Analytics Innovation, Disruption, and Transformation

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Analytics Innovation, Disruption,And TransformationSID 5978Timo Elliott, SAP

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Timo Elliott, SAP, October, 2016

Analytics Innovation, DisruptionAnd Transformation


Agenda

Top Trends

Supporting “Modern BI”

Big Data Architectures

Organizing for Data

Conclusion

Top Trends


Technology Priorities for 2016 and beyond

Rank Technology Trend

1 BI/Analytics2 Cloud3 Mobile4 Digitalization / Digital Marketing5 Infrastructure & Data Center6 ERP7 Security8 Industry-Specific Applications9 Customer Relationships

10 Networking, Voice, and Data Comms

Nine out ofeleven years2006-2016

ANALYTICS

#1


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

”

“

@timoelliott

Live Business


BI Success …

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

Techtarget, 2015

Source: TechTaget

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




BI is Dead!?…


Are you a BI-nosaur?

© 2014 SAP AG. All rights reserved. 12

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 outside

Source: Forrester

http://www.sap.com/bin/sapcom/en_us/downloadasset.2014-05-may-07-17.business-driven-agile-enterprise-business-intelligence-pdf.bypassReg.html


The Penetration of BI Remains Low

“Close to 40% of organizations report fewer than 10% of employees using BI”

Techtarget, 2015



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

OptionalData warehouse

Semantic layers

OLAP Cubes


“Systems of Insight”

“Earlier-Generation BI Is No Longer Enough”

• Earlier-generation BI can’t keep up in the age of the customer.

• Agile BI and big data are the building blocks of systems of insight

Source: Forrester Research, Inc.


Pret A Manger



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?

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 Cannibalisation

Footfall

Weather

Events

Real Estate

Choosing a New Store Location

• $4.48 billion revenue• 40K employees• > 8M patients/year


Mercy Health

Mercy — one of US Most Wired for 12th Year in a row!

“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


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


Big Data Architectures = Digital Business

By 2018, 40% of enterprise architecture teams teams will be distinguished as leaders by their primary focus on applying disruptive technologies to drive business innovation.

By 2018, 40% of enterprise architecture teams will be responsible for advancing the organization's digital business strategy.

By 2018, the new economics of connections will drive organizations to increase investments in connected physical assets and systems by 30%.

By 2018, 20% of enterprise architects will use business ecosystem modeling to identify and predict business moments.

By 2017, 20% of EA will be responsible for identifying new business designs that leverage business algorithms.

Source: Gartner, Predicts 2016: Five Key Trends Driving Enterprise Architecture Into the Future


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!”


What is Hadoop?


“Classic” 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



Coca-Cola East Japan Architecture


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


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 – 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)


Centrica (British Gas)


Zurich Insurance


Some Projects

Atlas – open data governance

Ranger – policies and security

Nifi – data in motion

Flink – streaming data analytics

Zeppelin – analytic “notebooks”

Juypter, Kafka, Flume, Sqoop, etc etc


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 which 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 Big Data Platform – “Hadoop Inside”Vision

HANA native BigData Dynamic Tiering Smart Data Streaming NoSQL | Graph | Geo |

TimeSeries

HANA & Hadoop SDA Hive | Spark MapReduce | HDFS Admin & Monitoring User Mgmt / Security

Hadoop Extension Vora Engine Integrated with HANA and

Hadoop

HANA Data Management Platform

Instant Results

SAP HANAIn-Memory

0.0sec ∞Infinite Storage Raw Data

HADOOPVora

Information Management | Text | Search | Graph | Geospatial | Predictive

Smart Data Streaming

Administration | Monitoring | Operations | User Management | Security


Key Features -- Vora SQL Engine

#FEA433

Components

Written FromScratch

Multi Platform

Compressed Columns

Parallel QueryProcessing

In Memory Storage Fast Column Scans

Cache EfficientAlgorithms

Code Generation


SAP HANA Vora Modeler


SQL/OLAP on Big Data

• Hierarchical data storage of contextual data supports structured analysis

• Fast drill-down interaction aids in root-cause analysis

• Familiar OLAP tool enables experienced business analysts derive useful insights from contextual data

• Support for HDFS, Parquet and ORC formats

• LLVM/Clang – JIT compilation of query plans and execution

Hadoop/NoSQL DATA


SQL-on-Hadoop using Vora

A different context allows access to SAP HANA data from Spark SQL

Creates an in-memory data object, similar to a Spark dataframe

Load data from HDFS, temp table will be distributed across Hadoop cluster


SAP Predictive Analytics 3.0

Native Spark Modeling

Standalone or included in SAP HANA

Predictive Factory

Integration with cloud & other apps


DW Directions

SAP HANA DW SAP HANA DWSAP HANA DWOptional Components

DW Foundation

PowerDesigner

HANA EIM

Business Warehouse

SAP HANA Platform

Planning and Definition2015

Market presence in Data Warehousing with a clear roadmap

Strong and simplified offering with tight integration

Convergence into one technology stack addressing BW and SQL-based

DW needs

DWH Foundation

PowerDesigner

HANA EIM

Business Warehouse

SAP HANA Platform

DW Modeling DW ETL & DM

SAP HANA Platform

Analytics , BI Suite, Predictive Analytics , BI Suite, Predictive Analytics , BI Suite, Predictive

HadoopSAP HANA Vora

HadoopSAP HANA Vora

HadoopSAP HANA Vora

This is the current state of planning and may be changed by SAP at any time.

Execution and Delivery2016-2018 Vision


SAP HANA DW – Future-proof data management platform



Looking Forward to the Future: “Data Refineries”

Nobody believes that a single big data warehouse is THE solution any more• But they’re not going away any time soon • “Data warehouses are dead! Long live data warehousing!”

Instead:

Enterprise Information Catalog – transparency• Search for data: origin, owner, trust level, sensitivity, formats, how to order…

Data Factories – workflows, not just data• The collective know-how on getting, refining, displaying data

More info from Mike Ferguson, here:http://www.slideshare.net/HadoopSummit/organising-the-data-lake-information-management-in-a-big-data-world

http://www.slideshare.net/HadoopSummit/organising-the-data-lake-information-management-in-a-big-data-world







The Big Big Picture

Embrace Hadoop as if it were SAP technology

HANA Hadoop

What SAP does best: business process (live!)

Vora

“infrastructure”

Organizing For Data


“When Gartner started to cover BICC as a trend over 10 years ago, it turned out to be one of the biggest success factors for BI programs …

But: “All good things must come to an end.”

The BICC is Also Dead


So What Replaces BICCs? (According to Gartner)

Long live the ACE:“Analytic Community

of Excellence”!

© 2016 SAP SE or an SAP affiliate company. All rights reserved. 65InternalWho Drives Business Intelligence?

Executives Finance

Operations ITSales

Strategic Planning

Mktg

BICC


Embrace Shadow IT

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

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


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!

Conclusion


Suite

Applications

S/4HANA

DigitalBoardroomIcon

Analytics

C4A

BOBJ

ExtensionsApplicationsIoT

HANA Cloud Platform

(Micro-) Services

IoTPlatform

Identity Management

Business Network

CEC

Platform

HANAEnterprise

Computing Platform

any DB Hadoop

VoraDistributed Computing

Platform

SAP Platform for Digital Transformation

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Thank You!Timo ElliottVP, Global innovation Evangelist

[email protected] @timoelliott