Imagine an innovative new product – the automated stapler. This stapler boasts some rather nifty features. First, it is biometrically activated. Gone are the days of someone stealing a stapler from your desk, using all the staples, and returning it with an empty cartridge. Now, without your unique fingerprint activation, the stapler is rendered useless. The stapler connects wirelessly to your organization’s biometric database and is programmed to allow only authorized users to fasten papers together.

The stapler also is fitted with a geographic tracking system that is connected to your smart phone by an app that tracks its homing signal. Never again will you lose a stapler under a pile of strewn papers. Additionally, the stapler keeps an electronic tally across users in terms of date and time of use, number of staples used, longitude and latitude of use instances, and the number of staples rendered useless due to attempts by uninformed users to fasten together a number of pages that exceeds the stapler’s capacity. The stapler’s internal records are uploaded daily to your organization’s property management system.

Sound far-fetched? Sound crazy? This is the future, or what the future could be, if organizations don’t think strategically about data. The Internet of Things, a generic name for the massive web of our device- and gadget-based culture, is expanding exponentially on an almost daily basis. Whether the devices are wearable, consumable, observable, or usable, it is very likely that, somewhere in the world, there will be a lot of data stored about you and your coworkers that will be downright boring and completely useless. And without a strategic approach to determine which data have value, this trend is only going to increase.

Don’t misunderstand my meaning. The Internet of Things is definitely not bad. In fact, its potential for informing strategic decisions across industries and domains is tremendous. But this potential comes with a caveat: don’t allow your data to become too big to be meaningful.

Here are four steps organizations can take to ensure that they do not get crushed by the “data tsunami of things.”

Step 1: Establish a data vision. Before collecting information, organizations should think about why the data are needed, how the data will be used, and the alignment of collection strategies with strategic needs. Too often, organizations collect data because they think it might be useful someday. Such a strategy could inadvertently render important information unusable because analysts are unable to sort through useless terabytes of data to get to it. A collect-all-data strategy consumes the resources required to analyze data that is truly needed.

Step 2: Determine data gaps and needs. Explore whether data needed to support decisions can be collected via current reporting mechanisms. Data from the Internet of Things shouldn’t be targeted just because it’s cool or trendy. Make sure a specific strategic need can be filled via data collected from automated devices.

Step 3: Establish limited and strategic data processes and protocols. Identify the limited but impactful data elements required for key decision-making. This is the number-one rule to observe when dabbling in data from the Internet of Things: Don’t collect more and more data just because you can. Successful organizations distinguish the potential data elements that actually drive decisions from those that are just noise.

Step 4: Get smart about using your data. Identify holes, improper structure, and instances of over-ambitious collection. For example, in practice, it may be enough that your organization knows simply that you have a stapler. The other data about the stapler, described above, may be just too much. If you discover massive amounts of data are being collected but not used, don’t be afraid to re-evaluate the purpose of collecting that data. If you treat data like a mushroom (leave it in the dark under a pile of dead leaves until you are ready to harvest it), you may get blind-sided by problems with the data when you try to use them. Unused data are probably ignored data. This means that no one is paying attention to making sure the data are free of errors.

Together, these steps provide a useful framework for thinking about your organization’s data situation and how the future data environment may impact you. The key point of the steps is to question your data-collection practices regularly and in a strategic way. Ask yourself how you are using your data to develop, execute, and measure the strategies that drive your organization’s performance. Otherwise, forget about the big data revolution. The next generation of consumable information may instead be “silly” data. Big brother may be watching everything you do, but don’t worry: much of the data will be worthless. Unlike financial institutions, data is not too big to fail.

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