Claro’s IoT Strategy Creation Guide: important in own right & symbolically

IoT_strategy_cards

Claro IoT Service Diagram Cards — collect the whole set!

Some IoT advances are as important symbolically (especially as key steps in the IoT’s maturation) as in their own right.

I consider Claro Partners‘s new “A Guide to Succeeding in the Internet of Things” in that vein, both showing that it’s not just enough to create a whizbang IoT device or app — you need a methodical strategy to maximize the benefits– and providing a very practical tool to create such a strategy. Written as the IoT reaches the top of the Gartner Hype Cycle, it aims at helping readers identify and meet real user needs and create viable business models. Based on several conversations at last night’s Boston IoT Meetup, it couldn’t be more timely, as (for example) smart home device sales slump, as reflected in Quirky’s bankruptcy.

Claro, in case you haven’t heard about them before, is headquartered in my favorite “smart city,” Barcelona, and is known for its Clayton Christensen-style emphasis on the opportunities presented by disruptive change (hmm: wonder if they have wei ji ideograms on the wall, LOL?), particularly with the IoT.

The Guide is a quick read, but can inspire you for a long time to come.

It’s divided into four portions, which I’m guessing codify the process that Claro uses internally to brainstorm strategies for its own clients:

  1. Define the challenge. “Identify a user-centric challenge to solve.”
  2. Ideate* the solution. “Create a solution that provides new value to the user.”
  3. Develop the offer. “Map out the ecosystem and interactions of your product and service.”
  4. Plan for production. “Identify resources needed and conduct gap analysis.”

They suggest you follow these steps sequentially, even if you already have a solution in mind, because “the exercises will help you to refine, develop or rethink it.”

Now for the details, which include very specific steps and some very helpful graphic aids.

First, Define the challenge. They stress you need to avoid being seduced by the lure of doing something just because it’s technologically possible. Make sure it meets a real
human need. The initial categories they suggest include:

    • Human Needs FrameworkAgeing population (sweeeeet! My “smart aging” paradigm shift!)
    • Work-life balance
    • Urban life
    • Health and wellbeing
    • Local Communities
    • Education
    • Sustainability/Shopping
    • Tourism, Family.

Then Claro suggests that your team go through a 30-minute process where it uses the four questions in this “human needs framework,” such as “what do people want to control?” and decide which challenge you’re going to design for (assume you could think big and try for one that meets multiple questions).

Second, Ideate the solution.  Similar to my “What can you do now that you couldn’t do before” question, this one asks you to not just use the IoT to refine a current approach to the issue you identified, but to “reimagine entirely new capabilities and value that an IoT service can deliver.”

This 40-min. process includes defining the person facing the challenge and aspects of their life, then brainstorming solutions to meet their real needs and how the IoT could be used to enable that solution.

Third, Develop the offer. They share my concern about proprietary IoT solutions, (which they label “intranet of things, LOL), and instead remind your team to, IFTTT-like,

IoT Service Diagram

IoT Service Diagram

“take advantage of the ecosystem enabled by the IoT to create interconnected services, experiences and business models.” In this process, which they estimate takes 40 minutes, you print out the IoT Service Diagram Cards (see above — I imagine “flipping” them and trading with the other kids on the playground, until our Moms throw out our collections…) and use them to map out how your idea will work, including drawing the data flow (don’t forget my dictum that data flow must be cyclical with the IoT!).  The important questions to ask — make sure to ask all of them! — include:

  • Will the device just provide information to the user or will it act on that information?
  • What are the specific inputs/outputs of the service? (eg. sight, sounds, touch, taste, smell, temperature)
  • Could the device learn through its use over time and adapt its behaviour accordingly?
  • Could the service use existing devices, data streams or interfaces?

Finally, in the fourth step, (30 minutes? Dream on!) the rubber hits the road, and you

IoT Canvas

IoT Canvas

Plan for Production!  Claro warns, “Don’t underestimate the complexity of bringing to life an offer that spans both the physical and digital, Do map out all the elements you’ll need to successfully develop and deliver your IoT offer.”

On the IoT Canvas, you bring together all the crucial considerations, such as manufacturing and logistics, revenues and costs, that must be nailed down to make the product affordable and profitable.  Specifically, Claro says you need to specifically state the offer’s value proposition to the end user, use the questions in each box on the form as prompts, fill out the rest of the canvas with details of the product and service idea, and write down “which resources, capabilities and processes you have, and which you’d need to acquire (gap analysis).”

I agree with Claro that these four steps, especially the last one, are iterative, and you need to revisit each of them throughout the entire conceptual and production process.

I have no doubt that, as IoT technology (especially miniscule, low-energy sensors) and experience continues to evolve, this process will be refined, but Claro has done the entire IoT industry, especially makers and entrepreneurs, a real service by codifying this approach and being willing to share it — after all, the IoT’s all about collaboration! 


