data warehousing & business intelligence at bmw financial services where we are & how we got...

Data Warehousing & Business Intelligence at BMW Financial Services

Where We Are & How We Got Here

October 22, 2008 pg 2

Presentation Goals Share the what’s, how’s, and why’s of DW

and BI at BMWFS Give others an opportunity to learn from our

experience Hear your ideas and opinions Share, share, share


Topics Company Overview Business Drivers Delivery Process Architecture, Infrastructure Data Modeling Challenges Tools Data Governance & Data Quality Organization Strategy


- BMW FS Overview -


Company Overview About BMW Group Financial Services

Established in the U.S. in ‘93 to support sales and marketing efforts of BMW of North America.

Offer wide range of leasing, retail and commercial financing and banking products tailored to meet the needs of BMW customers

Offer financing to BMW dealers to expand dealership capabilities and enhance operations.

Expanded into others markets and countries. Continue to evolve beyond a captive finance unit. For example we offer finance products for non-BMW customers and P2P.

3 locations (OH, NJ, UT) 1,000 headcount (associates + contract/temp) 700,000 active accounts. Halloween is a very big deal

DW/BI program started in 2003


Our BusinessProvide attractive financing products to dealers and

customers: Help the Sales company move cars off the lots Generate profit and revenue for Financial Services

Key Measures Customer sat Residuals Bookings Profitability Delinquency Penetration


What We Have in Common Many disparate data sources Rapidly changing business needs Impact from current economic conditions IT isn’t nimble enough (business perception) Some shadow IT in the business


What Makes BMW FS Unique Deeply entrenched static reporting paradigm Business on it’s own when it came to reporting Data wasn’t being leveraged to its fullest, but business results

were still healthy and strong Strict technology blueprint (Microsoft, Intel) Tactical funding model. We are not a “build it and they will

come” organization. Rigorous release process Majority of IT resources are contract / consultant. Transitioning from a nimble medium-sized company to a less

flexible, large corporation.


- Business Drivers -


Problems We Set Out To Solve (’03) Give our business access to the data it needs for analysis &

non-operational reporting. Data must be reliable, integrated, historical, 1-day latent. Deliver & support appropriate BI tools. Collect, maintain, and deliver critical business metadata. Approval to help the business when they have questions. Demonstrate how the right data, delivered at the right time, to

the right people, having the right data analysis skills, can significantly move the needle on business results.

Business is used to approaching IT with a solution in mind. Create a culture where the “what’s” come before the “how’s”.

Deliver value every step of the way.


What We’ve Achieved (’08) Business Processes We Support

Financial Reporting Account Profitability

Operations Collections Lease End

Sales & Marketing Vehicle Logistics Dealer Bonus Program Front Office (used by Sales force

and Dealer channel) Customer Retention / Loyalty

Risk Credit Risk Residual Risk

200+ total users

Services We Provide Answer questions and resolve

issues with supported query, analysis, & reporting tools

help the business find data in EDW and Bengal (operational reporting database)

Recommend best tool(s) for analysis & reporting needs

Optimization & tuning Share tool and data analysis

tips, techniques, best practices


- Delivery -


Delivery ProcessProjects

Initial request w/high level

scope

Initial IT estimate

Business go / no-go decision

Final IT estimate

Business go / no-go decision

Maintenance

IT reserves capacity, project scheduled for

future release

Ticket w/ high level

scope

IT Estimate Business owners prioritize tickets for

next release

IT reserves capacity, tickets scheduled for a

future release

• Heavy emphasis on delivering projects on time and on schedule• IT capacity and budgets very transparent• Lot of process around managing capacity (supply) and business requests (demand)• If demand > supply, business may need to prioritize• IT capacity takes into consideration production support + admin / overhead• DW/BI team delivers ~30 projects and ~100 maintenance requests / year.


SDLC Have one, but it only addressed

transaction processing solutions.

DW/BI team defined process & collateral for data solutions. Matured over time. Integrated with core SDLC in ’07. Analysis & Reporting

Requirements ETL, BI, Data Model Design Estimating Model Business Rule Validation, Source

Data Quality Verification


Releases• All platforms follow a common

release schedule. Very efficient.

• DW/BI platform tied to these dates for the simple reason that we have to react to changes in our sources.

