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


Tesla vs. General Motors

2011 2017

$50Bn

$55Bn

$2.5Bn

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10,000,000

76,000 🚗


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.


Virtuous circle of data – start now!

Best data

Best products

Most customers

Analytics is more strategic than ever


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


Analytics is at the center of new digital business models


We are used to processes

generating data for analytics

BUSINESS

PROCESS

BUSINESS

INTELLIGENCE

A big change


But digital transformation is about analytics

creating new processes

BUSINESS

PROCESS

BUSINESS

INTELLIGENCE

A big change


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


At one level, nothing new …

I helped launch Business Objects

BusinessMiner in 1996 – 20 yrs. ago!

“Data Mining for the Masses”


But now algorithms really are taking over

Voice transcription Lip reading Image Descriptions


Detailed example: image recognition

Source: https://www.eff.org/ai/metrics


Thankfully, still some things AI is awful at (for now…)

Source: https://www.eff.org/ai/metrics


What’s in a name?

Old!

Partisan(?)

(deep learning is part of

ML, using neural

networks)


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


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)

https://cloud.saplumira.com/explorer?itemId=53044C6E5B755FF7E10000000A4E423F&type=HANALYTIC&initialMode=View

https://cloud.saplumira.com/explorer?itemId=53044C6E5B755FF7E10000000A4E423F&type=HANALYTIC&initialMode=View


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


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


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.”

http://www.mckinsey.com/global-themes/digital-disruption/whats-now-and-next-in-analytics-ai-and-automation


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


Artificial intelligence + analytics = “smart data discovery”

Preparing data Finding patterns

Sharing and operationalizing


« Une grande responsabilité

est la suite inséparable d’un

grand pouvoir »– Voltaire

Beware: ethics ahead!

(“with great power comes great responsibility”)


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.”

https://www.linkedin.com/pulse/risks-unfettered-ai-tony-bain

It’s time to monetize your data


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


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


“Most people have a better

inventory of their office furniture

than their own information assets”

Doug Laney, Gartner

First step: an information Inventory


SAP Data Network

www.sapdatanetwork.com

Things to do tomorrow


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


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


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”


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


Community & dialog

Conferences, user groups,

SAP events….

SAP Community network,

blogs, social media….

Thank you!

Timo Elliott

[email protected]

timoelliott.com

@timoelliott

timoelliott.com/cartoons