Retail & CPG

1June 25 and 26

Hyatt Regency, Santa Clara, Calif.

New rules… ..Retailers’ New Games

2June 25 and 26


Consumerization of RetailTechnology Led Transformation

K. Ananth KrishnanVP & CTO, TCS

3June 25 and 26Hyatt Regency, Santa Clara, Calif.TCS Innovation

Forum

1800 Inversion of Retail


Forum

Customer Relationships are Multi – Dimensional and Non Linear

View Product banner

ad

Search product details online

Ask friends on Face Book for opinion

View product reviews by experts on You Tube

Read blog on product

Search for

product

SHOP ON WEBSITE / MOBILE / STORE

Track order

status on mobile

Like You on Face

Book

Download the mobile

appShare

posts on your Face Book page

Visit Store for product

demo

Compare prices with competitors

Write reviews about

product

Follow the

Twitter page

….56 touch points between moment of inspiration and moment of transaction


Forum

What we are hearing from our Customers…

Reimagining store

experience

Cross Channel

Customer Centricity

Efficiency

1. Its time to “re-imagine” the store experience and keep stores relevant to the customers. In-store technology will drive this transformation

2. Expanding Digital is everyone’s top agenda; enabling the entire organization to enable cross channel is top priority

3. Deeper understanding of customers, derive actionable insights and drive customer loyalty

4. Focus, across the board, on efficiency with following approaches -- a) Standardization & Simplification c) Supply Chain Re-design d) Business Process Optimization

5. Lastly, Retailers are in hurry because customers are in hurry

Time to market


Forum

Anatomy of Digital Transformation

User Experience

Multi-Option Fulfillment

Cross Channel Loyalty

Single View of Customer

Mobility

INNOVATIONIn Store way-

finding, Location Commerce

Multichannel Foundation

On Demand Service (search,

inventory)

DeepPersonalization

Time to Market

Components

Customer Experience Management as a capability to achieve seamless and intuitive shopping experience

Cross Channel Order Orchestration to facilitate profitable cross channel fulfillment supported by real-time Omni-channel analytics

Faster Time to Market through frequent updates/ release of functionalities enabling fresh digital experience

Foundation of Customer knowledge base to drive deep personalization and targeting


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Creating the customer “connections”

Creating a single view

Core Attributes

Extended Attributes

Transactions Interactions

Customer information

managementIn store behavior Social Media Data Online Behavior


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Long tail Content Monetization – Deep Personalization, acknowledge context

Nurture to loyalty - Transaction is not the only criteria for personalization. Track and Measure Micro Conversions

Leverage Enterprise-wide data to create 3600 customer profiles

Deploy Personalization as a differentiator – Moving away from black box techniques

Business goal driven – data driven “Plan” & “Execute”

Orchestrate personalization across touch-points

APPROACH

GOALS

Personalization for Customer Engagement


Forum

Customer engagement in the store

Self Scanning Identification of customer Self Help mobile App Customer shopping

enablement ( Aug Reality) Mobile Payment

Associate Empowerment

Associate

Customer

Center of cross channel orchestrations …Process Optimization

New Processes. Old Systems

Keeping Stores relevant…

Simplify…Standardize…Synergize Labor Productivity (receiving, cashiering,

RDQ….) Inventory Optimization Space Productivity Back office process optimization


Forum

Front end of tomorrow… empowered associates

Location based servicesMobile based customer engagementMobile Payment Self Check Out, Faster Check Out

Associate mobilityEmployee collaborationReal time predictive alerts

Front end of tomorrow

Empowered Associates

1

2

Store Technology for agility

3The New POSCloud(Near) Real Time integration

Cost/ Process Optimization

4Energy Usage Backroom EfficiencyMacro Space Optimization


Forum

…integrating customer into the merchandising process

..NOW ..NEXT

Lists, Basket, Trips

Consumer decision trees

Demand Substitution

Event, Sentiments

Weather Demand Based Forecasting

Sentiment based forecasting

Velocity based space and inventory

Product Elasticity

Demographic based assortment

Substitution, CDT based space and Inventory

Basket Elasticity

Trips based assortment

12June 25 and 26


New rules… ..Retailers’ New Games

Thank You

13June 25 and 26


Enterprise Mobility - Transformational Strategies

Naveen Krishna, VP-Online & Mobile Tech., Home Depot

HOME DEPOT MOBILITYJune 2013

16June 25 and 26


Big Data Revolution – Best Practices for Successful Adoption

Aashish Chandra, Divisional VP, Application Modernization, Sears

Legacy Rides the Elephant

Aashish Chandra

Divisional VP, Sears Holdings

GM / Head, Legacy Modernization, MetaScale

18

Legacy Rides The Elephant

Hadoop has changed the enterprise big data game.