*we’ll let them off with a warning from the Grammar Police this time. However, please, no more management babble in the future, OK?

 

Deloitte provides process for nuanced IoT strategy decisions

So much of the Internet of Things is still in the gee-whiz stage that we haven’t seen much in terms of nuanced IoT strategies. By that I mean ones that carefully weigh tradeoffs between companies and consumers to try to find strategies that are mutually beneficial and recognize there are new factors at play in IoT strategies, such as privacy and data mining, that may have positive or negative consequences for the customer/company interplay.

Deloitte’s “University” has made an important step in that direction with its “Power Struggle: Customers, companies and the Internet of Things” paper, co-authored by Brenna Sniderman and Michael E. Raynor.

In it, they explore how to create sustainable strategies that will be mutually beneficial to the customer and company — which are not always immediately apparent, especially when you explore the subtleties of how these strategies might play out in the new reality of the Internet of Things.

The study’s goal was to understand the factors that can distort IoT’s benefits, and instead create win-win IoT strategies.

Sniderman and Raynor suggest there are four quadrants into which a given strategy might fall:

  1. (the sweet spot!) “All’s well: Sufficient value is created, and that value is shared between customers and companies sufficiently equitably such that both parties are better off and feel fairly treated.
  2. “Hobson’s choice: A Hobson’s choice exists when you’re free to decide but only one option exists; thus, it is really no choice at all…. Even when customers come out ahead compared with their former options, their implied powerlessness can lead to feelings of unfairness.
  3. “Gridlock: In their quest for value capture, both sides are pulled in opposite directions, with neither able to move toward an optimal outcome. Here, both parties recognize IoT enablement as something that should lead to success, but neither party is able to reach it, since their competing interests or different value drivers are working at cross purposes.
  4. “Customer is king: Although particular IoT deployments might make economic sense for companies, customers end up capturing a disproportionate share of the new value created, pulling this outcome more in the customers’ favor; Craigslist is an obvious example.”

According to the authors, a key to finding the win-win, “all’s well” solution is the Information Value Loop (which I first discussed last Spring) that creates value out of the vast increase in information made possible by the IoT.

As I mentioned then, “This fits nicely with one of my IoT ‘Essential Truths,’ that we need to turn linear information flows into cyclical ones to fully capitalize on the IoT.” When you do that, it’s possible to design continuous improvement processes that feed back data from actual users to fine tune products and processes.  GE has found it leads to much shorter iterative loops to design improved versions of its products.

Here’s the gussied-up version of the cool hand-drawn visualization from the Deloitte brainstorming session that led to the Information Value Loop (print it & place it on your wall next to the one on privacy and security that I wrote about a while ago):

Deloitte Information Value Loop

The information no longer flows in linear fashion: it’s created from using sensors to record how things act in the real world, then goes through the various stages of the loop, each of which is made possible by one of the new technologies enabling the IoT.  The goal is either enhanced M2M integration among things, or improved actions by humans, and, to be sustainable over time:

“A value loop is sustainable when both parties capture sufficient value, in ways that respect important non-financial sensibilities. For example, retailer-specific and independent shopping apps can use past browsing and purchasing history—along with other behaviors—to suggest targeted products to particular customers, rather than showing everyone the same generic products, as on a store shelf. Customers get what they want, and companies sell more.

…  “The amount of value created by information passing through the loop is a function of the value drivers identified in the middle. Falling into three generic categories—magnitude, risk, and time—the specific drivers listed are not exhaustive but only illustrative. Different applications will benefit from an emphasis on different drivers.”

OK, so how does this theory play out?

Sniderman and Raynor picked a range of IoT-informed strategies to illustrate the concept, some of which may include unintended consequences that would harm/turn off customers or companies. For example, “An ill-considered push for competitive advantage could well overreach and drive away skittish customers. Alternatively, building too dominant an advantage may leave customers feeling exploited or coerced, a position unlikely to prove viable in the long term.”

Understanding the underlying structure of each type of loop is critical, because they naturally pull an IoT strategy in a particular, divergent way.

The example they pick to illustrate the “all’s well” quadrant of results is the dramatic increase in built-in diagnostic technology in cars.  This is of great personal interest: genetic testing has revealed that I am one of the approximately 10% of men who are missing the male car gene: I can’t stand the things, and view them as a big block of metal and plastic just waiting to develop problems (or, ahem, get hit by deer …), so I need all the help I can get. Sniderman and Raynor zero in on maintenance as one area for win-win benefits for drivers and dealers through the IoT:

“Customers often have little understanding of which repairs are necessary, feel inconvenienced by having to go without their car during maintenance periods, and are frustrated by potential overcharges. In response, automakers are embedding sensors that can run a wide range of reliable diagnostics, allowing a car to “self-identify” service issues, rather than relying on customers (“Where’s that squeaking coming from?”) or mechanics (“You might want to replace those brake pads, since I’ve already got the wheels off”). This creates a level of objectivity of obvious customer value and enables automakers to differentiate their products. Interactive features that work with customers’ information can further add value by, for example, potentially syncing with an owner’s calendar to schedule a dealership appointment at a convenient time and reserving a loaner vehicle for the customer, pre-programmed with his preferences to minimize the frustration of driving an unfamiliar car.

