Daylight savings time is upon us once again, and if you’re like us, you may have a lengthy list of things that are recommended to be taken care of when you change your clocks. The first item on the list used to be changing your clocks, although the number of devices whose clocks don’t automatically update seems to be getting smaller and smaller in our house!
A well-known recommendation is testing the batteries in your smoke detectors. (Yes, we're aware that modern smoke and carbon monoxide detectors don’t always have batteries you can change, but they still need regular testing, and they need to be replaced on a schedule.) Over the years we’ve collected quite a number of tasks for the daylight savings list. For example, flip your mattress, clean the coils behind your refrigerator, inspect/replace HVAC filters, clean laundry ducts, clear out your gutters, and so on. One of our favorites came from a locksmith who suggested you lubricate locks and door hinges twice a year!
Some of these recommendations have a much larger impact than others, at least potentially. If the batteries fail in your smoke detector, and a fire breaks out in your house, your belongings and your life could be in real danger. That's a much larger concern than how much longer your mattress will last if you flip it periodically. Having clean filters on devices that use them will save energy (and money) and will provide better air quality in your house. There’s probably even a chance that lubricating the lock on your front door means you’re less likely to need a locksmith down the road! So, it’s not as if following these recommendations won’t have benefits.
They aren’t very costly activities, although having your ducts cleaned or your gutters cleared won’t be free, and replacing batteries or vacuuming behind your refrigerator, at least for us, is more of an inconvenience than anything else. We know we’re supposed to do them, and we have a simple mnemonic to help us remember when to do them.
But the fact of the matter is, probably, that most of us don’t always get to these tasks as often as we should. And most of the time, the impact is minimal, although some of the time the impact could be catastrophic. So we might say to ourselves, we’ll get to that in November, and then the next thing you know several years have gone by and you haven’t lubricated your locks or changed your batteries or dragged your refrigerator out of its corner.
We wonder if this is analogous to the way many of us treat our organizational data. Sometimes smoke detectors tell you when their battery is going bad, and we both hope and strongly recommend that you replace the battery at that point, rather than just pulling it out! By the same token, sometimes your data lets you know when there’s a problem: an automated process errors out, an intrusion or breach is discovered through automated tools, applications fail due to an underlying data issue; maybe more frequently, the people in your organization notice anomalies or other issues and sound the alarm. And when this kind of alarm sounds, we imagine it's hard to ignore.
If you don't make it a habit twice a year to perform these household maintenance checks, you expose yourself to increased risk, and almost certainly you'll miss out on easy ways to save at least a little money. What would it look like to run a periodic battery health check on data? What can we do quickly, at least once or twice a year, to minimize the risk of a data catastrophe, and to realize at least some small data efficiencies or cost savings?
After a period of time, there’s a reasonable chance that some—maybe a lot—of the data you collect becomes something like dryer lint, or dust on your refrigerator coils: it’s no longer valuable, it may not even be that noticeable except when it clogs things up, and it may even become something you’d be better off without. These days it doesn’t cost much to store data, even data ROT (data that is redundant, obsolete, or trivial), and it’s pretty unlikely that obsolete data is part of your analytics data sets. But, there’s still some risk associated with holding on to this digital dryer lint, just as there is with storing any data. Just because it’s no longer useful to you doesn’t mean it’s no longer useful to hackers or unscrupulous data brokers, for example. Sharing protected data inappropriately, or exposing it accidentally, could still result in financial or reputational penalties.
Suppose there’s a buildup of dust or lint or whatever lurks in your HVAC innards that you don’t know about and don’t look for. Chances are you spend more money on heating and cooling, and maybe you end up replacing units sooner than your neighbors (or sooner than your wallet would like!). But on an ongoing basis it’s probably a barely noticeable difference in cost, especially when you factor in other variables such as the weather, the cost per energy unit, whether your family sets the thermostat to an energy-efficient number, etc.
