Internet of Things (IoT) Expert Session Webinar

Rebecca Wettemann
Expert Sessions
IoT
Jake Freivald
Information Builders
Nucleus Research
Incorporated

Rebecca Wettemann
+1-617-720-2000
rwettemann@nucleusresearch.com
IOT: FROM HYPE TO VALUE

ABOUT NUCLEUS RESEARCH
 Leader in measuring the value of
technology
 Leader in evidence-based ROI analysis;
more than 600 published case studies
 Founded in 2000, Boston HQ
 Primary clients: business decision makers
Registration #108024
Copyright 2018 Nucleus Research

LET’S TALK ABOUT…
 IOT promises – and reality
 The three Cs of IoT
 Currency
 Clarity
 Consumability
 Identifying value opportunities
 Looking ahead

THE IOT PROMISE
 Better customer experience
 More efficient cities
 Self-correcting supply chains
 No more empty refrigerators!

THE IOT REALITY
 Smarter devices are increasing the need to
centralize, contextualize, and manage data
to improve business processes and
decision making
 Fewer than 5% of companies selling IoT-
enabled products have operationalized
device data value
 Challenges lie in currency, clarity,
consumability … and cost

THE IOT REALITY
 It’s not just about sensors and computing
at the edge
 Embedded analytics in context bring
currency, clarity, and consumability

CLARITY
 What data is important?
 What is IoT “noise”?
 What is the data telling us?
 Embedded analytics bring decisions – and
action – to the edges because of greater
clarity.

CONSUMABILITY
 How is IoT pushed, parsed, and delivered
for meaningful decision making/process
triggers?
 Operationalized analytics systemizes the
ingestion and parsing of IoT data to deliver
it in a consumable context at the point of
consumption.

CURRENCY
 When and how often are sensors/devices
delivering data?
 Is data an absolute or a delta?
 How often is updated data needed for
process triggers / decision making?
 To build a relevant and cost-effective
currency model, we need to consider the
half life of data.

THE HALF LIFE OF DATA
The measurement of the value of business
data can be broadly defined using the
concept of half life.
Just as radioactive materials diminish at
different rates based on their characteristics,
the value of data diminishes over time. The
rate of decline in data value can be correlated
to the tempo of its use in a company’s
decision making processes.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 5 10 15 20 25 30 35 40 45 50
Tactical
Operational
Strategic

APPLYING HALF-LIFE TO DATA QUALITY
 The average value of data quality varies significantly based on
whether that data is used to make tactical, strategic, or
operational decisions.
 The value of retaining and analyzing historical data for predictive
and future planning purposes also varies significantly based on
whether that data will be primarily used for tactical, strategic, or
operational decisions.
 Most companies will need to be tactical, strategic, and operational
in their decision making cadence at different times depending on
their product mix, product life cycle, and market conditions. This
creates a need for an information architecture that is flexible
enough to support all three cadences.

MEASURING DATA VALUE
 The difference in agility and the value of
rapid access to relevant and trusted
business data depends not on the type of
industry but on the characteristics of the
decision-making horizons required to
deliver differentiated products/services to
the market.
 The value of data diminishes based on the
cadence of decisions.

DECISION CADENCE
 Tactical - driving process changes in near-
real time
 Operational - driving changes that take
days or weeks to implement
 Strategic - driving changes that become
part of a quarterly or longer planning and
implementation process

USING THE HALF LIFE OF DATA
 To prioritize new data quality
initiatives
 To evaluate the productivity
and other impacts of
improving data quality
 To drive a consistent and
effective business case
 To manage ongoing resource
investments in data quality
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 5 10 15 20 25 30 35 40 45 50
Tactical Operational
Strategic

BUSINESS CASE – KEY TIPS
 A good business case isn’t exhaustive, just structured
 A few strong benefits are better than a lot of weak ones
 Break your case process down into manageable steps

THREE STEPS TO A BUSINESS CASE
 Identify the top areas of
benefit
 Calculate costs and benefits
 Assess ROI and payback
1.Identify
2. Quantify
3. Assess

THE FIVE FACTORS THAT DRIVE VALUE
 Breadth
 Repeatability
 Risk
 Collaboration
 Knowledge
Copyright 2018 Nucleus Research 21

Breadth
The more people, applications, or channels a project touches, the
greater the potential return.