In this scenario, both parties collaborate to provide and act on data, in a mutual exchange of value. The customer captures value in multiple ways: He enjoys increased convenience and decreased frustration, improved vehicle performance and longer operating life, reduced maintenance charges, and—since almost everything about this interaction is automated—fewer occasions for perceived exploitation at the hands of unscrupulous service providers.

Value capture extends to companies in the form of ongoing customer interaction. Linking maintenance programming to the dealership encourages customers to return for tune-ups rather than go elsewhere, ideally leading to continued purchases in the long term. OEMs can also access data regarding vehicle maintenance issues and may be able to identify systematic malfunctions worthy of greater attention. Dealers also have an opportunity to make inroads into an untapped market: Currently, just 30 percent of drivers use the dealer for routine maintenance…”

Kumbaya! But then there’s the opposite extreme, according to Sniderman and Raynor, represented by smart home devices, which would lead to the lose-lose, gridlock scenario.  I think they seriously underestimate the understanding already by manufacturers in the field that they need to embrace open standards in order to avoid a range of competing standards (Zigbee, Bluetooth, etc.) that will force consumers to invest in a variety of proprietary, incompatible hubs, and therefore discourage them from buying anything at all.  All you have to do is look at new hubs, such as Amazon’s Echo, which can control devices from WeMo, Hue, Quirky, Wink — you name ’em, to realize that sharing data is already the norm with smart home devices.

Because this missive is getting long, I’ll leave it to you, dear reader, to investigate Sniderman & Raynor’s examples of the “customer is king” scenario, in which the customer grabs too much of the benefit (have to admit, a lot of the location-based IoT retail incentives still give me the creeps: I hate shopping under the best of circumstances, and having something pop up on my phone offering me an incentive based on my past purchases makes a bad experience even worse. How about you?); and the “Hobson’s choice” one, in which usage-based car insurance runs amok and insurers begin to charge unsafe drivers a surcharge — as documented by the devices such as Progressive’s “Snapshot” (I was dismayed to read in the article that Progressive is in fact doing that in Missouri, although I guess it’s a logical consequence of having objective evidence that someone consistently drives unsafely).

I can’t help thinking that the 800-pound gorilla in the room in many of these situations are the Scylla and Charybdis of the IoT, threats to privacy and security, and that makes it even more important that your IoT strategies are well thought out.

They conclude that, from my perspective, data isn’t just enough, you also need the decidedly non-technical tools of judgment and wisdom (aided by tools such as their Information Value Loop) to come up with a sustainable, mutually advantageous IoT strategy:

“Identifying where the bottlenecks lie (using the Information Value Loop), how each party is motivated to respond, and seeking to shape both incentives and the value loop itself puts companies more in control of their destinies.

“Second, taking a hard look at who benefits most from each IoT-enabled transaction, understanding when a lopsided value-capture outcome tips too far and becomes unsustainable, and taking steps to correct it may also lead to long-term success.

“Lastly, an honest assessment of where IoT investments may not have an appreciable benefit—or may decrease one’s potential for value capture—is just as crucial to a company’s IoT strategy as knowing the right places to invest.”

I may quibble with some of their findings, such as those about smart homes, but bravo to Sniderman and Raynor for beginning what I hope is a spirited and sustained dialogue about how to create sustainable, mutually-advantageous IoT strategies!  I’ve weighed in with my Essential Truths, but what are you thinking about this critical issue, often overlooked in our concentration on IoT technologies? 

McKinsey IoT Report Nails It: Interoperability is Key!

I’ll be posting on various aspects of McKinsey’s new “The Internet of Things: Mapping the Value Beyond the Hype” report for quite some time.

First of all, it’s big: 148 pages in the online edition, making it the longest IoT analysis I’ve seen! Second, it’s exhaustive and insightful. Third, as with several other IoT landmarks, such as Google’s purchase of Nest and GE’s divestiture of its non-industrial internet division, the fact that a leading consulting firm would put such an emphasis on the IoT has tremendous symbolic importance.

McKinsey report — The IoT: Mapping the Value Beyond the Hype

My favorite finding:

“Interoperability is critical to maximizing the value of the Internet of Things. On average, 40 percent of the total value that can be unlocked requires different IoT systems to work together. Without these benefits, the maximum value of the applications we size would be only about $7 trillion per year in 2025, rather than $11.1 trillion.” (my emphasis)

This goes along with my most basic IoT Essential Truth, “share data.”  I’ve been preaching this mantra since my 2011 book, Data Dynamite (which, if I may toot my own horn, I believe remains the only book to focus on the sweeping benefits of a paradigm shift from hoarding data to sharing it).