• However it’s not easy to knit iterative BI development into this schedule.Key users may not be available at the right times

Harder to deploy changes off cycle

Harder to manage DW/BI capacity & budget


Testing1. Unit

Responsibility of each developer Verify individual code components using low volume, sample data

2A. System Full volume production data Test cases with expected results. Verified by Build Team and BA’s. Also used for performance testing

2B. Regression Full volume production data loaded separately through old and new ETL process SQL used to concatenate columns & mechanically compare row images Focus on a few high risk DW fact tables each release High effort to build regression scripts but very re-usable & efficience

3. UAT Full volume production data Test cases with expected results. Verified by the business. Goal is 0 defects, we often come close

DW/BI platform goes beyond the IS standard for testing. Higher up-front effort but worth the effort.


Business Requirements Super critical...but it’s hard to find best

practices. Feels like an area of opportunity for the data management profession.

Detailed requirements don’t guarantee a successful project. But we can’t be successful without it.

Review of our approach:

BI Requirements Template


Lessons Learned Chunk and iterate projects whenever possible. Note:

We’re still trying to figure out the best way to marry iterative development to a fixed release schedule.

Good requirements have value. They can (and should) evolve during the delivery process, but a baseline is important.

Start with “what’s” before getting into the “how’s”. In other words define the business questions & problems before defining the solution.

Quality is key to keeping the end user’s confidence.


- Architecture, Infrastructure -


Where We Started (2003) Organization was luke-warm to a grand DW/BI

implementation. “Why can’t you put all the data in one big table?” Big bang approach did not fit tactical funding model

or culture. Majority of business was getting data from an un-

architected near real-time reporting database. DW/BI Team made a conscious decision:

Start small, deliver business value quickly and frequently Grow organically, but make sure every step is on the path

to an enterprise solution.


Evolution2003: Daily reporting database to support Dealer Bonus Program.2004: Separate monthly analysis & reporting database for Risk.2005: Databases consolidated into “EDW” with first architected relational

marts for Front Office Reporting.2006: Higher value solutions ex. Customer Retention & Loyalty.

Additional relational marts deployed.2007: First enterprise launch of true BI for Front Office Analytics.

Additional relational marts, first cubes and semantic layers deployed.2008: More demand for BI. Also significant focus on hardware upgrade.2009: Commitment for mission critical BI initiatives (ex. Collections /

Delinquency Analysis, Pricing Analytics).

Incr

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Design Basics Data Warehouse

Not star schema, closer to 3NF Snapshot history Updated nightly

Data Marts Relational marts are star schema, deviating if/when it

makes sense Semantic layers & cubes are also marts

Nightly batch window a major challenge No architected ODS…yet.


CAN New Biz

IBVLE

ad hoc querying, analysis, standard reportingcontrolled access

for Infobahn, DRM, Ghostfill

CAM

D DDD DM

Extract/Transform/Load

Staging

EDB(leases, loans)

Canada(vehicle sales)

VCS(extended service)

AuctionNet(luxury

auction data)

Bank(CC apps & contracts)

NA (vehicle sales)

APPRO (credit apps)

Safeguard (gap

coverage)

Excel(custom rollups & groupings)

FIN

D

Enterprise Data Warehouse

Siebel (DRM data)

VPSO

M

Credit Risk

M


FOA

D

FOA cube

LEARiskFCST

M D

Davox (Dialer data)

HSOB

HIST cube

D

So

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es

Wa

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aye

rM

art

La

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Acc

ess

La

yer

SL SLSL

Current DW Architecture


IB

ad hoc querying, analysis, standard reporting

controlled access for Infobahn,

DRM, Ghostfill

D


Staging

EDB(leases, loans)

Canada(vehicle sales)

VCS(extended service)

AuctionNet(luxury

auction data)

Bank(CC apps & contracts)

NA (vehicle sales)

APPRO (credit apps)

Safeguard (gap

coverage)

Excel(custom rollups & groupings)

Enterprise Data Warehouse

Siebel (DRM data)


CSOBFCST

M D

Davox (Dialer data)

So

urc

es

Wa

reh

ou

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aye

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La

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Acc

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La

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cubes

SL’s

D

HSOB

cubes

SL’s

Target DW Architecture

Consolidate, consolidate!• Easier to answer more complex,

higher value business questions• Less dependency on IT• Less data redundancy, more

efficient• Low cost to get here


Lessons Learned Not enough emphasis on mart usability.

Inconsistent design approaches (normalized, denormalized, star schemas, etc.)

Some structures hard to query Having a good foundation (EDW) makes it

easy to evolve & adapt the marts.


- Data & Data Modeling Challenges -


“The Dead Zone” Had our fair share of unpleasant data surprises. Original requirement from the business:

Need “Total FS Accounts as of PM, MTD, YTD” Need “Total NA Sales as of PM, MTD, YTD”

During testing we discovered: FS and NA have different fiscal calendars for internal

reporting & tracking. Uncovered another 5 distinct fiscal calendars Start/end dates for some fiscal periods change over time. Some measures combine metrics associated with different

fiscal periods.


