BI+Analytics Conference Trends Webinar on Top Analytic Trends
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Transcript of BI+Analytics Conference Trends Webinar on Top Analytic Trends
Timo Elliott, SAP
July 2017
Disruption Ahead: The Big Trends in Business Intelligence and Analytics
2© 2017 SAP SE or an SAP affiliate company. All rights reserved.
Introduction
Analytics is more strategic than ever
Artificial intelligence is taking over the world
It’s time to monetize your data
Things to do tomorrow
Conclusion
Agenda
3© 2017 SAP SE or an SAP affiliate company. All rights reserved.
4© 2017 SAP SE or an SAP affiliate company. All rights reserved.
Tesla vs. General Motors
2011 2017
$50Bn
$55Bn
$2.5Bn
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10,000,000
76,000 🚗
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In the future, all products will be self-improving
“Last year, it was still quite humanlike when it
played,” said Mr. Ke after the game. “But this year,
it became like a god of Go.”
“AlphaGo is improving too fast,” he said in a news
conference after the game. “AlphaGo is like a
different player this year compared to last year.”
SAP CoPilot is an Enterprise Digital Assistant --
what Siri, Alexa, and Cortana are to consumers.
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Virtuous circle of data – start now!
Best data
Best products
Most customers
Analytics is more strategic than ever
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Technology priorities for 2017 and beyond
Rank Technology Trend
1 BI/Analytics
2 Cloud
3 Digitalization / Digital Marketing
4 Infrastructure & Data Center
5 Mobile
6 Cyber and information security
7 Industry-Specific Applications
8 ERP
9 Networking, Voice, and Data Comms
Ten out of
Twelve years
2006-2017
ANALYTICS
#1Source: Gartner
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Analytics is at the center of new digital business models
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We are used to processes
generating data for analytics
BUSINESS
PROCESS
BUSINESS
INTELLIGENCE
A big change
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But digital transformation is about analytics
creating new processes
BUSINESS
PROCESS
BUSINESS
INTELLIGENCE
A big change
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Support decision-making, not just information gathering
Make analytics and decision-making a robust, end-to-end, continuously improving process
I believe that analytics is THE most important business process in organizations today –
but we don’t treat it like one
Artificial Intelligence
is taking over the world
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At one level, nothing new …
I helped launch Business Objects
BusinessMiner in 1996 – 20 yrs. ago!
“Data Mining for the Masses”
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But now algorithms really are taking over
Voice transcription Lip reading Image Descriptions
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Detailed example: image recognition
Source: https://www.eff.org/ai/metrics
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Thankfully, still some things AI is awful at (for now…)
Source: https://www.eff.org/ai/metrics
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What’s in a name?
Old!
Partisan(?)
(deep learning is part of
ML, using neural
networks)
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Artificial intelligence
AI is a “sociotechnical construct” indicating machine capabilities which solve complex tasks that were
recently only possible by humans (equally well or better)
Technical
disciplines that
solve business
problems through
the extraction of
knowledge from
data.
Deep learning
Machine learning
Data Science
Big Data
Advanced
Analytics
Predictive
Analytics
Data
mining
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Predictive analytics: easy first step
Model deployed using
In-Database-Apply
Customer Database
Hancock, John M 38 D Y 4.2 N Y
Doe, Jane F 45 M Y 9.4 N N
Red, Simply F 18 S N 2.1 N Y
SQL Dataset w/ Scoring
Business Users can get on-the-fly
scoring without even knowing they
are using predictive algorithms
BI Artifact
(or even just a dataset)
SAP BI (3.x/4.x)
Embedded into any application
SQL
(Or any other application)
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Predictive trends
At “peak of inflated expectations”
Predictive is a lower priority than data
discovery/self-service, data quality,
governance, …
But higher use of predictive is … predicted
The top users of predictive are now BI Experts
& Business Analysts – not data scientists
Biggest challenges: greater volume & variety
of data, operationalizing predictive, usability,
skills/understanding
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Situational
Awareness
What do I need
to do right now?
Prediction
What can I expect
to happen?
Suggestion
What do you
recommend?
Notification
What do I need
to know?
Perception
What’s happening
now?
Artificial intelligence-powered processes
Automation
What should I
always do?
Prevention
What can I
avoid?
Requires:
• Large amounts of robust training data
• Decision process expertise
• Embedding in process flows
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Machine learning everywhere
Source; McKinsey & Company
Machine Learning has
broad potential across
industries and use
cases
“the value of the potential benefits
of automation—calculated as a
percentage of operating costs—
could range from between 10–
15% for a hospital emergency
department to 25% for aircraft
maintenance, and to more than
90% for mortgage origination.”
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It’s mostly about automation
Source: http://gartner.com/SmarterWithGartner
“In 2018, half a billion users will save two hours a day
thanks to AI-powered tools.” Gartner
But also about AI = Augmented Intelligence
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Artificial intelligence + analytics = “smart data discovery”
Preparing data Finding patterns
Sharing and operationalizing
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« Une grande responsabilité
est la suite inséparable d’un
grand pouvoir »– Voltaire
Beware: ethics ahead!
(“with great power comes great responsibility”)
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Minimum: make sure there’s monitoring + overrides!
https://www.linkedin.com/pulse/risks-unfettered-ai-tony-bain
“Implementing machine learning systems with
real world influence, without a “sanity” override
can lead to undesired consequences.”
It’s time to monetize your data
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From the Vs to the Ms
“By 2020 – 10 percent of organizations will
have a highly profitable business unit
specifically for productizing and
commercializing their information assets.”
Gartner
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Despite everything, data is still not treated as a strategic asset
5%
28%
26%
20%
21%
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
“My organization values
our information as a
strategic asset – in
words, and actions”
Source: audience survey, Gartner Data & Analytics, London
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“Most people have a better
inventory of their office furniture
than their own information assets”
Doug Laney, Gartner
First step: an information Inventory
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SAP Data Network
www.sapdatanetwork.com
Things to do tomorrow
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What goes wrong? people and process!
Source: Gartner, basis of approx. 2000 assessments from 2010 to 2016
Overall: 2.7
Business outcomes:
3.16
People: 2.62
Program Management:
2.82
Process: 2.54
Technology & data: 2.75
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It’s all about culture …
1%
8%
6%
45%
40%
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
“The most significant
internal roadblock that I
face in my role is culture
and change management”
Source: audience survey, Gartner Data & Analytics, London
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BI Competency Centers vs. Analytic Communities of Excellence
• Acquire and model the data
• Create data warehouses
• Create semantic models
• Author reports
• Grant access
• Provision, certify data, enable access
• Innovation lab
• Identify common requirements
• Consult on new projects
• Provide pool of experts, including
advanced analytics
• Evangelize best practices, success
stories
• Facilitate training and skills advancement
BICC: Do Most ACE: Do Some -- Enable More
“We fly the planes”
“We provide the airport & control tower”
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Some tips
There are no magic tools. It
will always take effort
People will always want
more than you can provide
People want self-service without
education. Not possible.
Giving people the best paintbrush in
the world will not make them Picasso
Iteration has to be
welcomed, not penalized
Develop data visualization
expertise
Your job is to make people
think
Take human behavior into
account
Conclusion
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Community & dialog
Conferences, user groups,
SAP events….
SAP Community network,
blogs, social media….