BI2017 Analytics Innovation, Disruption, and Transformation

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@timoelliott

Timo Elliott, SAP

Analytics Innovation, DisruptionAnd Transformation

© 2017 SAP SE or an SAP affiliate company. All rights reserved. 2

Agenda

Top Trends

Supporting “Modern BI”

Big Data Architectures

Predictive & Machine Learning

Organizing for Data

Wrap-up

Top Trends


Top Strategic Technology Trends, 2017

INTELLIGENT

DIGITAL

MESH

Applied AI & Advanced Machine Learning

Intelligent Apps

Intelligent Things

Virtual & Augmented Reality

Digital Twins

Blockchains and Distributed Ledgers

Adaptive Security Architecture

Digital Technology Platforms

Mesh App and Service Architecture

Conversational Systems

Source: Gartner Identifies the Top 10 Strategic Technology Trends for 2017 (Gartner, 2016)

http://www.gartner.com/newsroom/id/3482617


Technology Priorities for 2017 and Beyond

Rank Technology Trend

1 BI/Analytics2 Cloud3 Digitalization / Digital Marketing4 Infrastructure & Data Center5 Mobile6 Cyber and information security7 Industry-Specific Applications8 ERP9 Networking, Voice, and Data Comms

Ten out ofTwelve years2006-2017

ANALYTICS

#1Source: Gartner


WE ARE USED TO PROCESSES GENERATING DATA FOR ANALYTICS

BUSINESSPROCESS

BUSINESSINTELLIGENCE

A Big Change


BUT DIGITAL TRANSFORMATION IS ABOUT ANALYTICS CREATING NEW PROCESSES

BUSINESSPROCESS

BUSINESSINTELLIGENCE

A Big Change


By 2020, information will be used to

reinvent, digitalize, or

eliminate 80%of business processes and products

from a decade earlier.

From The Back Office To The Business Models of Future

”

“

Source: Gartner

© 2017 SAP SE or an SAP affiliate company. All rights reserved. 9© 2017 SAP SE or an SAP affiliate company. All rights reserved.

Analytics Enables Live Business


BI Success …

“BI initiatives described as ‘successful’ dropped from 41% to 35% in 2015”

Techtarget, 2015

Source: New reports highlight state of BI reporting tools (TechTaget, 2015)

http://webcache.googleusercontent.com/search?q=cache:ytcBuSnp_34J:searchbusinessanalytics.techtarget.com/news/4500247197/New-reports-highlight-state-of-BI-reporting-tools+&cd=1&hl=en&ct=clnk&gl=us



Are you a BI-nosaur?


Complaints…

31% wait days or weeks for an average BI request

32% say Enterprise BI too complex, complicated,

cumbersome to use

Enterprise systems don’t have all the data needed --

>45% from outsideSource: Forrester

http://www.tms-visualindex.com:8080/DemoDocsIndex/Forrester_Agile_TLP.pdf


The Penetration of BI Remains Low

“Close to 40% of organizations report fewer than 10% of employees using BI”Source: New reports highlight state of BI reporting tools, Techtarget, 2015



Are You The Taxi Company?

Supporting “Modern BI”


Data-Driven Approach

Push:• From IT• Data-Driven• Data to Insight• Technology-Centric

A.S.P.I.R.E.


Value-Driven Approach

Pull:• From LOB• Outcome-Driven• Insight to Data• Use-Case-Centric


Combination Approach

Push:• From IT• Data-Driven• Data to Insight• Technology-Centric

Pull:• From LOB• Outcome-Driven• Insight to Data• Use-Case-Centric


“Modern BI”

DATA Self-servicedata preparation

Structured/Unstructured

Internal/External

Batch/Streaming

Integration, blending

Cleansing, augmentation

Agile modeling

BI DBColumnar

In-memory

Self-servicedata analysis

Data discovery

Visual exploration

Dashboards/storytelling

Agile Iteration

Now considered “optional!”Data warehouseSemantic layers

OLAP Cubes


Invest in Self-Service Data Discovery Tools

“Through 2020 spending on self-service visual discovery and data preparation market will grow 2.5x faster than traditional IT-controlled tools for similar functionality”