Are you languishing in the past or adopting outdated trends?

19

The Classic Enterprise Challenge

The Challenge

Growing Data

Volumes

Shortened Processing Windows

Escalating Costs

Hitting Scalability Ceilings

Demanding Business

Rqts

ETL Complexity

Latency in Data

Tight IT Budgets

Constant pressure to lower costs, deliver faster, migrate to real time and answer more difficult questions for business..

• Copy & Use Source once and Re-use

• Linear Parallel Processing

• Proprietary Open Source

• Capital Cloud Expense

• Batch Real time

• Operating Costs Down

20

The Gartner Hype Curve

21

Gbytes Tbytes 100's of Tbytes

Minutes

Hours

Days

Data Size

SecondsMillisecondsCom

plex

Ana

lytic

al Q

uery

Hadoop

Database and high speed applianceswith parallel processing

Pbytes

ExpensiveInefficient

Cheaper price for performance

Why Hadoop?

22


Minutes

Hours

Days

Data Size


plex

Ana

lytic

al Q

uery

Hadoop


Pbytes



Why Hadoop?

23


Minutes

Hours

Days

Data Size


plex

Ana

lytic

al Q

uery

Hadoop


Pbytes

Gets expensive and inefficient as the data size grows. Will need investments in specialized appliances and will not scale beyond a point

Databases are best used for fast response needs on smaller datasets and for specific SQL

access



Why Hadoop?

24


Minutes

Hours

Days

Data Size


plex

Ana

lytic

al Q

uery

Hadoop


Pbytes



access



Why Hadoop?

25


Minutes

Hours

Days

Data Size


plex

Ana

lytic

al Q

uery

Hadoop


Pbytes



access

Hadoop is inefficient for small datasets but is designed to handle big and complex data: Stays efficient as you encounter Big Data and run complex workloads. Will

provide a lower price for performance



Why Hadoop?

26

What Hadoop is Not?

Understanding Hadoop’s limitations will help you identify the right use cases:

•Hadoop is not a high-speed SQL database•Hadoop is not a particularly simple technology•Hadoop is not easy to connect to legacy systems. You can do it, but the complexity needs to be considered.•Hadoop is not a replacement for traditional data warehouses. It is an adjunctive product to data warehouses.•Normal DBAs will need to learn new skills before they can adopt Hadoop tools.•The architecture around the data - the way you store data, the way you de-normalize data, the way you ingest data, the way you extract data - is different in Hadoop.•Linux and Java skills are critical for making a Hadoop environment a reality.

27

A super-powerful environment that can transform your understanding of data:

• Store vast amounts of data.

• Run queries on huge data sets.

• Transform traditional ETL

• Archive data on Hadoop and still analyze it

• Ingest data at incredible speeds and

analyze it and report on it in near real-time

• Hadoop massively reduces the latency of data

• Hadoop allows you to ask questions that were previously impossible to answer

Capabilities of Hadoop

Big DataThe Sears Holdings Journey

29

Where did we start?

• Issues with meeting production schedules

• Multiple copies of data, no single version of truth

• ETL complexity, cost of software and cost to manage

• Time taken to setup ETL data sources for projects

• Latency in data, up to weeks in some cases

• Enterprise Data Warehouses unable to handle load

• Mainframe workload over consuming capacity

• IT Budgets not growing BUT data volumes escalating

30

The Sears Holdings Approach

Implement a Hadoop-centric

reference architecture

Move enterprise

batch processing to

Hadoop

Make Hadoop the single point

of truth

Massively reduce ETL

by transforming

within Hadoop

Move results and

aggregates back to legacy

systems for consumption

Retain, within Hadoop,

source files at the finest granularity for re-use

1 2 3 4 5 6

Key to our Approach:1) allowing users to continue to use familiar consumption interfaces2) providing inherent HA3) enabling businesses to unlock previously unusable data

31

The JourneyIn 3 years, we are in a much different place..

