self-service business intelligence program...

Self-Service Business Intelligence

Program Overview

Director, Enterprise BI

Cummins, Inc

May 2015

2

Build a sustainable future for all stakeholdersCummins - Profitable Growth

Strong Shareholder ReturnProfits Grow Faster Than

Revenues

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

$0

$5

$10

$15

$20

2007 2008 2009 2010 2011 2012 2013 2014R

evenue (

$ B

illio

n)

Revenue and EBIT

Revenue

EBIT %

1 EBIT excludes restructuring charges in 2009 and 2014 (in Power Generation), and the gains from the divestiture of two businesses and flood insurance recovery are excluded from 2011. Also, Q2‘12 EBIT excludes $6 million pre-tax

additional gain from the divestiture of two businesses in 2011, and Q4’12 EBIT excludes $52 million in restructuring charges.

$20 BSales in 2014

Record Level

Revenues

3

Power Generation Components DistributionEngine

Global Power LeaderCummins Complementary Businesses

Cummins Market Applications

4

Light-Duty

Automotive & RV

Mining, Marine, Oil & Gas,

GovernmentConstruction & Agriculture Stationary Power

RailMedium-Duty

Truck & Bus

Heavy-Duty

Truck

Global Power Leader

countries and territories employees worldwide

Develop, design and manufacture products on continents

Business Units

Regional Organizations

Corporate Functions

Engine Applications

Market Segments

Why Self-Service BI?

The Most Effective Way to Deploy Analytics to a Global Organization

Self Service BI at Cummins

Why Self-Service Business Intelligence?

Our current business intelligence and analytical platforms

are not delivering on our business needs

Current State Microsoft Self-Service BI Platform

Business requests for new data analysis

require analyst engagement and IT

development to source and deliver data

Existing data sources are directly accessible

by business users; minimal to no analyst

and IT work needed

Sourcing of new data sources into the data

warehouse requires modeling, coding, and

testing

New data sources can be rapidly sourced

into ad hoc data area for quick access;

formal modeling into warehouse if needed

Multiple end user tools for analytics requires

substantial licensing, maintenance, and

training

Common set of analytics tools focuses

investments in training and increases

reusability across Cummins

Limited capacity for analyzing of ad hoc and

what if scenarios for exploring business data

Full featured suite of data analysis tools that

build on and around a widely-understood

Excel platform

Self-Service BI: Guiding Principles

Focus on the end users, think like an analyst

Build and foster an analytical organization

Reduce IT complexity

Business owns the data

Work together to govern the data and process

Drive BI technology innovation

Self-Service BI Distinctions

Introduces a new analytical paradigm

– Rapid prototyping, agile deployment

Connect to any data source, connect any data source

Spreadsheets with unlimited columns, unlimited rows

Eliminates costly ETLs, replaced by Extract, Load, then Transform

– Faster development, easier to reconcile data, easier to adapt to changes

Leverage analysts and business community

– Reduce dependency on 3rd party developers

Grass-Roots deployment, word of mouth communication

What is Self-Service BI?

Cummins uses the Microsoft

Business Intelligence built in

Azure to provide:

– Business friendly reporting,

analytics, and modeling tools

– Web front end for scheduling

data refreshes and migrating

analytical models

– Flexible and scalable

Analytical platform

– Ability to connect to any data

set (Source Systems, Data

Marts, Data Warehouse,

Teradata, Hadoop, etc.)

Source Systems and Processes across CMI

Engine Test Bed &

Performance

Manufacturing Control Systems

Sales Order Management

ProcessingEtc.

Operational Reporting on MES

Operational Reporting on Sales

Orders

Data Transformation and Conformation to Enterprise Model

Data Quality Assurance and MonitoringMinimal Transformation

EnterpriseData

Warehouse

Self-ServiceAnalytics Data

Data Data Data

Customer Product

SalesEtc. EDW

Self-Service Reporting Tools

Excel Reporting & Analysis PowerViews & PowerPivots Visualization & Analysis Tools Reporting & Analysis Storage

Sou

rce

Tran

sfo

rmSt

ore

Co

nsu

me

Model

Microsoft Self-Service BI Stack

Why Self-Service BI Benefits the Business

Business accountability and responsibility of the

data they already own

SSBI improves time to market (introducing agile

processes)

– New engagements: empirically gives the business

their data up to 27 weeks faster

– Change requests: empirically realized by the

business up to 34 weeks faster

SSBI allows data cleansing without impacting

source data

Business

Effective data driven decisions

Faster time-to-market

Quality data on which to make

decisions

Decision

DecisionTime to Data Analysis

AnalysisTime to Data

Why Self-Service BI Benefits IT

Security and auditing is streamlined

– The infrastructure and environment isolation in place

– The business owns the responsibility for the user-level security and auditing of their data and access rules

IT has governance visibility into the environment

– Complete records of who is using the system, what and when they have accessed, etc.