– IDC, 2015


Invest in Self-Service Data Preparation

SAP Agile Data Preparation

I.e., “Data Blending” — combine, merge, cleanse data


Invest in Predictive Analytics

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


SAP BusinessObjects Cloud

Big Data Architectures


You Need Both of These…


A Common Question

“We like SAP ERP (and HANA), we like Hadoop, and your BI tools are a standard. But we don’t understand how it’s all going to fit together. Help!”


“Classic” Enterprise Hadoop Use Cases

Semi-structured data loading / processing• First web data, now IoT/documents/images, etc.

Offload traditional relational DW• Typically no reduction in existing DW, but new data increasingly tiered

Queryable alternative to tape backups• E.g., when upgrade to different ERP system, keep copy of all old data


A “Modern Data Architecture” Example


Other Interesting Hadoop Use Cases

Fast scale up/down• Game apps company: big fan of Teradata, and found it cheaper to run than Hadoop, but when

individual games became a hit, they needed to be able to scale up (and down) fast

Avoid “brittle” ETL, push schema creation to the business• Large investment bank had dozens of different CRM setups, thousands of ETL jobs that kept

breaking – kept traditional DW, but added data lake -- “it’s all in there – have fun!”

Excel on steroids/exploration• Big, one-off decisions• We don’t know what we don’t know

Customer-facing “analytics”• Gas bill, etc.


Sandboxing/Data Extensions


Not Just a Data Store – A Platform

Far more than a batch-driven data store• Many still have an out of date view – it’s now based on Yarn/Spark, etc.• ”Data at Rest and Data in Motion”• But still not for “transactions” any time soon

Still maturing, still a lot of work, but has proved enterprise value• In particular, overcame biggest security & auditing concerns – Kerberos integration, encryption,

tokenization, Apache Ranger, … • Low capital costs to try things out (but don’t underestimate time/training/expertise needed)

Considered the heart of “digital transformation” in some large organizations…• ...At least by the team implementing Hadoop! (but there’s typically a large ”traditional IT”

modernization effort going on at the same time)


Result of All This: Data Complexity For The Foreseeable Future

Data Warehouse

Hybrid Transaction/

Analytical Processing

Hadoop,MongoDB,Spark, etc. Personal

Data / BI

Where does data arrive?When does it need to move?Where does modeling happen?What can users do themselves?What governance is required?

Big Data Architectures got complicated

What we would like — consistent, seamless solution

Data

Feeds


SAP HANA VoraWhat’s Inside and What Does It Do?

DemocratizeData Access

Make PrecisionDecisions

SimplifyBig DataOwnership

SAP HANA Vora is an in-memory query engine that leverages and extends the Apache Spark execution framework to provide enriched interactive analytics on Hadoop. Drill Downs on HDFS

Mashup API EnhancementsCompiled Queries

HANA-Spark AdapterUnified LandscapeOpen Programming

Any Hadoop Clusters


SAP Predictive Analytics 3.0 & Hadoop

Native Spark Modeling

Standalone or included in SAP HANA

Predictive Factory

Integration with cloud & other apps


SAP HANA DW – Future-proof data management platform


Future Vision: More “Black Box” Approach

Predictive & Machine Learning

38© 2017 SAP SE or an SAP affiliate company. All rights reserved.

Random…

I helped launch Business Objects BusinessMiner in 1996 – 20 yrs ago!

“Data Mining for the Masses”


Retail Predictive Analytics Example

SAP BusinessObjects Mobile showing store managers near real-time sales compared to prior day/week


What Food to Make, When?

Knowledge

Check Past Sales

Check Forecast

Check Must Stock

Run and Check Range

Tool

Set 60%/70% Fixed First Production

Hot Food Continuous

Replenishment

All Other Food Monitor for 2nd

and 3rd Variable

Productions


What Food to Make, When? (cont.)