•From Legacy (>1000 lines) to Ruby / MapReduce (400 lines)• COBOL is cryptic, difficult to support, difficult to train PiG is simple, short

and easy to maintain

•We tried HIVE (~400 lines, SQL-like abstraction)• Easy to Use, easy to experiment and test with• Poor performance, difficult to implement business logic

•We evolved to PiG with Java UDF Extensions• Compressed, very efficient, easy to code / read (~200 lines)• Demonstrated success in transforming mainframe developers to PiG

developers in under 2 weeks

•As we progressed, our business partners requested more and more data from the cluster –which required developer time

• We are now using Datameer as a business-user reporting and query front-end to the cluster

32

Re-Think..

• The way you capture data• The way you store data• The structure of your data• The way you analyze data• The costs of data storage• The size of your data• What you can analyze• The speed of analysis• The skills of your team

33

Mainframe Migration

Batch Processing - JOB FLOW

JCL1 - APPLICATION 1

Mainframe Batch Processing Flow

User Interface Data Sources Batch Processing

External Systems/

DatawarehouseInput

Resultant Data Resultant Data

SORT Input SPLITInput

SORT

Input COBOL

Input FILTER

Input FORMAT



LOAD TO DATABASE

COPY Input COBOL Input FORMAT

Input

Input

34

Mainframe Migration



Mainframe Batch Processing Flow

User Interface Data Sources Batch Processing

External Systems/

DatawarehouseInput

Resultant Data Resultant Data

SORT Input SPLITInput

SORT

Input COBOL

Input FILTER

Input FORMAT



LOAD TO DATABASE


Input

Input

Commodity Hardware Based Software Framework


Batch Process - APPLICATION 1

Batch Processing - JOB FLOW - Legacy Platform

Invention - Migration methodology for Legacy Applications to Commodity Hardware

User Interface Data SourcesExternal Systems/

Datawarehouse

Batch ProcessingInput Resultant Data

PIG/MR Input PIG/MRInput

PIG/MR

Input PIG/MR

Input PIG/MR

Input PIG/MR



LOAD TO DATABASE


Input

Input

Resultant Data

Seamless migration of high MIPS processing jobs with no application alteration

35

Mainframe Migration

Mysql

EnterpriseSystems

JQUERY/AJAXQuart

zJAXB

REST API

JDBC/IBATIS

JBOSSJ2EE/JBOSS/SPRING

Batch ProcessingHIVE

RUBY/MAPREDUCE

JBOSSHADOOP/PIG

DB2

EnterpriseSystems

JQUERY/AJAXQuart

zJAXB

REST APIJDBC/IBATIS

JBOSSJ2EE/WebSphere

Mainframe Batch Processing

VSAM

JBOSSCOBOL/JCL

MetaScale

36

Enterprise Data Hub & ETL Replacement

Experience evolved to move into ETL Replacement and architecting Enterprise Data Hub

• A major system effort in our Marketing department was heavily reliant on traditional ETL• As data volumes increased the system began to have performance issues as the ETL

platform degraded• Re-work CPU-intensive portions in Hadoop• Now run those workloads 20-50 times faster in Hadoop• Run-times do not grow as data volumes grow

• Enterprise Data Hub• Source Data once, Re-use multiple times• ETL gives way to ELTTTTTT

37

The Sears Holdings Architecture

38

Current Focus• ETL Complexity is no longer needed – Data Hub

• Source Once, Re-Use many times• ETL changes to ELTTTTTTTTT

• Data Latency is the thing of the past• Analysis is routinely possible within minutes of data creation

• Long Running Workload• Can be eliminated and executed at any time• Run times are a fraction of the original clock time

• Batch Processing on Mainframes or other conventional Batch• Run 10, 50, even 100 times Faster

• Intelligent Archive• Put your archives/ large data on Hadoop and make it intelligent• Archive with the ability to run analytics or join it with other data

• Modernize Legacy• Mainframe MIPS Reductions has very attractive ROI• Move Data Warehouse workload – Reduce Cost – Go Faster

39

Simple & Maintainable

Complexitycreates fog;

Simplicity clears it.

40

Faster & Resilient

Data Analytics driving the speed of business..

Secured and Resilient..

41

Summary of Benefits

• Readily available resources & commodity skills

• Access to latest technologies• IT Operational Efficiencies• Moved 7000 lines of COBOL

code to under 150 lines in PiG

• Ancient systems no longer bottleneck for business

• Faster time to Market • Mission critical “Item Master”

application in COBOL/JCL being converted by our tool in Java (JOBOL)

• Modernized COBOL, JCL, DB2, VSAM, IMS & so on

• Reduced batch processing in COBOL/JCL from over 6 hrs to less than 10 min in PiG Latin on Hadoop

• Simpler, and easily maintainable code

• Massively Parallel Processing

• Significant reduction in ISV costs & mainframe software licenses fees

• Open Source platform• Saved ~ $2MM annually within 13

weeks by MIPS Optimization efforts

• Reduced 1500+ MIPS by moving batch processing to Hadoop

Cost Savings

Transform

I.T.

