11
Dec

The IoT Frontier: As Seen In Trade Shows

By Lucas Lowden, Research Director

As professionals, we often hear about expanding our horizons. How frequently do we actually do so? In reality, not very often. We get comfortable, and, we are experts at what we do anyway, right?

Commercial Vehicles. Fleets. Construction Equipment. I’ve done plenty of research projects with these professionals.

Big Data. Internet of Things. I’ve heard plenty about these concepts, and do work with a lot of data.

Now put all those ingredients into a pot and distill them into something useful for professionals in those industries?  That’s a different kind of problem. It requires new tools. New skills. A new way of thinking. A new understanding.

Truthfully, everyone’s reality is different and being made uncomfortable is not easy for most.

The last two years of my career have been a whirlwind of discomfort for me. And I’ve loved every minute of it. Learning, growing, helping – each in parallel with teammates and partners alike. In establishing a data-driven mindset we’ve embraced a new way of thinking to get to a new understanding. It’s been incredible!

It’s now late into 2017, and I recently attended the North American Commercial Vehicle Show, NTEA Executive Leadership Summit, and EquipmentWatch’s Traction 2017 show.

Interacting with fleet and equipment professionals at the trade shows forced me to personally broaden my horizons, and embrace the pain points that may make those professionals uncomfortable. I quickly realized that my reality as a Market Research professional differs greatly from that of a Fleet Manager or an Equipment Manager.

Which brings me to the first theme that became apparent to me.

Theme 1: Big Data Is A Big Deal, Getting Bigger With IoT

Let’s start with something that most industries in existence are familiar with – Data. Data. Data.

Data has long been available from an enterprise perspective – financial data, employee data, customer data, and transaction data, among others. Most have utilized each source of data independently for their own practical, everyday needs. Some have integrated the data for a broader application.

Operational data is becoming much more prevalent today – passive data coming in from sensors integrated with all types of equipment and applications used to conduct our everyday business and in our personal lives.

With the IoT I expect the growth curve of data to be an exponential factor the likes of which we may have never seen before. I’ve heard the term 4th Industrial Revolution thrown about. I’m not totally sold on that scope just yet, but it seems more possible than not from my perspective.

Getting the data is often not terribly difficult. Making sense of it is slightly more difficult. Harnessing the power held in these disparate data sources? Broad success stories are far and few between.

So how do we get past this hurdle?

Theme 2: Integration Is Key

Everyone has data. Few have truly harnessed the power of integrating their data to the extent it could be today.

To use an example from a long haul transportation perspective, integrating truck telematics data can give you the amount of fuel burned while a tractor is idling. Layer contextual feedback from a driver survey to understand the idle situation to deem an idle event necessary or unnecessary from a business perspective. Lastly layering that with fuel spend, and you can see how much money lost due to unnecessary idling.

There are lots of high quality solutions in the burgeoning market that provide services around the IOT ecosystem – telematics hardware, internal/external CRM, database architecture, reporting dashboards. As of yet, not many have fully embraced data integration.

That doesn’t even get into what I feel like is the next technology wave of data integration– blockchain. That’s a whole topic in its own right, so will save this for a later post.

For small to midsize organizations this highlights a challenge – they often don’t have the time available or skill set needed to integrate their own data across platforms.

Ultimately, baby steps are critical to integration efforts. Partner. Discuss. Get smarter. Get better. Rome wasn’t built in a day.

Integration of data and systems is a natural progression to the final theme.

Theme 3: Any IoT Solution Has To Be Easy To Use

The integration of data at the business level leads to a “what’s next?” question of sorts.

Sometimes, a reporting dashboard can be a solution. For others, it’s an app delivering their data and insights.

Any solution in this space needs to be data-driven and actionable to be most useful and effective for industry executives.

It also needs to be simple and easy to use. Time is money.  Difficult to use and hard to understand solutions cost a company more time and more money.

Currently I’m contributing to a data-driven solution that delivers descriptive dashboards and actionable light-prescriptive reports that, with ongoing interaction, can develop into full predictive and prescriptive systems.

From my perspective, full prescriptive and predictive analytics come with nothing more than time and data pumped into the appropriate systems. Those claiming the ability to do so already are quite far ahead of the curve.

Recap & Conclusion

To work through these steps requires some keen self-awareness and the desire to embrace a data-driven decision making approach around business and competitive intelligence.

In each case, we get there one way – by data.

New technologies are allowing data to be brought in, analyzed, and presented to stakeholders in ways never before imagined.

Doing so represents a whole new batch of challenges at the same time.

Do we have the time? Do we have the people? Do we have the money?

Yes. Yes. Yes. You have to.

If you answer “yes” to all the above then you’re golden. If you don’t answer “yes, I do internally” to all the above that’s ok too. One way to shorten your timeline is to say “yes, I do by partnerships”.

The risk of not saying “yes” and taking action in this new frontier is potentially greater than taking action and failing, but still learning along the way.