Data quality is problematic throughout the company, but we can fix it manually whenever we have some free time. Better yet, we’ll hire some college interns over the summer to clean up the data
for us. I’ve heard this one so many times. The rationale for hiring a lot of cheap labor to resolve issues with the mission critical data that fuels your business is misguided, to say the least.
Consider this: the data anomalies that you’re living with aren’t usually the result of unskilled labor merely hacking away at your corporate data assets. These conditions exist in spite of the
fact that the stewards of your information — your professional staff — are well educated and competent in their jobs. It stands to reason that, if your own qualified staff can’t get a handle on
data quality concerns, no interim, modestly paid part-time help is going to solve it for you. Turn to professional data quality practitioners to help you resolve your data quality challenges. They
will undoubtedly cost more than internal resources, but you stand a much higher chance of actually solving the problems! Unless your business is one that has no real requirement for reliable
information (I’m not sure which business that might be), you can’t afford to take the path of least resistance (and cost) to addressing data quality concerns. Find the budget, interview the
vendors and get the thing solved properly.
- A lot of the customer information and order history are in the sales person’s head. So get it out of their head and into a database under your control. Keep in mind that people don’t “own”
your data. Your company owns your data and your company is responsible for maintaining it. You may have customer service staff and/or sales professionals who aren’t utilizing the systems you’ve
put in place (mainly CRM solutions) to make your customer information shareable, either because they think its “too complicated” or somehow beneath them to worry about trivial matters like sound
data entry and maintenance practices. Don’t tolerate this situation. Your customer data is one of your most valuable (if not the most valuable) corporate assets. Get it under your control and keep
it there and if your staff protests, show them the door. Anyone without a fundamental understanding that shareable information leads to better communication, which in turn fosters greater
creativity, which ultimately results in higher profits, is liable to squander that information resource at every turn. Let them go and ruin somebody else’s company.
- Data privacy is important, but we don’t have highly sensitive information about customers that we have to safeguard. It doesn’t have to be medical history or credit card data for it to be
sensitive (and therefore worthy of safeguarding). A complete list of your customers falling into the wrong hands has the potential to be just as damaging as the negative press you’d receive for
sloppy data security practices. How about employee information? You have social security numbers, bank account information, payroll records, medical insurance information, etc. that is pure
dynamite in the possession of unscrupulous parties. Pay heed to data privacy and data security matters before someone’s attorney focuses your attention for you. At the end of the day, an ounce of
prevention is worth whatever it costs to achieve.
- We’ll make Joe responsible for data management since he’s the one who knows the most about the systems. It is unwise to assume that because a particular individual in the IT group knows the
most about the servers, databases, applications, etc that they are the logical best choice to serve as corporate data steward. Joe may be expert in the technical “metal” of your IT assets, but
being a good data steward demands a deep understanding of the business processes which create, retrieve, update and delete corporate data, not just how to configure the database indexes for optimal
performance. Seek out the person in your firm who knows the business better than anyone else and enlist them to lead the data stewardship function (which might also involve Joe and/or a number of
other subject-matter-experts). These people will have an intuitive understanding of the impacts of making one data management decision versus another. They may not be able to perform the down and
dirty technical aspects of data management, but they don’t need to if they can help the technical gurus to plan and execute sound policies.
- Since data touches everyone in the firm, we’ll create a cross functional standing committee to be responsible for setting policy and making things happen. This actually sounds like a pretty
good idea at first blush. Then, when you have to take this idea from paper to reality, you find that data management by quorum is inefficient and frustrating. One of the prevalent failure points is
the matter of consistency and commitment. When company-wide groups are formed that are supposed to pass judgment on new policies, review existing processes, evaluate current conditions, etc., they
are almost always launched with the best of intentions and assumptions about the long term commitment of the members. Unfortunately, after the newness wears off and the tough slogging begins, the
very people who are supposed to be helping to solve data management problems are, themselves, becoming a problem because of conflicting schedules and demands that exceed resources. Suddenly getting
the right people in a room for a detailed walkthrough becomes problematic. They may have a junior person sit in for them when they can’t make meetings. This isn’t, by itself, a bad thing except
that their limited experience may diminish the contribution they can make. If a member of this committee is becoming detached and/or disinterested, don’t wait to resolve the situation on the
assumption that the loss of one key contributing resource is manageable, because it never ends with just the first person. Eventually, all of the members will be challenged to meet their committee
obligations, especially so when they don’t see corrective action taken as problems arise. Effective data management actually requires active management, not passive concern.
These six issues are not the finite list of mistakes that small companies make regarding data management. Lots of other things go wrong, from insufficient hardware or improperly configured software
to the loss of corporate commitment to sound practices. Whatever the issue though, they can almost all be traced to one of these six core missteps. Get these six things right and you stand a good
chance of keeping the rest of the fight winnable.