Skills & Resources

Business Agility

42

The Learning

• Big Data is here and ready – Avoid the hype• An Enterprise Data Architecture model is essential• Hadoop can revolutionize Enterprise workload

• Can reduce strain on legacy platforms• Can reduce cost• Can bring new business opportunities

• The Solution must be an Eco-system• Must be part of an overall enterprise data strategy• Not to be underestimated

43

Fo

r m

ore

info

rmat

ion

, vis

it:

www.metascale.com

Follow us on Twitter @LegacyModernizationMadeEasy

Join us on LinkedIn: www.linkedin.com/company/metascale-llc

Legacy Modernization Made Easy!

This presentation, including any supporting materials, is owned by Gartner, Inc. and/or its affiliates and is for the sole use of the intended Gartner audience or other authorized recipients. This presentation may contain information that is confidential, proprietary or otherwise legally protected, and it may not be further copied, distributed or publicly displayed without the express written permission of Gartner, Inc. or its affiliates.© 2013 Gartner, Inc. and/or its affiliates. All rights reserved.

Jeff [email protected]

Twitter: @JeffPR

Instagram: @JeffPR

Retail 2013…and Beyond

Nexus of Forces: Social, Mobile, Cloud & Information

Nexus Impacts Differ Across Industries

ManufacturingGovernment

Professional Services

MediaRetail

ManufacturingCommunicationsBankingHealthcare

ManufacturingWholesale Distribution

"Process Value

Targets"

"Business Model

Redesign"

"Business asUsual"

"Technology Platform Refresh"

54

Major Market Opportunity for Software

45.3%

41.5%

38.7%

36.8%

32.1%

29.2%

23.6%

17.0%

12.3%

5.7%

2.8%

Retiring legacy systems

Developing applications to satisfyempowered consumers

Application integration

Managing big data

Optimizing stores as a major channel

Consumer smart devices in theenterprise

Upgrading store-level bandwidth andinfrastructure

PCI compliance

Mobile security

Fighting intrusions and Web attacks

Fighting against inflation

Over the next 3 years what “pains’ will you devote significant resources to solving?

55

Mobile POS Breaks into the Mainstream

50.0%

43.4%

42.5%

42.5%

41.5%

40.6%

40.6%

39.6%

39.6%

36.8%

Campaign analysis and forecasting

Forecasting and planning

Mobile POS

Predictive analytics

In-store pickup or returns of web goods

Multi-channel planning and forecasting

Campaign management

Allocation

Assortment planning

POS peripherals

Top 10 Technologies for 2013

56

IT Spend Continues to Climb

IT Budgets as a Percent of Total Revenue

Change in Year-Over-Year IT Budget

11.3%

30.2%

10.4%

6.6%

1.9%

7.5%

Less than 1%

1% to< 2%

2% to < 3%

3% to < 4%

4% to l< 5%

5% or more

4.7%

3.8%

4.7%

28.3%

26.4%

16.0%

16.0%

Decrease 10% ormore

Decrease between 5% to < 10%

Decrease between 1% to < 5%

No change

Increase between 1% to < 5%

Increase between 5% to < 10%

Increase 10% ormore

57

Has SaaS Finally Hit the Mainstream

56.6%

50.9%

38.7%

31.1%

35.8%

We seek best of breed software

We seek integrated solutions suites

We seek software-as-service models

We use in-house IT resources todevelop software

We use third party services to helpdevelop software

Your general point of view in how you want to acquire software going forward.

58

Status of Organization's Customer Facing Current Mobile Channel Development?

17.9%

44.3%

28.3%

9.4%

Not planning any activity

Planning under way

Pilots in progress

Fully functioning mobile commercestrategy in place

59

An Industry in Full Transformation Mode

13.2%

32.1%

35.8%

18.9%

Basic IT infrastructure and systems withcritical limitations

Mostly basic IT infrastructure and systemsbut some advanced upgrades

Mostly advanced IT infrastructure andsystems but lack of comprehensive

integration

Advanced IT infrastructure and systemswith deep integration

14.2%

7.5%

28.3%

15.1%

7.5%

27.4%

We don't have an e-commerce platform

Platform needs up dating, but no plan toupgrade

Currently upgrading platform now

Plan to upgrade within 12 months

Plan to upgrade within 24 months

Re-platformed within 2 years, no need toupgrade

Maturity of IT Architecture

Status of E-Commerce Platform

60

Has POS Hardware Commoditized?

