Smart Data Webinar

Please tweet @Primatics during the

webinar using #SmartData.

Presenters:

John Lankenau- VP, Product Management,

Primatics Financial

Craig Lovell- VP, Software Development,

Primatics Financial

Banks Face Demanding Environment

Rapid changes

Tighter regulation

More complexity

Increased demands for information

2

More demands combined with today’s powerful technology means

trouble !

Many Loan Processes Are Conceptually Straightforward

• General reserve

Impairment

Stress testing

Non-accrual

3

Conceptually simple, but operationally hard. Why? !

Data!

• 27 of 28 surveyed last year said they were not satisfied with data and

data management

• 23 of 24 surveyed this year said they were not satisfied with data and

data management

• February 2015 RMA Journal survey

• Majority of U.S. institutions surveyed disagree with statement that risk data is

“clean, comprehensive, accessible and timely”

• Highest level of concern about “difficult integration among systems”

• High level of concern about “lack of integrated risk and finance reporting”

4

Despite heavy investment, data remain a challenge nearly everywhere !

Symptoms of Data Challenges that Need to be Addressed:

Inability to do desired analysis because data architecture does not support

it

Many sources for reports – inconsistent answers depending on whom you

ask

Emailing, phone calls or other manual movement of data from one function

or process to another

Multiple sources for the same data elements require significant

reconciliation efforts or cause inconsistencies

Calculations require data from unwieldy number of sources

Making data “application-ready” happens in many places

Etc…

5

Data challenges lead to inefficiency, lack of ability to do desired tasks,

lack of control !

Handy Guide to Troubled Data Projects

Never-ending

• Two years into a scheduled two-year project with little

benefit and 18 months remaining to finish

Non-adoption

• Project is done but no processes actually use it

Attic

• Data are all in one place but processes use different data

for the same thing

6

Having a successful data project requires addressing root cause of

challenges !

1

2

3

Data Challenge is Foundational with Multi-Faceted Issues

Data challenge starts at the front end of all processes

(sourcing)

• Reconciliation, timing differences, “bad” data, etc…

• Identification and segregation important parts of process

Data and applications must be synchronized

• “Application-ready” data must be accessible to applications

• All applications must link to reporting data mart in native way

Back end reporting also a data challenge

• Aggregation

• Multiple views and ad hoc analysis need to be supported

7

Addressing the data challenge successfully requires addressing

sourcing, applications, and back-end reporting holistically !

Sourcing

Reporting

Applications

Elements of a Comprehensive Data Architecture

4/13/2015 8

Integration of multiple upstream data

sources

Integration of human data

Data validation, normalization, enrichment,

and population identification

Integration with Calculation Engines

Warehouse Design and Population

Reports, Analytics, and Extracts

2

1

3

4

5

6

Pitfall #1: Conflating Operational Data with Reporting

Warehouses

4/13/2015 9

Upstream Data Sources

Reports and

Extracts

The Mother

of All

Warehouse

Concepts

• Operational Data Store (ODS) design is

completely different than a Reporting Warehouse

design

• An ODS will have multiple data sources and

multiple applications integrated

• A modern reporting warehouse is designed

and optimized for read-only reports

• The warehouse should not have to share

resources with an active ODS

• For the best dining experience, cook your data in

the kitchen (Operational Data Store) and eat it in

the dining room (Reporting Warehouse)

Validation,

Normalization,

Transformation,

etc.

Pitfall #2: Failing to Normalize

4/13/2015 10

Upstream Data Sources

• Bringing upstream data into an ODS

without normalizing means the

applications will never see a consistent

view of the data across sources

• Applications and calculation engines

should not have to deal with multiple data

formats

• Reporting across data sources becomes

extremely difficult if not impossible

• Your ODS should not be a dumping

ground for disparate formats

• A loan is a loan is a loan – represent it

that way no matter where it’s from

Operational

Data Store

(ODS)

Without

Normalization

Applications /

Calculation

Engines

?

?

?

Ugh!

Pitfall #3: Don’t Forget the Humans!

4/13/2015 11

Upstream Data Sources

• Your data architecture must integrate both

upstream systems and data from human

processes

• Spreadsheets abound – bring them into

the system

• Humans should be able to reconcile,

correct, and augment data in a controlled

and auditable fashion

• Humans need to be able to stop the train

and re-load data if they spot problems

Operational

Data Store

(ODS)

But wait, I didn’t give

you my charge-offs!

Applications /

Calculation

Engines

Pitfall #4: If You Build It, They Will Come

4/13/2015 12

• Very nice architecture for

consolidating and reporting, but no

applications integrated!

• Applications left to fend for

themselves

• Input to each application needs

to be copied from somewhere

else and transformed

• Reconciliation nightmare

• Applications have to provide

their own reporting of outputs

• Can happen when IT throws a data

warehousing party and doesn’t invite

the business

Operational

Data Store

(ODS)

Data Warehouse Reports and

Extracts

Upstream Data Sources

Applications /

Calculation

Engines

Something’s

missing

here…

Putting it All Together: Smart Data Architecture

4/13/2015 13

Integration of multiple upstream data

sources

Integration of human data

Data validation, normalization, enrichment,

and population identification

Integration with Calculation Engines

Warehouse Design and Population

Reports, Analytics, and Extracts

Operational

Data Store

(ODS)

Data Warehouse Reports and

Extracts

2

Upstream Data Sources

1

1

3

2

3

4

4 5

5

6 6

Business

Rule

Engine

Applications /

Calculation

Engines

Data User

Interface

4/13/2015 14

15

Lo

an

Data

E

nd

User

Ris

k a

nd

Fin

an

ce

Fu

ncti

on

s

Servicing

Systems

Spread-

sheets /

DB’s

People

Source Data

EVOLV Integrated Risk and Finance Solutions

1. Reserving (FAS 5, FAS 114, TDRs, Journals, CECL Prep)

2. Loan Accounting (F91, Non-accrual, SBO, PCI, Journals)

3. Stress Testing (Model Execution, Aggregation, Templates)

4. Reporting (Credit Quality, SEC, Operational, Regulatory)

5. Custom Applications (Build on the EVOLV Platform)

Finance

Perspective

Regulatory

Perspective

Risk

Perspective

Operational

Perspective

Enterprise

Warehouse

Client GL

SaaS-Based Infrastructure

EVOLV SMART Data Solution

I

m

p

o

r

t

E

x

p

o

r

t

Integration Layer

• Data Rules Engine - API

• Integration, Normalization &

Validation

• Event Identification / Tagging

Loan Data Marts / BI

• OLAP - Star Schema

• Multiple Basis / Perspectives

• Risk and Finance in One Spot

• History / “Runs” Maintained

Platform Engines and Integration

User Interface Layer

Event Engine Workflow Engine Calculation Engines Reporting Engine Sub-ledger Engine

API Layer / Developer Toolkits

“The EVOLV’d State”

16

One Platform

Unified Source Data

Integrated Reporting Data Mart

Integrated Applications

The EVOLV Difference

17

Q&A Session

Contact Us

If you would like to hear more,

please reach out to us!

info@primaticsfinancial.com

703.342.0040