I was excited to see that the specific example they zeroed in on was offshore oil rigs, which I focused on in my op-ed on “real-time regulations,” because sharing the data from the rig’s sensors could both boost operating efficiency and reduce the chance of catastrophic failure. The paper points out that there can be 30,000 sensors on an rig, but most of them function in isolation, to monitor a single machine or system:

“Interoperability would significantly improve performance by combining sensor data from different machines and systems to provide decision makers with an integrated view of performance across an entire factory or oil rig. Our research shows that more than half of the potential issues that can be identified by predictive analysis in such environments require data from multiple IoT systems. Oil and gas experts interviewed for this research estimate that interoperability could improve the effectiveness of equipment maintenance in their industry by 100 to 200 percent.”

Yet, the researchers found that only about 1% of the rig data was being used, because it rarely was shared off the rig with other in the company and its ecosystem!

The section on interoperability goes on to talk about the benefits — and challenges — of linking sensor systems in examples such as urban traffic regulation, that could link not only data from stationary sensors and cameras, but also thousands of real-time feeds from individual cars and trucks, parking meters — and even non-traffic data that could have a huge impact on performance, such as weather forecasts.  

While more work needs to be done on the technical side to increase the ease of interoperability, either through the growing number of interface standards or middleware, it seems to me that a shift in management mindset is as critical as sensor and analysis technology to take advantage of this huge increase in data:

“A critical challenge is to use the flood of big data generated by IoT devices for prediction and optimization. Where IoT data are being used, they are often used only for anomaly detection or real-time control, rather than for optimization or prediction, which we know from our study of big data is where much additional value can be derived. For example, in manufacturing, an increasing number of machines are ‘wired,’ but this instrumentation is used primarily to control the tools or to send alarms when it detects something out of tolerance. The data from these tools are often not analyzed (or even collected in a place where they could be analyzed), even though the data could be used to optimize processes and head off disruptions.”

I urge you to download the whole report. I’ll blog more about it in coming weeks.

GE & IBM make it official: IoT is here & now & you ignore it at your own risk!

Pardon my absence while doing the annual IRS dance.

While I was preoccupied, GE and IBM put the last nail in the coffin of those who are waiting to launch IoT initiatives and revise their strategy until the Internet of Things is more ….. (supply your favorite dismissive wishy-washy adjective here).

It’s official: the IoT is here, substantive, and profitable.

Deal with it.

To wit:

The two blue-chips’ moves were decisive and unambiguous. If you aren’t following suit, you’re in trouble.

The companies accompanied these bold strategic moves with targeted ones that illustrate how they plan to transform their companies and services based on the IoT and related technologies such as 3-D printing and Big Data:

  • GE, which has become a leader in 3-D printing, announced its first FAA-approved 3-D jet engine part, housing a jet’s compressor inlet temperature sensor. Sensors and 3-D printing: a killer combination.
  • IBM, commercializing its gee-whiz Watson big data processing system, launched Watson Health in conjunction with Apple and Johnson & Johnson, calling it “our moonshot” in health care, hoping to transform the industry.  Chair Ginny Rometty said that:

“The Watson Health Cloud platform will ‘enable secure access to individualized insights and a more complete picture of the many factors that can affect people’s health,’ IBM says each person generates one million gigabytes of health-related data across his or her lifetime, the equivalent of more than 300 million books.”

There can no longer be any doubt that the Internet of Things is a here-and-now reality. What is your company doing to catch up to the leaders and share in the benefits?

 

Deloitte’s IoT “Information Value Loop”: critical attitudinal shift

Ever so often it’s good to step back from the day-to-day minutia of current Internet of Things projects, and get some perspective on the long-term prospects and challenges.

That’s what Deloitte did last December, when it held an “Internet of Things Grand Challenge Workshop,” with a focus on the all-important “forging the path to revenue generation.”

The attendees included two of my idols: John Seely Brown and John Hagel, of Deloitte’s “Center for the Edge” (love the pun in that title!).

The results were recently released, and bear close examination, especially the concept of how to foster what they call the “Information Value Loop”:

Deloitte IoT Information Value Loop

Deloitte IoT Information Value Loop

“The underlying asset that the IoT creates and exploits is information, yet we lack a well- developed, practical guide to understand how information creates value and how companies can effectively capture value. The ‘Information Value Loop’ describes how information creates value, how to increase that value, and how understanding the relevant technology is central to positioning an organization to capture value. The Information Value Loop is one way to begin making sense of the changes we face. The Loop consists of three interconnected elements: stages, value drivers, and technologies. Where the stages and value drivers are general principles defining if and how information creates value under any circumstances, it is the specifics of today’s technology that connect the Loop to the challenges and opportunities created by the IoT.”