Headache Time Had to:

Figure out which measures are associated with each fiscal calendar

Design a process that tracked start/end dates for each distinct fiscal period

Allowed updates to the calendar Some measures combine metrics associated with different

fiscal periods. Formula: FS Penetration = FS Contracts / NA Sales FS Contracts are measures through the last calendar day of the month NA Sales are measured into the first week of the following month So what is FS Penetration on November 2nd?


1st Half of Solution – Calendar Table Simple table to track

cutoff dates for the various fiscal periods.


2nd Half of Solution – Elegant ETL Logic to stop accumulating measures used in multiple

fiscal periods, until the end of the last period.

Calendar

date

FS Fiscal period

FS contracts

October

NA Fiscal Period

NA Sales

October

Prior Month

Penetration10/31/08 October 10 October 100 [Sept’s number]

11/1/08 Nov 10 October 110 [Sept’s number]

11/2/08 Nov 10 October 120 [Sept’s number]

11/3/08 Nov 10 November 120 10/120 = 8%

FS month end is 10/31NA month end is 11/3

frozen thru 11/3


Lessons Learned Not easy to find all the landmines via a

typical source data assessment. More detailed requirements and business rule

modeling may have caught it. Business SME was already part of the team!

At the end of the day we’re dependent on analysts asking the right questions of the business, and the business offering the right information at the right time.


- Tools -


BI Toolset At one time Crystal Reports was the only supported tool, hence it became

entrenched. It was the solution to every problem. When it was time to add BI capabilities, we evaluated several

products/platforms. Didn’t make sense to spend months & months on a “super” evaluation.

Vendors & technology changing too rapidly. Goal was to make an informed selection and get started, not find the

“perfect” BI platform. B.O. was a logical choice

Synergy with our Crystal platform Strengths aligned with current & near future needs Web Intelligence and Voyager have been deployed Dashboard pilot to “get smart” Universes still can’t span databases….ugh.


OperationalReporting

DataMining

PredictiveAnalytics

StrategicReporting

Forecasting

Lev

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f C

om

ple

xity

Functional Needs

Com

ple

xA

vera

ge

Sim

ple

Scorecards &Dashboards

Ad HocQuerying

DimensionalAnalysis

Anticipating Demand

Where we started(pre-

DW/BI)

added viacurrentDW/BI program

Expected in the next 6-18

monthsneed to “get smart here”

Silo solutions in the biz

(unsupported)


Other Tools / Technology Metadata

Metacenter by Data Advantage Group Currently not integrated with ETL or BI toolsets. Using it to deliver highest value business metadata.

ETL Informatica. Upgraded from v7 to v8 in Q2.

Database SQL Server 2005

Hardware Quad-core servers running Windows 2003 EE


Lessons Learned Generally happy with our technology choices but there is constant

pressure to drive down cost. For products licensed by CPU, there may be different interpretations of

how terms translate them to multi-core processors. Make it clear during your negotiations.

Bundled products (ex. SSIS) and open source offerings are maturing. For cost reasons we’re keeping an eye on them.

A “light” metadata implementation can be a good place to start. We have a large, unmet need for access to operational data for near real-

time analysis. Business doesn’t see value in an ODS. Looking at logical data integration tools in the Sypherlink / Altosoft category.

Don’t try to shut down silo solutions or rogue tools i.e. no empire building. They exist because they meet a need. Focus on delivering value, marketing accomplishments, and being a trusted partner. As others see value there will be less resistance.


- Data Governance & Quality -


Governance Not mature in this area. No formal process, but still

effective. We know who the subject matter experts are Rely on this group to define the “single version of the

truth” (business rules, definitions, etc.) No challenge we haven’t been able to resolve, easily

Gap: coordinating OLTP changes before they impact downstream systems, including EDW. No automated way to do this today. Relies on people & process. Misses occur.


Data Quality Also not mature in this area but, again, still

effective. Most systems are new and internally developed.

Data quality is generally good. EDW has some rudimentary data quality checks.

We know more is needed. Philosophy is not to cleanse data on the way into

the EDW. If data is bad, fix the source.


Lessons Learned Governance is important, but we see it being

critical when more areas of the enterprise are sharing data


- Organization -


DW/BI Team Staffing Team Structure

Team Lead (1) Architect (1) ETL Developers (4) BI Developers (1) Business Analysts (2)

Database Architect (1) Database Developer (1) Infrastructure (< 0.5) End User Access

Services (0.5)

Technical skills are important, but the key to a successful team is finding people that know when and how to collaborate.