– Complete visibility on the creation of data models and all connections to source systems

Gold standard Data Warehouse can be developed over time based on actual usage

IT can focus on the infrastructure and providing a service to the business

IT

Security

Governance

Planning for the future

HighlightedSuccess Stories

Parts Pricing and Analytics

Category: Large entity, high visibility

What: Expensive, inflexible 3rd party system delivered no result

How: Designed a Self-Service solution that the Parts business teams

(Pricing, Product Management, and Analytics) now support

Value: Enabled the Parts business to analytically price 80,000 parts and

begin building the history to achieve optimal pricing

User Experience Third-Party Proprietary Self-Service Analytics

Parts Pricing

Parts BI

$6.5M

$1.4M$0*

Parts Consulting Fees $2M $300,000

Implementation Time 3.5 Years 12 Weeks

Parts Priced 0 80,000

Year 1 Revenue $0 $33M

Corporate HR Analytics

Category: Large scope

What: Manual process with continual churn and no analytical capabilities

How: Designed a Self-Service solution that allowed for automated global business efficiencies

Value: “In my 4 years at Cummins, this is the first time that we have successfully moved forward from a system perspective on analytics. This is very exciting, the possibilities are huge!”

- Brian Hamilton, Director - HR Reporting & Analysis

User Experience Past Experience Self-Service Analytics

Efficiencies Gained Manual Reporting Workforce Analytics

BI Engagement No Capability/Support Capable in 4 Weeks

AOP Process Manual Automated

Decision Cycle Time No Ad-Hoc Responses 5 Minutes

Data

Quality/Governance

1 Day 5 Minutes

Distribution BU Global Inventory

Category: Do it yourself, never had BI AOP funding

What: Manual inventory and cleansing collection process against disparate systems. Error prone, time consuming, and demanded user compliance.

How: Designed a Self-Service solution that pulls and aggregates the data automatically from multiple ERP systems

Value: Automatic data aggregation allowed for the resolution of many customer down incidents. Additionally, it provides for a secure data environment that reduces user error and enhances work satisfaction (Cummins employees can now work strategically to benefit the business rather than spend their time with data entry).


Efficiencies Gained 46 Support Personnel 1 Person Part-Time

Process Compliance Chronic Weakness Automated

Data QualityManual Entry -

Worse than SourceSame as Source

Enterprise Remedy Analytics Category: Global IT Production Support group supporting multiple BI

reporting applications, reporting requests, and data warehousing services

What: Analytics capability on large volume of support requests (1k/mo)

How: Pull daily tickets direct from source into the MSBI environment for

visibility to SLA performance and all other KPIs

Value: Significantly improved ability to focus improvement work, reduce

support costs, improve customer satisfaction


Availability of data Limited by vendor Full access to all

relevant data

Reporting interval Monthly per vendor Daily refresh in MSBI

Ability to drill down None Full

Visualizations on data Minimal Unlimited

Engine Warranty Analysis Category: New capability, cross-BU/cross-functional unsatisfied need

What: Existing Big Data solution was limited and not delivering end-user

data or linking with other CMI data sets

How: Built a small Hadoop solution in Microsoft Azure within 3 weeks,

including reprocessing all engine INSITE logs for 3 years

Value: Extracted and delivered all engine information to Engineering,

Reliability, and Six Sigma teams to use in their investigations. Data used

in one Six Sigma project with projected $5.6M in savings.


