Business outcomes and technology priorities in data and analytics.

1. Jake Freivald
Five Hot Trends for 2018
Business outcomes and technology priorities in data and analytics
Product Marketing
December, 2017

2. The Promise of 2018
The Internet of Things Takes Off
The Enhanced Power of Embedded Analytics
Predictions, not “Predictive”
Real Artificial Intelligence
Data Monetization for a Happy CFO
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3. Internet of Things
3
Five Hot Trends for 2018

4. 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.

5. 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

6. Internet of Things
A dirty little secret
Just like “Big Data” is becoming “data”
...the “Internet of Things” is already mostly just the Internet
Needs communication, context, integration, mastering, analytics,
data discovery, information delivery, reporting, scoring, and presentation
2018 trends to watch: “Cloud to the edge ”
More IoT deployments go cloud-based

7. Embedded BI and Analytics
7
Five Hot Trends for 2018

8. 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?
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9. Embedded BI and Analytics
Issues driving 2018 changes
Pendulum swing: Centralized to
decentralized and back again
Embedded is generally not an area
for standalone analytical tools
Analytics adoption has hit a wall
Right information, right time is a
mantra, but hasn’t been fulfilled
SaaS application adoption

10. Embedded BI and Analytics
Import all the data
legacy data
warehouse
other
cloud
Physically load – and pay for –
any needed data
Embedded
legacy data
warehouse
other
cloud
Use data as needed
Swivel-chair analytics

11. Predictions, not “Predictive”
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Five Hot Trends for 2018

12. Predictions, Not “Predictive”
The Monty Hall Problem
1. You pick a door (say, #1).
2. Monty shows you another
door, empty (say, #2).
3. He offers to let you switch
to #3, or stay with #1.
What do you choose?
First, the answer: Always switch.
Second, it doesn’t matter whether you
agree with me or not: Every statistician
knows that’s the right answer.
Third, most ordinary people will go round
and round (and round) with this problem.
The analytics? Hard.
The prediction? Easy.
Lesson: Give the prediction.

13. Predictions, Not “Predictive”
Or... go there!Use predictive analytics
Weather patterns
Typical crime levels per type
Concerts and events
School days and weekdays
Holidays and weekends
Paydays
Shift / time of day
....

14. Predictions, Not “Predictive”
Market watch
Predictive analytics suddenly becomes AI or machine learning
(For that matter, lots of things do. More on that in a moment.)
Prescriptive analytics goes the same route
Market watch
Despite the need for predictions,
vendors will tout predictive analytics
“for the businessperson”

15. Predictions, Not “Predictive”
2018 areas we’ll see growth in “predictive analytics” (or shrink-wrapped predictions)
Healthcare: better treatment outcomes
Supply chain management: automated supplier, routing choices
Financial services: though with skepticism / throttling
Customer relationships: e.g., best-offer optimization

16. Real Artificial Intelligence
16
Five Hot Trends for 2018

17. Real Artificial Intelligence
What is it?
To some extent, who cares?
 Self-directing vacuum?
 Autonomous farming vehicle?
...okay, fine, some terms
 Algorithms
 Machine learning
”
“Transform
nature of workthe
and the structure
of the workplace

18. Real Artificial Intelligence
What is it?
To some extent, who cares?
 Self-directing vacuum?
 Autonomous farming vehicle?
...okay, fine, some terms
 Algorithms
 Machine learning
”
“highly scoped
machine-learning
solutions that target
a specific task

19. 19
Real Artificial Intelligence
Pattern matching across
heterogeneous data sets, e.g.,
 Metadata
 Data
 Analytical objects
Specific tasks such as...?
Anomaly detection
 Repeated data quality issues
 Match/merge assistance
 False positives or negatives
 Identifying patterns slightly
above the noise floor for
humans to investigate

20. Real Artificial Intelligence

21. Real Artificial Intelligence
Is there anything new?
“Cheap gas”
 Storage
 Computing power
 Bandwidth
AI swarms
...and where will we see failures in 2018?
“AI helps with unbiased decision-making”
“Take humans out of the equation”
To do it right
Help humans, don’t replace them
Create advanced user experiences
Sometimes called “augmented intelligence”

22. Data Monetization for a Happy CFO
22
Five Hot Trends for 2018

23. Data Monetization
Current,
saturating
McKinsey, 12/17

24. Data Monetization
Areasfor
growth
McKinsey, 12/17

25. McKinsey, 12/17
The changes are new and
continuing
Across industries, most
respondents agree that the
primary objective of their
data-and-analytics activities is
to generate new revenue.... Of
the 41 percent of respondents
whose companies have begun
to monetize data, a majority
say they began doing so just in
the past two years.

26. Defining Your Data Monetization Strategy
Step 1 – Which Information Will Deliver the Most Value?
Step 2 – Where is the Data Coming From?
Step 3 – Is the Data Ready to Be Monetized?
Step 4 – Can All Stakeholders Participate in Data Monetization?
Monetizing Data

27. Data Monetization
Our experience: externally facing BI applications will yield more measurable returns
Enhance customer “stickiness.” Customers spend more time with
you, which gives you more opportunities to interact with them
Competitive advantage. It can give you a leg up on competitors as
you offer more value-added services
Process improvement. Reduce cost and eliminate bottlenecks
Increase market share. New data-and-analytics products can open
doors you once had a problem opening
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28. Data Monetization
Possible types of monetization
Benchmarking!
Offering analytics on top of
commodity products
Unify your data with external data
(e.g., weather, economy)
for additional insights
Interactive e-statements

29. Data Monetization
Considerations
Make sure your house is in order:
mastered and suitable quality data
Customer-facing applications need
high-quality data – they know
themselves better than you do
Data products often need more than
just what you get natively; you might
end up reselling data

30. Recap
The Internet of Things Takes Off
The Enhanced Power of Embedded Analytics
Predictions, not “Predictive”
Real Artificial Intelligence
Data Monetization for a Happy CFO
30

31. Questions?
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