A Day In The Life Building, testing, implementing new data and functionality

(project & tickets) Defining, designing, estimating new requests Production support / break fix End User services and support Strategic activities (reference architecture, technology

evaluations, etc.). Not enough time for this! Project / maintenance split is about 50 / 50. Typical

distribution of maintenance activities:

Admin/Other14%

Enhancements28%

Prod Support27%

End User Services25%

Release8%


- Strategy -


Where To? Confident we have a solid DW/BI foundation. Gap: some parts of the business aren’t

leveraging it, or don’t see the benefit of going beyond basic reporting. Opportunity here.

It is time to help the organization mature into a data driven enterprise. Mostly organizational and political, not much technical.


Changing Hearts & MindsLevel 1

Analytically Impaired

Organization has some interest in analytics

Top Mngmt Support

Level 2Localized Analytics

No

Yes

Functional areas tacklelocal needs

Top Mngmt Support

No

Yes

Terminal stage. Analytics not part of culture

Level 3Analytical Aspirations

Executives committed. Resources & timetable for building broad capabilities.

Level 4Analytical Company

Enterprise-wide analytic capability in development. Corporate priority for execs.

Level 5Analytical Competitor

Organization routinely reaps benefits of enterprise-wide

capability. Ongoing support & renewal.

CurrentState

To-BeState

from “Competing on Analytics: The New Science of Winning” by Thomas Davenport


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SDLC Exists but Does not take BI Into Account

• Proactive, Repeatable, & Reusable Process for End User Services and Training

• Shared Accountability (via SLA’s) with Vendors

• Enterprise Level Strategy (IS & Business) for Backup/Disaster Plans Implemented

• Communication extends to External FS Divisions/Partners

• Optimized Organization, Processes and Roles Measured and Refined

• Effective Metrics/Levers & Consistent Process Turn Value into Project Opportunities

• Method to Incorporate Emerging Technology

• Ongoing Measurement of SDLC Effectiveness

• SDLC Process, Tools and Methodologies Refined

Reference Architecture Does Not Exist

No Data Consistency Across Stores

Understand Major Levers & KPI’s at a Departmental Level

Defined Consistent Measures

No Dedicated Change Management Team

Resources Lack Analytical Skills

Partial Sponsorship

Limited Set of End User Services

Inconsistent Backup and Disaster Recovery Plans

Inconsistent Use of SLA’s

• SDLC Updated with BI Processes, Tools and Templates

• Best Practice Driven Model Defined

• Partial Implementation of SDLC in Projects

• Reference Architecture Established

• Best Practices to Model Data Defined

• Tools Selected and Reused

• Executive Support and Understanding of Levers & KPI’s

• Enterprise-Wide Sponsorship

• New Roles, Positions, Development Plans & Compensation Models Defined

• Communication Plan Developed

• IIG Factors Used to Forecast Projections and Ensure Data Quality

• Full Commitment to Service Offering and Training

• Consistent Use of Backup/Disaster Plans & SLA’s

• Full Implementation of SDLC in Projects

• Incorporate Appropriate Tools and Organization Support

• Reference Architecture Practiced and Enforced

• Data Consistent Across Stores

• Metrics/Levers in Place to Drive Consistent Enterprise Processes

• Change Management Team Staffed• Resources Deep in BI/Analytical Skills• Communication Plan Implemented

• Funding Allocated Based on Projections

• Strategy Developed for End User Services and Training

• Enterprise Level Strategy (IS Only) for Backup/Disaster Plans Implemented

Current State To-Be StateCapability By Maturity Level


Other Unmet Challenges Master Data Management

Dealer Number Customer Number

ODS or other near real-time solution


Wrap Up Questions? Thank you for listening.

A person who never made a mistake never tried anything new. - Albert Einstein

I'm sorry this presentation is so long, but I did not have time to make it shorter.- Mark Twain


About Eric Juttner Information Delivery Team Lead (DW/BI platform) in the IS Department at

BMW FS.

DW/BI consultant and project manager at IBM Global Services / Business Consulting Services from 1996 – 2003. Also a member of the team that defined the IGS worldwide DW/BI methodology and SDLC.

Started I/T career and was introduced to data management and data warehousing at Aetna Life in Casualty in the mid-1980's.

Experiences include leading many large data warehouse / data integration / business intelligence projects in Banking and Finance, K-12 Education, Retail, State & Local Government.

Originally from Connecticut, Eric moved to Ohio in 1996 and lives in Lewis Center with his wife and 3 children.

data warehousing & business intelligence at bmw financial services where we are & how we got...

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