Repeatability
The greater the frequency of use, the greater the potential return.

Risk
The greater the likelihood of a project to reduce risk, the greater the
potential return.

Collaboration
The greater the potential of an application to support collaboration, the
greater the potential return.

Knowledge
The more a project has the potential to disseminate knowledge, the greater
the potential return.

 Identify the top areas of
benefit
 Initial and ongoing
 Software, hardware,
consulting, personnel, training
 Direct and indirect
1.Identify
2. Quantify
3. Assess

 Identify the top areas of benefit
 Initial and ongoing
 Software, hardware, consulting, personnel,
training
 Direct and indirect
 ROI – compare to other
investments
 Payback – excellent measure of
risk, flexibility
1.Identify
2. Quantify
3. Assess

LOOKING AHEAD
 The future of IoT is in embedded analytics, not devices
 Sensors are the signals, not the nervous system
 IoT delivers most value when it is operationalized – with embedded
analytics in the network, not the core

WRAPUP
 Currency, clarity, consumability key to value in IoT
 It’s not the devices on the edge but intelligence throughout
 Half-life can help structure your data quality priorities
 Use breadth, repeatability as your guide
 ROI, payback are key metrics
 Additional resources at NucleusResearch.com

Internet of Things
32
Balancing Edge Computing with Centralization & Contextualization

Internet of Things
Smarter devices in the IoT are increasing the need to
centralize, contextualize, and manage data to improve
decisions and business processes.
Manufacturing
Sharing smart device data
can differentiate
commodity products.
Government and Smart Cities
Sensors can help central
planners decide how to best
deploy resources and regulate
requirements.
Logistics and Supply Chain
The IoT can bring an aircraft,
replacement parts, and a
skilled tech to the same place
at the same time.
Health Care
The IoT can provide data that can
predict, prevent, and help prosecute
fraud, theft, and inefficiencies that
affect patient outcomes.

Computing power at the edge drives autonomous action
closer to the device location to improve response times and
reduce traffic to the central processor
Internet of Things
In humans, this is called the reflex arc...
...the greatest strength and weakness of which is that
it doesn’t involve the brain.
Photo credit: bit.ly/2AiDXeX

Why “Centralize, Contextualize, and Manage” Data?
Parts PlanePeople

Embedded BI and Analytics
Your brain
It’s always with you, and always on.
...and you don’t need to tap into all of it at once.
You don’t need to do anything special
to interact with it.
...though you may need to focus it sometimes.
Why shouldn’t analytics be the same?
36

IoT Use Cases
37
AutoZone monitors the driving
performance of delivery
vehicles servicing local repair
shops.
The cars awaiting those parts
are typically tying up a service
bay and impacting
productivity, revenue, and
customer wait times.

IoT Use Cases
Richmond, British Columbia, has
high flood risks. Their drainage
system has water level metering
devices that alert civil engineers to
proactively manage potentially
dangerous risks.
Mexico City has a planned project to
monitor the emissions of vehicles on
the highways. Fines will be given to
vehicles with poor emission ratings
in order to manage air quality.

IoT Use Cases
Ikari manufactures IoT-
enabled pest control devices
that help food processing
facilities and food store
chains monitor pest situations
in real-time throughout their
locations.

40
IoT Use Cases: Lipari Warehouse Management
40
What is next at Lipari?

41
IoT Use Cases: Lipari Visual Warehouse Facility Analytics
41
Supply Chain Warehouse Analytics for Optimization and Performance
Real time 3D Graphic (SVG) view of a warehouse gives you the ability to visualize each location,
color coded depending on Warehouse attribute of your choice. You can define filters, drills
downs and location-based analytics on events that matter most to you.
Near Real-Time Tracking:
 Weight Placement
 Size of Cases
 Cost / Damage Prone Areas
 Hazard Items
 Movement/Number of Visits
 Cross-Contamination
 Labor
 Congestion
 Top Selling Items

Questions?
42
Use the On24 Q&A facility

Internet of Things (IoT) Expert Session Webinar

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

Similaire à Internet of Things (IoT) Expert Session Webinar

Similaire à Internet of Things (IoT) Expert Session Webinar (20)

Plus de ibi

Plus de ibi (20)

Dernier

Dernier (20)

Internet of Things (IoT) Expert Session Webinar