This fits nicely with one of my IoT Esssential Truths,” that we need to turn linear information flows into cyclical ones to fully capitalize on the IoT.  No pussy-footin’ about this for these guys: “For information to create any value at all, it must pass through all the stages of the Loop. This is a binary outcome: should the flow of information be blocked completely at any stage, no value is created by that information.”

IMHO, this is also going to be one of the biggest challenges of the IoT for management: in the days when it was sooo difficult to gather and disseminate information, it made sense for those in the C-suite to control it, and parcel out what they felt was relevant, to whom and when they felt it was relevant. More often than not, the flow was linear and hierarchical, with one information silo in the company handing on the results to the next after they’d processed it. That didn’t allow any of the critical advantages the IoT brings, of allowing everyone who needs it to share real-time data instantly.  But saying we need to change those information management practices is one thing: actually having senior management give up their gatekeeper functions is another, and shouldn’t be understated as a challenge.

So here are some of the other key points in the conference proceedings:

  • In line with the multi-step strategy I outlined in Managing the Internet of Things Revolution, they concluded that incremental improvements to existing processes and products are important, but will only take you so far, at which point radical innovation will be crucial: “At first blush, the early IoT emphasis on sustaining innovation seems reasonable. Performance and cost improvement are seldom absent from the priorities of stakeholders; they are relatively easy to measure and their impact is likely more immediate than any investment that is truly disruptive. Put simply, the business case for an IoT application that focuses on operational efficiencies is relatively easy to make. Many decision makers are hard-wired to prefer the path of less resistance and, for many, truly innovative IoT applications seem too far-flung and abstract to risk pursuing. Still, organizations cannot innovate from the cost side forever.”
  • Melding the public and private, “Cities have inherent societal challenges in place to serve as natural incubators of IoT solutions.” Yeah!
  • As in everything else, those contrarian Millennials (who aren’t so hung up on buying stuff and often prefer to just use it)  are likely to save us when it comes to the IoT:  “From an innovation perspective … some of the new technologies are first marketed at the consumers. Thus, many believe that near-term innovation in IoT applications will come out of the consumer sector – spurred by the emergence of the tech-savvy Millennial consumers as a driving economic force.”
  • As I’ve written before, while some customers will still prefer to buy products outright, the IoT will probably bring a shift from selling products to marketing services based on those products, creating new revenue streams and long-term relationships with customers: “As IoT makes successful forays into the world of consumer and industrial products, it may radically change the producer—buyer transactional model from one based on capital expenditure to one based on operating expenditure. Specifically, in a widely adopted IoT world, buyers may be more apt to purchase product service outcomes on some kind of “per unit” basis, rather than the product itself and in so doing, render the physical product as something more of an afterthought. The manufacturer would then gradually transform into a service provider, operating on a complete awareness of each product’s need for replenishment, repair, replacement, etc.”

    Or, a hybrid model may emerge: “What may ultimately happen in a relatively connected product world is that many may accept the notion of the smartly connected product, but in a limited way. Such people will want to own the smartly connected product outright, but will also accept the idea of sharing the usage data to the limited extent that the sellers use such data in relatively benign ways, such as providing advice on more efficient usage, etc. The outcome here will also rely upon a long term total cost of ownership (TCO) perspective. With any fundamental purchasing model changes (as is taking place in owned vs. cloud resources in the network / IT world), not all suppliers will be able to reap additional economic benefit under the service model. Buyers will eventually recognize the increase in TCO and revert back to the more economical business model if the economic rents are too high.”

  • It’s likely that those players in the IoT ecosystem who create value-added data interpretation will be the most valuable and profitable: “…are certain building blocks of the IoT network “more equal” than others?

    “Some have argued that the holy grail of the IoT value loop resides in the data and that those in the IoT ecosystem who aggregate and transform massive amounts of raw data into commercially useful intelligence capture the real value in the IoT environment. This notion holds that commercially useful data provide insights that drive action and ultimately represent the reason that the end user pursues a smart solution in the first place. Put another way, the end customer is more apt to pay for a more comprehensive treatment of raw data than for a better sensor. Indeed, some even believe that as time passes, the gap in relative value captured by those who curate and analyze the data and the rest of the IoT ecosystem will only widen and that, on a long-term basis, players within the “non-data” part of the IoT ecosystem will need to develop some data analytics capabilities simply to differentiate themselves as something more than commodity providers. Of course, some think that the emphasis on data is overblown and argue that where the real value in the IoT ecosystem is captured depends on application. Time will tell of course. But there can be little doubt that the collection and enhancement of data is highly coveted, and analytics and the ability to make use of the vast quantities of information that is captured will serve as critical elements to virtually any IoT solution.”

I urge you to download and closely analyze the entire report. It’s one of the most thoughtful and visionary pieces of IoT theory I’ve seen (no doubt because of its roundtable origins: in keeping with the above-mentioned need for cyclical information flow for the IoT [and, IMHO, creativity in general], the more insights you can bring together on a real-time basis, the richer the outcome. Bravo!

