Live Blogging from SAP’s HANA IoT event

Hmm. Never been to Vegas before: seems designed to bring out the New England Puritan in me. I’ll pass on opulence, thank you very much…

 SAP HANA/ IoT Conference

SAP HANA/ IoT Conference

Up front, very interested in a handout from Deloitte, “Beyond Linear,” which really is in line with speech I’ll give here tomorrow on the IoT “Essential Truths,” in which one of my four key points will be that we need to abandon the old, linear flow of data for a continuous cyclical one.  According to Deloitte’s Jag Bandia,

“Among users with a complete, 360-degree view of relevant data for each specific process can help avoid missed opportunities. The ‘all data’ approach means relevant data can and should come from anywhere — any application, any system, any process — not just the traditional channels associated with the process.”

Bravo!

First speaker: SAP Global Customers Operations CTO Ifran Khan:

  • “digital disruption”: catalyst for change & imperative to go digital.
  • digression about running going digital (I put in my 30 minutes this morning!!!), creating a totally new way of exercising (fits beautifully with “Smart Aging“!)
  • new macro tech trends are enabling digitalizations: hyper-connectivity, super computing, cloud computing, smart world, and cybersecurity (horrifying stat about how many USB sticks were left in dry cleaning!)
  • those who don’t go digital will go under…. (like John Chambers’ warning about IoT).
  • new opportunities in wide range of industries
  • need new digital architectures — “driving locality of data, integrated as deep as possible into the engine.
  • HOLY COW! He starts talking about a circular, digitally-centered concept, with a buckyball visual.  Yikes: great minds think alike.
  • sez HANA allows a single platform for all digital enterprise computing.
  • running things in real-time, with no latency — music to my ears!

Jayne Landry, SAP:

  • too few in enterprise have real-time access to analytics — oh yeah!
  • “analytics for everyone”
  • “own the outcome”
  • “be the one to know”
  • SAP Cloud for Analytics — “all analytics capabilities in one product.” real-time, embedded, consumer-grade user experience, cloud-based. Looking forward to seeing this one!
  • “Digital Boardroom” — instant insight. Same info available to board also available to shopfloor — oh yeah — democratizing data!

Very funny bit by Ty Miller on using SAP Cloud for Analytics to analyze Area 51 data. Woo Woo!

Ifran Khan again:

  • how to bring it to the masses? Because it’s expensive and difficult to maintain on the premises, extend and build in cloud! Add new “micro services” to SAP HANA cloud platform: SAP Application Integration, Tax Service, Procurement, Customer Engagement, Predictive, and, ta da, IoT.
  • video of Hamburg Port Authority. Absolutely love that and what they’re doing with construction sites!

Jan Jackman, IBM:

  • customers want speed. Cloud is essential. IBM & HANA are partners in cloud…

This guy is sooo neat: Michael Lynch, IoT Extended Supply Chain for SAP (and former opera student!):

  • “Connecting information, people, and things is greatest resource ever to drive insightful action.”
  • “big deal is the big data processing potential is real & chips are cheaper, so you can build actual business solutions”
  • STILL gmbh (forklifts) great example!
  • phase 1: connect w/ billions of internet-enabled things to gain new insights
  • phase II: transform the way you make decisions and take action
  • phase III: re-imagine your customer’s experience.
  • they do design thinking workshops — would luv one of those!
  • great paradigm shift: Hagleitner commercial bathroom supplies
  • Kaeser compressors: re-imaging customer service
  • working with several German car companies on enabling connected driving
  • once again, the  Hamburg Port Authority!!

SAP’s strategy:

  • offers IoT apps. platforms, and facilitates extensions of IoT solutions
  • work closely with Siemens: he’s talked with them about turbine business.
  • SAP has several solutions for IoT
  • Cloud-based predictive maintenance!
  • “social network for assets”: Asset Intelligence Network
  • They did the Harley York PA plant! — one line, 21-day per bike to 6 hrs.  (displays all around the plant with KPIs)
  • 5 layers of connectivity in manufacturing “shop floor to top floor”  SAP Connected Manufacturing
  • They have a IoT Starter Kit — neat
  • SAP Manufacturing Integration and Intelligence
  • SAP Plant Connectivity
  • SAP Event Stream Processor
  • SAP MobiLink
  • SAP SQL Anywhere/SAP ultralite
  • 3rd Party IoT Device Cloud (had never heard of “device cloud” concept — specialize in various industry verticals).

“Becoming an Insight-Driven Organization”  Speakers: Jag Bandla and Chris Dinkel of Deloitte.