39%

39%

6%

47%

8%

15%

4%

19%

17%

18%

14%

15%

6%

24%

6%

10%

20%

18%

28%

15%

14%

18%

5%

17%

11%

14%

18%

8%

5%

13%

14%

19%

POS peripherals

POS software

Mobile POS

POS terminals (traditional, fixed)

Self checkout terminals

In-store pickup or returns of web goods

Item level RFID

Returns management

Up-to-date tech in place Started but not finished major tech upgrade

Will start major tech upgrade in next 12 months Will start major tech upgrade in next 12-24 months

Status of In Store Technology

61

Mobile POS Looks to be Additive

Yes, already investing29.2%

Yes, planning to

invest during 2013

22.6%

No35.8%

Don't know12.3% Yes, plan to

decrease fixed POS

25.5%

No plans to decrease fixed POS

63.6%

Don't know10.9%

Does your organization plan to invest in mobile point of sale (POS) in 2013

Does your organization plan to decrease the number of fixed POS devices in stores during 2013 as a result of its mobile POS investments?

62

Digital Signage on the Rise

29%

25%

19%

19%

17%

10%

5%

3%

21%

14%

8%

16%

11%

13%

11%

6%

12%

8%

10%

11%

12%

9%

7%

3%

19%

16%

13%

19%

21%

14%

20%

14%

Frequent shopper or loyalty program

Store level Loss prevention

Kiosks

Shopper tracking capability

Store level task management

Digital signage displays

NFC (Near FieldCommunication) payments

Electronic shelf labels



Status of In Store Technology

63

Multichannel Key Even in Supply Chain

32%

24%

24%

22%

20%

16%

8%

2%

16%

13%

14%

13%

14%

16%

12%

7%

11%

8%

8%

10%

19%

18%

17%

8%

8%

18%

13%

11%

17%

22%

10%

9%

Warehouse managementsystems

Distributed order managementsystems

Transportation managementsystems

Sourcing

Real time inventory visibility(SCIV)

Multichannel fulfillment

Trade promotion management

Radio frequency identification(RFID) Case/Pallet



Status of Supply Chain Technology

64

Optimization Finds its Ways to retail

37%

25%

22%

14%

13%

14%

15%

18%

14%

11%

16%

11%

Time and attendance

Labor scheduling and optimization

Task management



27%

25%

23%

20%

5%

10%

13%

11%

13%

13%

21%

15%

18%

13%

15%

11%

14%

12%

12%

24%

Human Resources and benefits

Education and training

Recruitment and on boarding

Recruiting via social media (e.g.Facebook, LinkedIn)

Mobile-enabled workforce and/orHR applications



Status of Workforce Mgmt. Technology

Status of Human Resources Technology

65

Merchandise Technology

33%

25%

24%

24%

21%

21%

19%

19%

17%

15%

12%

9%

8%

21%

18%

25%

13%

21%

17%

23%

15%

14%

25%

19%

21%

16%

11%

17%

19%

14%

19%

15%

17%

16%

15%

25%

14%

20%

25%

10%

8%

13%

8%

7%

11%

12%

15%

13%

16%

8%

12%

22%

Replenishment

Item management

Forecasting and planning

New product or private label development

Allocation

Category management

Assortment planning

Price and markdown optimization

Product lifecycle management

Campaign analysis and forecasting

Shelf and space planning

Campaign management

Multi-channel planning and forecasting



Status of Merchandise Technology

66

BI/Analytics Continues to Show Strength

25%

21%

19%

10%

9%

21%

22%

16%

18%

18%

16%

15%

16%

25%

17%

13%

20%

17%

21%

26%

Market basketanalysis

Shopper Tracking

Margin optimization

Predictive analytics

Social mediaanalytics



Status of BI/Analytics solutions

Recommendations

Be assured the rate of innovation in retail will accelerate dramatically over the next 3 years- Meaning your competitors are transforming their

businesses….what about you?

Understand your organizations (really senior Mgt.s) readiness to absorb new technologies.

You are the change agent. Embrace the job!

Thank [email protected]

68

*

69June 25 and 26


Retail & CPGClosing Remarks

Thank You

Retail & CPG

Technology

Transcript of Retail & CPG