 

IBM picks for IoT trends to watch this year emphasize privacy & security

Last month Bill Chamberlin, the principal analyst for Emerging Tech Trends and Horizon Watch Community Leader for IBM Market Development (hmmm, must have an oversized biz card..) published a list of 20 IoT trends to watch this year that I think provide a pretty good checklist for evaluating what promises to be an important period in which the IoT becomes more mainstream.

It’s interesting to me, especially in light of my recent focus on the topics (and I’ll blog on the recent FTC report on the issue in several days), that he put privacy and security number one on the list, commenting that “Trust and authentication become critical across all elements of the IoT, including devices, the networks, the cloud and software apps.” Amen.

Most of the rest of the list was no surprise, with standards, hardware, software, and edge analytics rounding out the top five (even though it hasn’t gotten a lot of attention, I agree edge analytics are going to be crucial as the volume of sensor data increases dramatically: why pass along the vast majority of data, that is probably redundant, to the cloud, vs. just what’s a deviation from the norm and probably more important?).

Two dealing with sensors did strike my eye:

9.  Sensor fusion: Combining data from different sources can improve accuracy. Data from two sensors is better than data from one. Data from lots of sensors is even better.

10.  Sensor hubs: Developers will increasingly experiment with sensor hubs for IoT devices, which will be used to offload tasks from the application processor, cutting down on power consumption and improving battery life in the devices”

Both make a lot of sense.

One was particularly noteworthy in light of my last post, about the Gartner survey showing most companies were ill-prepared to plan and launch IoT strategies: “14.  Chief IoT Officer: Expect more senior level execs to be put in place to build the enterprise-wide IoT strategy.” Couldn’t agree more that this is vital!

Check out the whole list: I think you’ll find it helpful in tracking this year’s major IoT developments.

Gartner study confirms senior managers don’t understand IoT

Posted on 21st February 2015 in Internet of Things, M2M, management, manufacturing, marketing, strategy

The “Managing the Internet of Things Revolution” e-guide I wrote for SAP was aimed at C-level executives. Even though it’s proven popular enough that the company is translating it into several languages, it appears we need to redouble our efforts to Managing_the_Internet_of_Things_Revolutionbuild IoT awareness among executives.

I say that because Gartner has just come out with a survey confirming my suspicions: even though a lot of companies now think the IoT will have a major effect on them, they’re clueless about how to manage it and most have yet to launch major IoT initiatives.

In fact, “many survey respondents felt that the senior levels of their organizations don’t yet have a good understanding of the potential impact of the IoT.” (my emphasis)

 

That’s despite the fact that a key conclusion of my guide was that (even though the IoT is a long way from full maturity) companies can and should begin their IoT strategies and implementation now, because they can already achieve significant savings in operating costs, improve marketing, and create new revenue streams with the current early stage sensors and analytical tools. Getting started will also build their confidence and familiarity with IoT tools and strategy before they begin more dramatic transformational strategies.

Consider these findings from the survey of 463 business and IT leaders:

  • 40% of companies think the IoT will at least bring new short-term revenue and cost reduction opportunities in the next three years — or perhaps even transform them. More than 60% think that will be true over 5 years or more.
  • Fewer than 25% said their company had “established clear business leadership for the IoT,” — even among the companies predicting a significant  – this includes those who said they expect the IoT to have a significant or transformational impact, says Gartner (however, 35% of them came from this group).
  • Yet, few have delegated specific responsibility for IoT strategy and management: “… less than one-quarter of survey respondents has established clear business leadership for the IoT, either in the form of a single organizational unit owning the issue or multiple business units taking ownership of separate IoT efforts.”
  • “attitudes toward the IoT vary widely by industry. For example, board of directors’ understanding of the IoT was rated as particularly weak in government, education, banking and insurance, whereas the communications and services industries scored above-average ratings for senior executive understanding of the IoT.”

Gartner concluded most companies have yet to really create IoT strategies:

“‘The survey confirmed that the IoT is very immature, and many organizations have only just started experimenting with it,’ said Nick Jones, vice president and distinguished analyst at Gartner. ‘Only a small minority have deployed solutions in a production environment. However, the falling costs of networking and processing mean that there are few economic inhibitors to adding sensing and communications to products costing as little as a few tens of dollars. The real challenge of the IoT is less in making products ‘smart’ and more in understanding the business opportunities enabled by smart products and new ecosystems.’ However, a lack of clear business or technical leadership is holding back investment in the technology.” (my emphasis)

In line with my current preoccupation, privacy and security, the survey did show companies are concerned with both issues, as well as with finding talented new staff who understand the IoT and how to benefit from it. According to Steve Kleyhans, Gartner’s research vp:

 “While a single leader for the IoT is not essential, leadership and vision are important, even in the form of several leaders from different business units. We expect that over the next three years, more organizations will establish clear leadership, and more will recognize the value of some form of an IoT center of excellence because of the need to master a wide range of new technologies and skills.”