By the same token, perhaps at your organization your analysts and data scientists spend more time than they’d like preparing and blending data, and perhaps it takes longer to produce reports and dashboards than you’d like, and perhaps you encounter more potential errors than seems desirable. But it costs money to replace tools, and it costs time and effort to make significant cultural changes around managing data, and we always need that data yesterday, so whatever you can provide today will have to do, and so on.
In the long run how much impact does it have to cut corners? With appliances, there is always a chance that accumulated dust or pet hair or other foreign matter burns out the motor, and either you’re in for an expensive repair or an early replacement of the product. So a little bit of preventive maintenance can save money by extending the life of an appliance, and it can certainly reduce hassle and inconvenience. Not that there’s ever a good time for your air conditioner to give out on you, but if it happens on the first day of a long holiday weekend, you’re in for some discomfort at best.
What would a similar seasonal maintenance of your data assets look like? If you have an inventory of applications and products, you could check to make sure all those tools are still in use, and that the contact person at your organization is still the contact person. If you have visibility into your BI tools, you could review which published dashboards haven’t been viewed in some period of time, and investigate whether they should be taken down. While we’d like to see everyone running automated data quality checks, that may not be possible for you. But certainly there are some spot-check queries you can have on hand to run every six months to look for glaring issues.
These suggestions assume that a robust data governance framework is not already in place. If one is in place, then it’s easy to think of additional tasks for period review: updating the data governance duty roster, reviewing outstanding action items from the past six months or a year of meetings, identifying policies in need of revision or replacement, etc.
How long does it take to walk around to a few rooms in your house and inspect the smoke detectors? Most of the time they’ll be in good working order, and if you find a problem then that goes to the top of your to-do list. Maybe it takes the same amount of time to look at a filter, to clean out a vent, to flip a mattress. Ideally, you’d do all these tasks one after the other after you change your clocks, and then they’re done (at least for a few months). But if you do one a day for a few days then it’s less time and work each day, and the result is still the same.
We have clients who have identified some key data products that require an annual review for usefulness, trustworthiness, and currency. We love this, for many reasons, really. One reason is that this practice reflects a recognition that data isn’t static: its flow changes, its use and users evolve, its meaning and downstream effects can also change. Another reason is that this is a practical task for employees, rather than an abstraction: review the dashboard, including the metadata related to it (terminology, lineage, calculations of variables, etc.), make the needed changes in the context of existing governance protocols, and be finished. (This may not be a classic example of what we call just-in-time data governance, but it’s close enough.)
Note that these clients are not performing a full audit of their entire data ecosystem! They’ve identified areas where they know they don’t want to accumulate technical debt, or where they don’t want to face the consequences of not performing routine maintenance. Sure, it’s easier to fall into bad habits than to develop good ones, but once something is habitual it’s less of a burden, and it comes to seem natural. Effective data governance looks a lot like healthy habits, and as with any lifestyle change, most people need to ease in with support, reinforcement, a lack of judgement, and helpful reminders.
Your healthy data habits can start anywhere, at any time. That’s exactly our approach with new clients who seek our data governance assistance, or who purchase our Data Cookbook solution. What are the problems you’re trying to address using data? What barriers are preventing you from using data? What practical, repeatable steps can you take right now to make visible progress? What reminders can you set—weekly, monthly, quarterly, yearly, when the clocks change, whatever—to reinforce that repetition, to build that muscle memory?
Now, if you’ll excuse us, our vacuum cleaner has finished charging, and we’ve got some work to do. We hope you’re enjoying the evening daylight, and maybe, when you step outside and can still see the sun, you’ll be inspired to keep up with healthy data habits!
Hope you found this blog post beneficial. To access other resources (blog posts, videos, and recorded webinars) about data governance and data intelligence feel free to check out our data governance resources page.
Feel free to contact us and let us know how we can assist.
(Image Credit: StockSnap_6D8ZSERTAR_manwatch_daylighttime_BP #B1289)
