Five Hot Trends for 2018

Jake Freivald

Five Hot Trends for 2018Business outcomes and technology priorities in data and analytics

Product Marketing

December, 2017

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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Internet of Things

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Five Hot Trends for 2018

Internet of Things

Smarter devices in the IoT are increasing the need to centralize, contextualize, and manage data to improve decisions and business processes.

ManufacturingSharing smart device data can differentiate commodity products.

Government and Smart CitiesSensors can help central planners decide how to best deploy resources and regulate requirements.

Logistics and Supply ChainThe IoT can bring an aircraft, replacement parts, and a skilled tech to the same place at the same time.

Health CareThe 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

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

Embedded BI and Analytics

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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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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


Import all the data

legacy datawarehouse

othercloud

Physically load – and pay for –any needed data

Embedded

legacy datawarehouse

othercloud

Use data as needed

Swivel-chair analytics


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Predictions, Not “Predictive”

The Monty Hall Problem1. 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.


Or... go there!Use predictive analyticsWeather patternsTypical crime levels per typeConcerts and eventsSchool days and weekdaysHolidays and weekendsPaydaysShift / time of day....


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”


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


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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


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

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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


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 experiencesSometimes called “augmented intelligence”


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Data MonetizationCu

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McKinsey, 12/17

Data Monetization

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McKinsey, 12/17

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.

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

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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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

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

Recap

The Internet of Things Takes Off

The Enhanced Power of Embedded Analytics




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Questions?

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Five Hot Trends for 2018

Data & Analytics

Transcript of Five Hot Trends for 2018