If you haven’t launched any IoT projects or begun to create a strategy, the writing’s on the wall: get going!


Carpe diem: I take this survey as an omen that there’s a desperate need for When Things Can Talk: profiting from the Internet of Things revolution,” my proposed full-length book on IoT corporate strategy. Let me know if you can suggest a possible publisher!

Management Challenge: Lifeguards in the IoT Data Lake

In their Harvard Business Review November cover story, How Smart, Connected Products Are Transforming Competition, PTC CEO Jim Heppelmann and Professor Michael Porter make a critical strategic point about the Internet of Things that’s obscured by just focusing on IoT technology: “…What makes smart, connected products fundamentally different is not the internet, but the changing nature of the “things.”

In the past, “things” were largely inscrutable. We couldn’t peer inside massive assembly line machinery or inside cars once they left the factory, forcing companies to base much of both strategy and daily operations on inferences about these things and their behavior from limited data (data which was also often gathered only after the fact).

Now that lack of information is being removed. The Internet of Things creates two unprecedented opportunities regarding data about things:

  • data will be available instantly, as it is generated by the things
  • it can also be shared instantly by everyone who needs it.

This real-time knowledge of things presents both real opportunities and significant management challenges.

Each opportunity carries with it the challenge of crafting new policies on how to manage access to the vast new amounts of data and the forms in which it can be accessed.

For example: with the Internet of Things we will be able to bring about optimal manufacturing efficiency as well as unprecedented integration of supply chains and distribution networks. Why? Because we will now be able to “see” inside assembly line machinery, and the various parts of the assembly line will be able to automatically regulate each other without human intervention (M2M) to optimize each other’s efficiency, and/or workers will be able to fine-tune their operation based on this data.

Equally important, because of the second new opportunity, the exact same assembly line data can also be shared in real time with supply chain and distribution network partners. Each of them can use the data to trigger their own processes to optimize their efficiency and integration with the factory and its production schedule.

But that possibility also creates a challenge for management.

When data was hard to get, limited in scope, and largely gathered historically rather than in the moment, what data was available flowed in a linear, top-down fashion. Senior management had first access, then they passed on to individual departments only what they decided was relevant. Departments had no chance to simultaneously examine the raw data and have round-table discussions of its significance and improve decision-making. Everything was sequential. Relevant real-time data that they could use to do their jobs better almost never reached workers on the factory floor.

That all potentially changes with the IoT – but will it, or will the old tight control of data remain?

Managers must learn to ask a new question that’s so contrary to old top-down control of information: who else can use this data?

To answer that question they will have to consider the concept of a “data lake” created by the IoT.

“In broad terms, data lakes are marketed as enterprise wide data management platforms for analyzing disparate sources of data in its native format,” Nick Heudecker, research director at Gartner, says. “The idea is simple: instead of placing data in a purpose-built data store, you move it into a data lake in its original format. This eliminates the upfront costs of data ingestion, like transformation. Once data is placed into the lake, it’s available for analysis by everyone in the organization.”

Essentially, data that has been collected and stored in a data lake repository remains in the state it was gathered and is available to anyone, versus being structured, tagged with metadata, and having limited access.

That is a critical distinction and can make the data far more valuable, because the volume and variety will allow more cross-fertilization and serendipitous discovery.

At the same time, it’s also possible to “drown” in so much data, so C-level management must create new, deft policies – to serve as lifeguards, as it were. They must govern data lake access if we are to, on one hand, avoid drowning due to the sheer volume of data, and, on the other, to capitalize on its full value:

  • Senior management must resist the temptation to analyze the data first and then pass on only what they deem of value. They too will have a crack at the analysis, but the value of real-time data is getting it when it can still be acted on in the moment, rather than just in historical analyses (BTW, that’s not to say historical perspective won’t have value going forward: it will still provide valuable perspective).
  • There will need to be limits to data access, but they must be commonsense ones. For example, production line workers won’t need access to marketing data, just real-time data from the factory floor.
  • Perhaps most important, access shouldn’t be limited based on pre-conceptions of what might be relevant to a given function or department. For example, a prototype vending machine uses Near Field Communication to learn customers’ preferences over time, then offers them special deals based on those choices. However, by thinking inclusively about data from the machine, rather than just limiting access to the marketing department, the company shared the real-time information with its distribution network, so trucks were automatically rerouted to resupply machines that were running low due to factors such as summer heat.
  • Similarly, they will have to relax arbitrary boundaries between departments to encourage mutually-beneficial collaboration. When multiple departments not only share but also get to discuss the same data set, undoubtedly synergies will emerge among them (such as the vending machine ones) that no one department could have discovered on its own.
  • They will need to challenge their analytics software suppliers to create new software and dashboards specifically designed to make such a wide range of data easily digested and actionable.