Linkage to CMI Data Not Designed Fully Capable

Effort Realization Continual Churn 3 Weeks

New Capability Cost

Estimate>$270K $87K

Savings * >$5.6M

Engine Warranty Analysis (con’t) Phase I work completed in 3 weeks for under $60K

Over 2600 parameters per engine combined into a single analytical model to enable correlation of engine faults and failure codes to specific components

– Engine service logs pulled from EDW 93M and from CloudOne

– Customer information loaded from multiple sources (ERP’s, EDW, Customer Masters, and National Accounts)

– POLK / VIN data

– Reliability Warranty events

– Expert Diagnostic data

– Work order details/headers

– Warranty campaigns

– Vehicle registrations/OEM info

– Plasma/Genealogy

– Part sales/Product Coverage; some portions of the BOM/SBOM

Self-Service BI: Success Stories

Self Service BI

Deployment Status

Self-Service BI: Program Status

Self-Service BI: Training Status

$48,960

$88,128$119,808

$13,200

$84,150

$168,300

$0

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

$160,000

$180,000

Low Complexity Medium Complexity High Complexity

Engagement Cost

ADSC SSBI

21.25

38.25

52.00

2.00 12.75

25.50

0.00

10.00

20.00

30.00

40.00

50.00

60.00

Low Complexity Medium Complexity High Complexity

Engagement Time to Delivery (Weeks)

ADSC SSBI

2015 Self-Service Engagements

• Faster time to deliver

• Quicker to business

value

• Higher satisfaction

• What could 6 months

buy the business?

• Initial cost will be higher

only for complex

projects

• 100’s of small projects

vs. 15 high complexity

projects

II

2015 Self-Service Cost Avoidance

Self Service BI - Next Steps

Expand Global Training Program

Formal Communication?

Expand Azure – Europe, Singapore, others

Integration and alignment with traditional BI program

Expand advanced analytics, Machine Learning, IoT

– Tools and processes

Self Service BI – In Summary

Increased Revenue

Decreased IT Costs

Millions in IT Cost Avoidance

Increased User Satisfaction

Global Deployment

Business & IT Teams working together

Appendix

Microsoft Self-Service BI

Microsoft Self-Service BI Architecture

ACDC

PROD Delivery

CED

FTDC

PROD DR

CED

SQL for SP/MDS/SUPPORT

Primary Replica

8vCPU x 32GB

C:80GB, D:300GB, E:100GB

F:100GB, G:300GB


Secondary Replica

8vCPU x 32GB

C:80GB, D:300GB, E:100GB

F:100GB, G:300GB

SP WFE/APP

Active

8vCPU x 32GB

C:-80GB, D:100GB

SP WFE/APP

Active

8 vCPU x 32GB

C:-80GB, D:100GB

SQL for SUPPORT - DEV

Primary (no AG)

8vCPU x 32GB

C:80GB, D:300GB, E:100GB

F:100GB, G:300GB


Secondary Replica

8vCPU x 32GB

C:80GB, D:300GB, E:100GB

F:100GB, G:300GB

SP WFE/APP

Passive

8vCPU x 32GB

C:-80GB, D:100GB

SSAS Tabular

Primary/Query Replica

8vCPU x 64GB

C:80GB, D:100GB, E:50GB,

F:200GB

SSAS Tabular

Secondary/Processing Replica

8vCPU x 64GB

C:80GB, D:100GB, E:50GB,

F:200GB

SSAS Tabular

Tertiary Replica

8vCPU x 64GB

C:80GB, D:100GB, E:50GB, F:200GB

SSAS Tabular - DEV

Primary Replica

8vCPU x 64GB

C:80GB, D:100GB, E:50GB,

F:200GBACDC on-premise servers(see other illustration)

SQL 2012SSAS Tabular

Primary

Cummins CIDC

TFS 2013

Cummins FTDC

Azure-West(Production)

GTM


Secondary(Synchronous)

SQL Agent Sync

SQL 2012RDBS, MDS, DQS

Primary



SQL Always On Availability Group

SQL 2012Management

Primary

SQL 2012Management



SharePoint 2013Web

Active

SharePoint 2013Web

Active

SharePoint 2013App

SharePoint 2013App

SharePointSQL 2012Primary

SharePointSQL 2012Secondary

(Synchronous Commit)



DR

Azure-East(DR)


DR

SharePoint 2013WebDR

SharePoint 2013App

SharePointSQL 2012

DR

Azure 2015-03-04

AT&T NetBondMPLS ExpressRoute

AT&T NetBondMPLS ExpressRoute

Cummins NetworkMPLS


Pivot TablesPowerMapPowerView

PowerPivotModel

Enterprise Sync to SSAS Tabular

Microsoft Self-Service BI Architecture

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