  • Deloitte is using these techniques internally to make Deloitte “insight-driven”
  • “an insight-driven organization (IDO) is one which embeds analysis, data, and reasoning into every step of the decision-making process.” music to my ears!
  • emphasis on actionable insight
  • “when humans rely on their own experiences and knowledge, augmented by a stream of analytics-driven insights, the impact on value can be exponential”
  • benefits to becoming an IDO:
    • faster decisions
    • increased revenue
    • decreased cost of decision making
  • challenges:
    • lack of proper tech to capture
    • oooh: leaders who don’t understand the data…
  • 5 enabling capabilities:
    • strategy
    • people
    • process
    • data
    • tech
  • developing vision for analytics
  • Key questions: (only get a few..)
    • what are key purchase drivers for our customers?
    • how should we promote customer loyalty?
    • what customer sentiments are being expressed on social media?
    • how much should we invest in innovation?
  • Value drivers:
    • strategic alignment
    • revenue growth
    • cost reduction
    • margin improvement
    • tech
    • regulation/compliance
  • Organize for success (hmm: I don’t agree with any of these: want to decentralize while everyone is linked on a real-time basis):
    • centralized (don’t like this one, with all analyzed in one central group.. decentralize and empower!)
    • consulting: analysts are centralized, but act as internal consultants
    • center of excellence: central entity coordinates community of analysts across company
    • functional: analysts in functions such as marketing & supply chain
    • dispersed: analysts scattered across organization, little coordination
  • Hire right people! “Professionals who can deliver data-backed insights that create business value — and not just crunch numbers — are the lifeblood of an Insight-Driven Organization”
    • strong quantitative skills
    • strong biz & content skills (understand content and context)
    • strong data modeling & management skills
    • strong IT skills
    • strong creative design skills (yea: techies often overlook the cool design guys & gals)
  • Change the mindset (critical, IMHO!):
    • Communicate: build compelling picture of future to steer people in right direction.
    • Advocate: develop cohort of leaders to advocate for program.
    • Active Engagement: engage key figures to create pull for the program
    • Mobilize: mobilize right team across the organization.
  • How do you actually do it? 
    • improve insight-to-impact with “Exponential Biz Processes” — must rebuild existing business processes!  Involves digital user experience, biz process management, enterprise science, all data, and IT modernization.
      • re-engineer processes from ground up
      • develop intuitive, smart processes
      • enable exception-based management
  • Data:
    • “dark data:” digital exhaust, etc. might be hidden somewhere, but still actionable.
      • they use it for IoT: predictive personalization (not sure I get that straight…).
    • want to have well-defined data governance organization: standards, data quality, etc.
  • Technology: digital core (workforce engagement, big data & IoT, supplier collaboration, customer experience
    • HANA
  • Switch to digital delivery: visualizations are key!
    • allow for faster observations of trends & patterns
    • improve understanding & retention of info
    • empower embedded feeds and user engagement

 

IoT and the Data-Driven Enterprise: Bob Mahoney, Red Hat & Sid Sipes, Sr. Director of Edge Computing, SAP

  • What’s driving enterprise IoT?
    • more connected devices
    • non-traditional interactions such as M2M and H2M
    • ubiquitous internet connectivity
    • affordable bandwidth
    • cloud computing
    • standards-based and open-source software
  • Biz benefits:
    • economic gains
    • new revenue streams (such as sale of jet turbine data)
    • regulatory compliance
    • efficiencies and productivity
    • ecological impact
    • customer satisfaction
  • example of Positive Train Control systems to avert collisions. Now, that can be replaced by “smarter train tech”
  • SAP and edge computing (can’t move all of HANA to edge, but..)
    • improve security in transmission
    • reduce bandwidth need
    • what if connection goes down
    • actual analysis at the edge
    • allows much quicker response than sending it to corporate, analyzing & send it back
    • keep it simple
    • focused on, but not limited to, IoT
  • they can run SQL anywhere on IoT, including edge: SQL Anywhere
  • Red Hat & SAP doing interesting combination for retail, with iBeacons, video heat map & location tracking: yields real insights into consumer behavior.

Remember: The IoT Is Primarily About Small Data, Not Big

Posted on 16th March 2015 in data, Internet of Things, M2M, management, manufacturing, open data

In one of my fav examples of how the IoT can actually save lives, sensors on only eight preemies’ incubators at Toronto’s Hospital for Sick Children yield an eye-popping 90 million data points a day!  If all 90 million data points get relayed on to the “data pool,” the docs would be drowning in data, not saving sick preemies.

Enter “small data.”

Writing in Forbes, Mike Kavis has a worthwhile reminder that the essence of much of the Internet of Things isn’t big data, but small. By that, he means:

a dataset that contains very specific attributes. Small data is used to determine current states and conditions  or may be generated by analyzing larger data sets.

“When we talk about smart devices being deployed on wind turbines, small packages, on valves and pipes, or attached to drones, we are talking about collecting small datasets. Small data tell us about location, temperature, wetness, pressure, vibration, or even whether an item has been opened or not. Sensors give us small datasets in real time that we ingest into big data sets which provide a historical view.”

Usually, instead of aggregating  ALL of the data from all of the sensors (think about what that would mean for GE’s Durathon battery plant, where 10,000 sensors dot the assembly line!), the data is originally analyzed at “the edge,” i.e., at or near the point where the data is collected. Then only the data that deviates from the norm (i.e., is significant)  is passed on to to the centralized data bases and processing.  That’s why I’m so excited about Egburt, and its “fog computing” sensors.