Make no mistake about it: the simple creation of vast data lakes won’t automatically cure companies’ varied problems. But C-level managers who realize that if they are willing to give up control over data flow, real-time sharing of real-time data can create possibilities that were impossible to visualize in the past, will make data lakes safe, navigable – and profitable.

Good Checklist for Creating #IoT Strategy

Still not ready to tackle an analysis of the November Harvard Business Review cover story, by PTC CEO Jim Heppelmann and Professor Michael Porter, on How Smart, Connected Products Are Transforming Competition, but I did want to do a shout-out to a companion piece, Digital Ubiquity: How Connections, Sensors, and Data Are Revolutionizing Business, by two HBS profs, Marco Iansiti and Karim R. Lakhani.

In particular, I wanted to suggest that you use the last section of the paper, “Approaching Digital Ubiquity,” as a checklist of priorities to create your own IoT strategy (I’d be remiss if I didn’t also mention my “Managing the Internet of Things Revolution” i-guide and this blog’s “Essential Truths” as references as well..).

Here are their points, and my reflections on them:

  1. Apply the digital lens to existing products and services.
    This is a profound transformation, because we’ve become so accustomed to working around the gaps in our knowledge that were the reality in an analog world.As Iasanti and Lakhani say, you now need to ask:
    “What cumbersome processes in your business or industry are amenable to instrumentation and connectivity?
    Which ones are most challenging to you or your customers?”
  2. Connect your existing assets across companies.
    We “get” competition, but collaboration, especially with competitors, is a little less instinctive.

    “If you work in a traditional analog setting, examine your assets for new opportunities and look at other industries and the start-up world for new synergies. Your customer connections are especially valuable, as are your knowledge of customers’ needs and the capabilities you built to meet knowledge of customers’ needs and the capabilities you built to meet them. Nest is connecting with public utilities to share data and optimize overall energy usage. If you work in a start-up, don’t just focus on driving the obsolescence of established companies. Look at how you can connect with and enhance their value and extract some of it for yourself.knowledge of customers’ needs and the capabilities you built to meet them. Nest is connecting with public utilities to share data and optimize overall energy usage. [my note: this is a great example of thinking expansively: even though your product is installed in individual homes, if data can be aggregated from many homes, it can be of real value on a macro scale as well. The smart grid is a great example of bringing all components of energy production, distribution, and use together into an integrated system.]  If you work in a start-up, don’t just focus on driving the obsolescence of established companies. Look at how you can connect with and enhance their value and extract some of it for yourself.”

  3. Examine new modes of value creation.
    Just because you make tangible products doesn’t mean that you’re limited to just selling those products to make money in the future. You’ll be able to make money by selling customers actionable data that will allow them to improve productivity and reduce maintenance. Perhaps you’ll stop selling altogether, and make money instead by making your products the cornerstone of profitable services.

    Begin to ask:
    “What new data could you accumulate, and where could you derive value from new analytics?”
    “How could the data you generate enable old and new customers to add value?”

  4. Consider new value-capture modes.
    “Could you do a better job of tracking the actual value your business creates for others?”
    “Could you do a better job of monetizing that value, through either value-based pricing or outcomes-based models?”
  5. Use software to extend the boundaries of what you do.
    You will still make products, as in the past, and that gives you a tangible basis for the future. But you’ll need a digital component as well.

    “Digital transformation does not mean that your company will only sell software, but it will shift the capability base so that expertise in software development becomes increasingly important. And it won’t render all traditional skills obsolete. Your existing capabilities and customer relationships are the foundations for new opportunities. Invest in software-related skills that complement what you have, but make sure you retain those critical foundations. Don’t jettison your mechanical engineering wizards—couple them with some bright software developers so that you can do a better job of creating and extracting value.”

    What do you think?  Any more questions you’d add? Let me know!

I’ll be on SAP Radio Again Today: the IoT and Big Data

I’ll be on SAP’s “Coffee Breaks With Game Changers” radio again today, live @ 2 EST, appearing again with SAP’s David Jonker, again talking about the IoT and Big Data.  This time I plan to speak about:

  • Integrating real-time and historic data in decision-making:  in the past, it was so hard to glean real-time operating data that we had to operate on the basis of inferring about how to manage the future based on analysis of past data.  Now we have a more difficult challenge: learn to balance past and real-time data.
  • Sharing data in real-time: In the past, data trickled down from top management and might (or might not) eventually get to operators on the shop floor.  Now, everyone can get immediate access to it. Will senior managers continue to be the gatekeepers, or will everyone have real-time access to the data that might allow them to do their jobs more effectively (for example, fine-tuning production processes).

  • Revolutionizing decision-making: Decision-making will also change, because of everyone being able to have simultaneous access to data. Does it really make sense any more for sequential decision-making by various siloed departments when they might all benefit by making the decisions simultaneously and collaboratively, based on the data?

Tune in!

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