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Without Things, There is No Analytics of Things (AoT)

I was recently having a discussion with Richard Hackathorn, an industry strategist & analyst and Dan Graham, a colleague who is deep into my company’s strategy for the Internet of Things (IoT). We were specifically talking about how to enable the Analytics of Things (AoT) and what barriers and opportunities exist today.

During the discussion, it hit me that one of the biggest hurdles faced in trying to purse the AoT is actually just a new iteration of a common, recurring problem that has vexed analytics professionals for years. Namely, we can’t analyze anything related to IoT until the infrastructure investment is made to create, acquire, and make available the data necessary for analysis. I recently wrote about one key aspect of the infrastructure related to automating the tracking and management of all of our things. Let’s address the need for it here.


… impossible! This is obvious, of course. But, it is a very critical point both today and in the past. Early in my career, we had all sorts of wonderful ideas about how to analyze data. However, the data itself was incredibly difficult, if not impossible, to access for analytical purposes. The move from mainframes to enterprise analytic platforms certainly helped. But, to use those platforms to advantage required that the platforms made available the data we actually needed for our analysis.

Unfortunately, it often took years and a lot of pleading and business case building to convince an organization to invest the resources required to enable our analytic vision. Just when we thought we had all we needed, another new set of data would enter the scene and start the entire process all over again. It was worth it, however, because time has proven that new data improves the power of analytics immensely. The world of big data has recently reaffirmed this, and we can expect Internet of Things data to also reaffirm it.


That brings us to the current need to begin exploring the AoT. There are plenty of wonderful ideas and plans out there for the AoT. However, the biggest barrier for most organizations is actually getting the data required in the hands of those who can analyze it. There are several reasons that this is difficult today:

  • First, an organization has to invest in deploying IoT devices in the first place. Without devices with sensors deployed, there is nothing to capture!

  • Next, an organization has to enable the data from the deployed devices to be captured effectively and in a timely manner

  • Finally, even if the first two steps are complete, there is still the arduous task of figuring out how to efficiently store and analyze the data once it does start streaming in

Clearly, the AoT can be nothing but a dream until an organization takes care of the first two steps. Those first steps also require not only a large investment, but a timeline based on quarters, if not years, instead of weeks and months. In the short term, analysts can acquire inexpensive sensors and begin working with the data to gain an understanding of how IoT data streams work. For example, I have a colleague who outfitted his boat with dozens of sensors and now explores that data for fun and practice. When robust corporate IoT data is available, he’ll be ready to hit the ground running.

One huge factor working in our favor is that the cost of IoT sensors and technologies are dropping fast, as are the costs to store and analyze massive amounts of data. My colleague’s boat sensors were all quite affordable and readily available on hobbyist websites. This makes the hurdles much lower than just a few years ago. It is also becoming much easier to justify the investment in IoT infrastructure to harvest the benefits of the AoT because the analytics are clearly proving their value.


Once again, analytic professionals will have to marshal our forces to champion and justify the value of the AoT with our business partners. Luckily, there are more and more organizations already having success which can be pointed to. The Manufacturing and Oil & Gas industries are two sectors that have pursued the IoT and AoT faster than others and they have yielded some tremendous case studies. I believe this is in part due to the fact that when you’re already manufacturing and / or deploying complex, expensive physical things, it is not a big leap to add in new sensors.

Even in those sectors that are already embracing the IoT, there is continuous advancement in the breadth and depth of the data collected and the analysis executed. For example, instead of just analyzing data from drilling processes to optimize those processes, Oil & Gas organizations are beginning to place sensors all along pipelines to constantly monitor flow and look for issues that will disrupt it.


If your organization hasn’t yet put an IoT infrastructure in place, it is certainly frustrating to have to wait for it to be implemented so the data that will enable you to pursue the AoT can be made available. However, I’d encourage readers to consider a more positive view. Today, the opportunity exists to be an early adopter of AoT and to start reaping benefits ahead of others who lag behind. This is not an insubstantial opportunity!

Look back over the past couple of decades and you’ll see organizations that experienced terrific growth by adopting other types of analytics ahead of the pack. The first organizations to centralize and standardize data within a data warehouse environment had a sizeable advantage for years. More recently, early adopters of big data similarly have been able to exploit that advantage. By innovating with IoT data, it is possible to build a lasting competitive advantage and a new corporate strength.

I am already starting to see early adopters of AoT reap the rewards even though we’ve only begun to scratch the surface of what is possible with AoT. The door is still open to join them, but it won’t be for long as AoT gets closer to mainstream. Make sure your organization doesn’t miss the chance to walk through it before it is closed!