As with sooo many aspects of the IoT, it’s the real-time aspect of small data that makes it so valuable, and so different from past practices, where much of the potential was never collected at all, or, if it was, was only collected, analyzed and acted upon historically. Hence, the “Collective Blindness” that I’ve written about before, which limited our decision-making abilities in the past. Again, Kavis:

“Small data can trigger events based on what is happening now. Those events can be merged with behavioral or trending information derived from machine learning algorithms run against big data datasets.”

As examples of the interplay of small and large data, he cites:

  • real-time data from wind turbines that is used immediately to adjust the blades for maximum efficiency. The relevant data is then passed along to the data lake, “..where machine-learning algorithms begin to understand patterns. These patterns can reveal performance of certain mechanisms based on their historical maintenance record, like how wind and weather conditions effect wear and tear on various components, and what the life expectancy is of a particular part.”
  • medicine containers with smart labels. “Small data can be used to determine where the medicine is located, its remaining shelf life, if the seal of the bottle has been broken, and the current temperature conditions in an effort to prevent spoilage. Big data can be used to look at this information over time to examine root cause analysis of why drugs are expiring or spoiling. Is it due to a certain shipping company or a certain retailer? Are there re-occurring patterns that can point to problems in the supply chain that can help determine how to minimize these events?”

Big data is often irrelevant in IoT systems’ functioning: all that’s needed is the real-time small data to trigger an action:

“In many instances, knowing the current state of a handful of attributes is all that is required to trigger a desired event. Are the patient’s blood sugar levels too high? Are the containers in the refrigerated truck at the optimal temperature? Does the soil have the right mixture of nutrients? Is the valve leaking?”

In a future post, I’ll address the growing role of data scientists in the IoT — and the need to educate workers on all levels on how to deal effectively with data. For now, just remember that E.F. Schumacher was right: “small is beautiful.”

 

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.

Egburt: key tool to make IoT pay off NOW

Posted on 31st October 2014 in data, energy, Internet of Things, maintenance, management, retail

As I’ve remarked before, writing the Managing the Internet of Things Revolution e-guide to IoT strategy for SAP was an eye-opener for me, shifting my attention from the eye-popping opportunities for radical reinvention through the IoT (products as services, user-customizable products, seamless smart phone-car integration, etc.) to very practical ways the IoT could begin optimizing companies’ current operations TODAY (BTW: much-deserved shout-out to SAP’s Mahira Kalim: it was dialogue with her that led to this insight!).

Egburt

In that vein, I was blown away at this week’s IoT Global Summit by the roll-out of Egburt by Camgian.

Egburt stresses two crucial, inter-related obstacles to widespread IoT solution deployment by mainstream businesses:

  • low cost-of-ownership sensing (by using very little energy, thereby extending battery life)
  • reducing potentially huge cloud-computing costs (because of the sheer volume of 24/7 sensor data) by allowing “fog computing,” where the processing would be done right at the collection process, with only the small amount of really relevant data being passed on to a central location.

The highlight of the product launch was a live demo of Egburt in real-time use at a chain of dollar stores in the south, monitoring a wide range of factors, from floor traffic to freezer operation (Camgian pointed out the system paid for itself in the first month of operation when it recorded failure of a freezer when the store was unoccupied, in time for immediate repairs to avoid loss of frozen foods).

Think about it: the very volume of Big Data possible with constant monitoring by a whole range of sensors can also be the IoT’s undoing. Since all that’s of interest in many cases is data that deviates from the norm, doesn’t it make sense to process that data at the collection point, then only pass on the deviations?

The company has targeted three IoT segments:

  • retail to reduce heating and lighting, and maximize sales through tracking foot traffic patterns to optimize product placement.
  • infrastructure: with sensors at key points such as bridges that will detect flooding and stress.
  • smart cities: optimizing emergency response.

In a sponsored white paper by ABI Research, “Evolution of the Internet of Things: from connected to intelligent devices,” they documented the benefits of going beyond first-generation, “connected,” IoT devices that were just sensors collecting and passing on data, to a second generation of “intelligent ones” such as Egburt the combine sensors and processing and offer not only lower operating costs but also — critically — more data security:

  • “Communication Latency: Handling more processing at the network’s edge reduces latency from the device’s actions. Use cases that are highly time-sensitive and require immediate analysis of, or response to, the collected sensor data are, in general, unfeasible under cloud- centric IoT architectures, especially if the data are sent over long distances.
  • “Data Security: By and large, sensitive and business-critical operational data are safer when encrypted adequately on the endpoint level. Unintelligent devices transmitting frequent and badly secured payloads to the cloud are generally more vulnerable to hacking and interception by unauthorized parties. Additionally, many enterprises may need to secure and control their machine data on the edge level for compliance reasons.
  • “Total Cost of Ownership: Perhaps most significantly, the paradigm shift can reduce the IoT systems’ total cost of ownership, or TCO. Intelligent devices are usually more expensive than less sophisticated alternatives, but their TCO over a long service life can be substantially lower.”

IMHO, for the IoT to be widely deployed, especially in SMEs, devices such as Egburt that reduce the cost of collecting and processing data are a critical component.


(PROMINENT DISCLAIMER: I actually won a FitBit in Camgian’s drawing at the conference. That has no impact on this review. Had I won the iPhone 6 that they also gave away, I would have totally been in the bag, LOL…)