4 Reasons the goal of IOT is not analytics

I had the opportunity to attend the Smart cities and IOT event that the Arizona Tech Council put on yesterday. #AZTCSMARTCITY. It was a well-done event (as all AZ Tech Council events seem to be) with some great speakers with interesting takes on the IOT future and market.

"I'm extremely excited about IOT because I can get some great views into my customers, my constituents and my data and get "actionable insights" on that data!" Was a mantra I heard from speakers and attendees alike. When talking about the sensor data that they can amass, there was some great discussion of the analysis, the data lake, the ability to see "views" of what's happening. Don't get me wrong. All of this is useful information, and can help guide strategic direction to reach underserved markets and help you find niches that need improvement in your business.

But here's why that's just the tip of the iceberg -- and why analytics falls flat in producing lasting business value.

1. Analytics-focused IOT initiatives frequently leave insights in silos. We've got all this data, but only the data scientists who have access to the Data Lake can actually see this information, and we're largely reliant on them to look for the right details and key linkages to generate value. Who knows better how to facilitate the data to meet the business needs? The most frequent approach is to find Data Scientists and hire them from the outside and bring in their wisdom to run the analytics. This doesn't harness the legacy knowledge and institutional wisdom of the long-term business leaders who know where to look in the data to find the insights. Yes, the data scientists can help think outside the box for unknown unknowns that have been missed by the business, but what about linking those with the known unknowns and the known knowns?
2. Analyzing IOT data takes huge effort to communicate correctly. Anyone in analytics has heard the tragic reality that our scientists knew the space shuttle Challenger was likely to crash in the 80s. It wasn't a failure of analytics, it was a failure of communicating those analytics clearly and distinctly. See the link for a more thorough discussion: Challenger analysis solid -- but hard to understand. So just having a huge amount of data and analytics for that data is great -- but its not a guarantee of success. You run the risk of communicating this insight in some dashboard or analysis, but not communicating the impact of changing the business quickly to take into account this key information. So that insight sits and waits for someone to understand it's impact on the business.
3. Analytics on IOT initiatives can put your company at risk. This might be the least understood and potentially most dangerous impact of IOT and Big Data initiatives. In any scandal, the "what did they know and when did they know it" investigation is at the forefront. Huge data lakes with tons of disparate data and all kinds of analytics on that data increase the likelihood that the answer to this will be: They had the data, someone analyzed the data. But they didn't do anything about it. Unfortunately and often overlooked by many companies implementing IOT, this can very easily translate to the legal profession in one word: Negligence. The end result to your company is a fine or lawsuit.

So if analytics aren't the correct goal, what is?

The goal for IOT projects is to Operationalize Insights. Yeah, it's a big word, operationalize. Another, less descriptive and maybe more incomplete way to describe this is to automate into your business the insights you are getting from your IOT initiative.

The goal for IOT projects should be to automate your responses to IOT insights.

This means creating a structure and system in your organization to feed IOT findings and data through rules engines and automating the way your organization responds to both positive and negative IOT data. And my challenge to your company is to start with this end in mind. Why invest in IOT if you cannot automatically ingest and leverage the knowledge from that investment into your everyday business?

Here's three reasons why you should put Operationalization of your IOT initiatives at the top of your priority list.

Why invest in IOT if you cannot automatically ingest and leverage the knowledge from that investment into your everyday business?

1. Data Collection without business integration is like building a huge power plant with no power connections. Sure the plant is impressive, but it's not actually useful. It's like finding a cure for cancer, but not finding a way to deliver it to the patient. It's fascinating to me to see how many organizations are all excited about Big Data and about IOT -- but have no initiatives to port this data into their business. They want to play with it a bit, explore what can be done and see if it can be useful. Meanwhile it's business as usual for the rest of the company. The problem is that the people playing with the data don't understand the business issues and very often aren't involving business people in the "playing." It's weirder still when these Big Data and IOT leaders do this exploring, then seek out business units that they can help with their solution. Their a solution in search of a problem. This is the exact opposite of the way it should go. Find a business initiative, then build an IOT solution or Big Data solution to help support it, not the other way around.
2. Success breeds Success. If you're truly excited about the impact of IOT, there's no better way to get people excited about it than to have a successful business initiative to crow about. When people see that IOT can help drive revenue, cut costs or improve service, they start to pay attention. It's not about "seeing" the data, it's about seeing the solution and the solution's impact on the business.
3. "Seeing what's happening" MUST automatically power change. As the Challenger disaster showed us, knowing is not enough; that information has to produce change. Analytics vendors like to talk about "actionable insights." This is knowledge you get from your data that can help you decide you need to take action. That's great if you're looking at one dashboard, with a couple of actionable insights and you can pick up the phone or yell down the hall to someone to do something. But what if you have 400 dashboards with 30 actionable insights on each, every day? Then you have to automate your response to this data. You have to ensure it's routed correctly to the right decision makers -- if they're even needed -- maybe instead you can automate away those manual decisions and let a rules engine make them for you. This has the power to change your business. Instead of waiting on notes in someone's in-box to be handled appropriately, the impact of the data is immediately rolled back into the business to change course, adjust and improve. Add on some machine learning capability and now you're creating a competitive edge that is hard to overcome.
4. Competing in the next decade will mean harnessing this technology to drive your business. The companies that grow and thrive will leverage IOT to inform their moves -- automatically. We see this happening with sensors in the auto industry already. Lane-changing alerts led to automatic braking and driver steering assist as automakers seek to gain competitive advantage by leveraging IOT for business.

Yes it's important to explore new technology in a "sandbox" and understand what they can do. But if you tie these explorations to actual business needs and initiatives and brainstorm with the business the impact of IOT and big data and then work with them to build these sandbox solutions, you'll not only build internal excitement but you'll get to value so much faster and speed the pace of innovation in your company.

What do you think? How are you doing this where you are?