Trading Patterns

Core Range

Weather

Special Events


Internal Data

External Data

Slow and Steady DataTransactional,

Changeable Data

POS Data

Deliveries

Store Attributes

Store Org Structure

Store Placement

Store Staff

Store Visibility, Signage

Competitor Store Attributes

Census, ONS Data

POI Data

GIS Competitor and Cannibalization

Footfall

Weather

Events

Real Estate

Choosing a New Store Location


“It is mind-blowing how versatile and nimble our data warehouse is on SAP HANA.”

Agile self-service with SAP HANA and SAP Lumira. 9 years of data, structured & unstructured

Healthcare Example


”Does this 86-year old grandmother really need the same knee as the professional linebacker?”

Benchmarking Surgical Procedures


Benchmarking Surgical Procedures

“Using surgical procedure data to help achieve $9.42 million in cost reductions, eliminate or minimize the use of certain surgical products, reduce variation in surgical protocols, establish best practices across surgical departments and ensure quality post-operative results for patients.”

Source: http://www.himss.org/sites/himssorg/files/mercy-periop-case-study.pdf

http://www.himss.org/sites/himssorg/files/mercy-periop-case-study.pdf

http://www.himss.org/sites/himssorg/files/mercy-periop-case-study.pdf


Predictive Analytics

Develop expertise in treating breast cancer and type II diabetes


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?


The Challenge of AI & Humans Working Together

“Anything you can do, AI can do better…”


« Une grande responsabilité est la suite inséparable d’un grand pouvoir »

-Voltaire

Beware: Ethics Ahead!

(“with great power comes great responsibility”)

Organizing For Data


BICCs Are Dead?

Long live ACEs:“Analytic Communities

of Excellence”!


Embrace Shadow IT

Don’t fight back — be a co-conspirator …

40% of users are using an equal amount or more of homegrown applications

Source: http://sapassets.edgesuite.net/sapcom/docs/2015/09/541ccd61-437c-0010-82c7-eda71af511fa.pdf

http://sapassets.edgesuite.net/sapcom/docs/2015/09/541ccd61-437c-0010-82c7-eda71af511fa.pdf


Updating the Traditional BICC to Include Community

A Business Intelligence Competency Center (BICC) is a cross-functional organizational team that has defined tasks, responsibilities, roles, and skills for supporting and promoting the effective use of Business Intelligence* across an organization

* I.e., Analytics, Big Data, Data Science, etc.


It’s About Culture Change First and Foremost

From Power to Empower

From Collection to Connection

From Control to Trust

New BICCs are about providing good governance and encouraging best practice rather than providing reports and analytics


It’s All About The Relationship!

It’s nearly impossible to spend too much time understanding the real business needs.

It’s not something that can only be done from head office.

Wrap-up


Where to Find More Information

My personal blog: timoelliott.com

SAP BICC Playlist on YouTube: Link

SAP BI Self Assessment : www.sap.com/bistrategy

SAP BI Strategy Playlist on YouTube: Link

BI News: www.sap.com/BINews

SAP Community Network: https://blogs.sap.com/?s=bi+strategy

http://timoelliott.com/

http://www.youtube.com/playlist?list=PLPA5ZAeDvb1cPajM-cR8ar6Q8R_ihsE3i

http://www.sap.com/bistrategy

http://www.youtube.com/playlist?list=PLPA5ZAeDvb1dUMYIUChURAYcIhZy9etgD

http://www.sap.com/BINews

https://blogs.sap.com/?s=bi+strategy


7 Key Points to Take Home

Business Intelligence and Analytics is more strategic than ever

Analytics now creates processes instead of just being generated by them

New trends in analytics means new approaches are required

Companies should invest in more self-service analytics for business users

Companies should invest in more flexible information architectures

Start preparing now for the artificial intelligence future

The number one priority is always the same: optimize your organization


Thank You!Timo ElliottVP, Global innovation Evangelist

[email protected] @timoelliott


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