Big Data

Simplifying The Internet of Things Conversation

Bill Schmarzo By Bill Schmarzo April 7, 2016

We’ve all been in those sales meetings. The sales person kicks off the meeting by welcoming everyone and introducing the topic of discussion. Then the pre-sales expert drags everyone through their 100-slide PowerPoint deck with enough buzzwords and confusing phrases (at 9 point font, of course) to dull even the most engaged person.

I call these types of presentations the “Jabberwocky Strategy” because they remind me of one of my kids’ favorite poems, “Jabberwocky”, by Lewis Carroll:

`Twas brillig, and the slithy toves

Did gyre and gimble in the wabe:

All mimsy were the borogoves,

And the mome raths outgrabe.

“Beware the Jabberwock, my son!

The jaws that bite, the claws that catch!

Beware the Jubjub bird, and shun

The frumious Bandersnatch!”

It seems that the plan behind the “Jabberwocky Strategy” is to make the conversation so complicated, that the customer gets overwhelmed and has no choice but to buy from that particular vendor. Blinded by science, I guess.

Now the “Jabberwocky Strategy” is being applied to the “Internet of Things” (IoT). The IoT is already being declared the “big data killer app[1]”, so indispensable that companies cannot expect to survive without it. Much like we heard about social media three to four years ago and much like we’ll hear about embedded human sensors three to four years from now, IoT promises untold fortunes…but only if you buy your IoT products/services from me.

However, smart customers don’t act in an atmosphere of confusion and complexity; smart customers act when the conversation has been made as simple and straightforward as possible. Consequently, I want to take a very different approach. Instead of trying to overcomplicate the IoT conversation, I want to simplify it and provide a straightforward plan for how organizations can act today.

Internet of Things (IoT) Conversation

A recent article on Forbes.com titled “IOT Is The Killer App For Big Data” introduces the IoT technology “layer cake.” I like this chart as it lays out the IoT ecosystem (see Figure 1).

Figure 1: IoT Technology Layer Cake

Figure 1: IoT Technology Layer Cake

However, I dislike the title of the article as IoT is not an app; IoT is data. The real “Big Data Killer App” is the ability for organizations to couple new sources of data, such as social media, wearable computing and IoT, with data science to make better decisions

Making The IoT Conversation Actionable

The chart (and the article) stops short of telling me where and how I can leverage IoT. Consequently, I have added 4 steps (outlined below) to help simplify the IoT conversation and clarify what an organization needs to do to capitalize on the IoT.

Step #1: Begin With An End In Mind. Unless you make wearables and edge devices, IoT is just another data source for you. IoT is NOT an application. Much like social media and wearables data, now comes the IoT data and everyone loses their minds. But IoT is only data and having data is no guarantee of success. You have to do something with the data in order to create insight (value).

What operational or business initiatives are you trying to solve? The initiative could be machine downtime, for which you seek predictive maintenance. It could be network load balancing or capacity planning or demand forecasting. It could be any number of operational or business initiatives, but it is best to start with a targeted initiative in order to frame the rest of the conversation. Otherwise you just end up boiling the ocean and hope that something gets cooked in the process (other than your career). So let’s add Step 1 to the chart (see Figure 2).

Figure 2: Begin With An End In Mind

Figure 2: Begin With An End In Mind

Step 2: Identify The Decisions. The original chart states that “Layer 7” is about “transformational decision making.” However, instead of making this layer 7, I’d make it layer 2 (or step 2). You need to identify the decisions the key stakeholders need to make in support of the targeted operational initiative (see Figure 3).

Figure 3: Identifying Support Decisions

Figure 3: Identifying Support Decisions

By the way, decisions do not need to be transformational to deliver compelling business value. Optimizing boring decisions such as when a vehicle or jet engine needs servicing or how best to load balance the network given a spike in demand can also deliver a pretty nice ROI.

Step 3: Identify Technology And Organizational Requirements.  With the targeted operational initiative and supporting decisions in hand, the rest of the IoT technology and organizational requirements quickly fall into place, including:

  • Who are the stakeholders (technicians, mechanics, engineers, logistics managers, supply chain managers) impacted by the targeted operational initiative?
  • What descriptive, predictive and prescriptive questions do the stakeholders need to answer in support of their key decisions?
  • What additional data sources – both internal as well as external to the organization – should we consider (e.g., weather, traffic, technician notes, product specifications, field problem reports)?
  • What actionable recommendations (to the decisions that they are trying to make) do you need to deliver to the stakeholders in what timeframe and in what manner?
  • What data architecture and technologies do I need to support this process?

By the way, I will be covering this process in more detail at my “Developing A Big Data Business Strategy” session at Strata + Hadoop World on March 30 (see Figure 4).

Figure 4: Strata “Developing A Big Data Business Strategy” Session Framework

Figure 4: Strata “Developing A Big Data Business Strategy” Session Framework

Step 4: Contemplate “Right Time” As Well As “Real Time”. Not all decisions need to be made in real-time. For example, the article states:

…a company managing a fleet of delivery trucks can detect when truck parts are performing suboptimally and schedule these trucks for preventative maintenance long before the vehicle breaks down. These types of real-time decisions greatly reduce maintenance costs and improve the overall delivery performance of the fleet.

Determining and scheduling preventative maintenance is not a real-time decision. I am not going to rush a team of mechanics and parts to wherever the truck is located at the first sign that the vehicle is going to need maintenance. If I can predict that a truck is likely in need some maintenance, then I probably have a good estimate as to how soon I need to schedule that maintenance. And then I can schedule multiple maintenance activities to better reduce my maintenance costs and downtime for that truck (or wind turbine or jet engine or power generator or etc.).

Summary

The “Big Data Killer App” is the ability for organizations to couple new sources of data, such as social media, wearable computing and IoT, with data science to make better decisions. When you start aggregating all of those decisions across multiple use cases, then you have something that could be truly transformational to the business (see Figure 5).

Figure 5: Sample IoT Use Cases

Figure 5: Sample IoT Use Cases

[1] A “killer app” is any computer application that is so indispensable that it proves the core value of some larger technology. In this case, the IoT is being positioned as being that application that is so indispensable that everyone must adopt big data for their very survival.

Bill Schmarzo

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