4. 4
9/24/2015
Data Quality Project Objectives
The processes and systems of our Customer Service
Organization will begin to shift as we…
Embrace new technologies and processes to improve our
internal efficiencies (i.e.: replacing Banner or removing
manual processes)
Embrace new technologies and processes to improve our
customer experience (i.e.: improving the IVR. Mobile,
and Web channel interactions)
Client
Name
The primary goals of the Customer Data Quality Improvement Project is to ensure we
have:
An implemented data quality tool and supporting data quality governance structure
Improve customer data quality through governance and processes to provide systematic
iterations of profiling, cleansing, and data monitoring
Trained client personnel on the tool along with transition of the governance ownership
and processes
5. 5
9/24/2015
“Over 50% of data integration projects fail because of poor data quality.”
- Gartner Group
The Customer Data Quality Improvement Project will help achieve improvements to:
Current Customer data quality
Pave the path for upcoming replacement of CIS
Streamlined Data Governance process
Improvements to Customer data quality will allow client name to benefit from reduced
exceptions and better business decisions.
Current client name Customer data quality issues:
Missing data
Invalid data
Non-standardized data
Duplicate data
Invalid data formats
Benefits of the Data Quality Project
6. 6
9/24/2015
Data Quality Issues, Impacts, and DQ’s Role
Data Profiling can assist with data analysis to discover data quality issues such as missing / invalid data, data format
patterns, and non-standardized data elements.
Snapshot of Issues Business Impact Customer Data Quality improvement can help by:
Profiling and data standardization (address and zip
code) helps eliminate incorrect mailing to
Customers reducing mailing costs, faster revenue
recognition and enhanced Customer experience
Most common are bills returned
due to incorrect and / or incomplete
address
Invalid data
Data Quality scorecarding could help further help consistently monitor the quality of data provided by the source systems.
Observed trends in quality received from the source system can help drive conversations with source systems to fix data
issues.
Consistent Customer data improving Customer
interaction, target marketing with accurate
customer master data while increasing confidence
in reporting / analytics
Impacts spanning from Marketing
to Serving due to poor quality of
data. Incomplete data leads to poor
decision making
Missing data
Profiling and identifying common patterns and
incorrect formats for cleansing provides tighter
system integration, lowering IT costs and improving
efficiencies
Invalid / inaccurate population of
fields may not be fully understood
across multiple systems –
Customer Segmentation issues
Invalid data formats /
patterns
Profiling, matching, merging and de-duping can
assist with reducing Customer calling costs and
increase Customer satisfaction
Duplicate calls to same Customers
for same or different products –
unnecessary phone calls
Non-standardized /
duplicate data
8. 8
9/24/2015
Approach
Key steps below followed Accenture’s and client’s software selection methodology and approach to arrive
at the final DQ tool selection.
Step 1:
Vendor Assessments /
Recommendation
Step 2:
RFP Process
Step 3:
DQ Tool Selection
• Based on Market Research,
Accenture’s experience, and
availability of skilled client name
resources, Informatica and IBM were
shorted for detailed evaluation.
• Solutions from both vendors, were
evaluated using various methods:
scoring based on responses from
vendors to various tool capabilities
(RFI scoring), and team’s feedback
on vendor demos (Demo Scoring).
• RFP was defined and internally
reviewed with Purchasing during
the week of April 7th.
• RFP along with clearly defined
timelines specifying expected
turnaround times, was then issued
to both the vendors, Informatica and
IBM.
• Based on RFP responses, and other
factors such as Usability, availability of
skilled resources in the marketplace,
availability of skills within client name,
etc. each vendor would be ranked prior
to making a final DQ tool selection.
• Upon final decision, a DQ tool would be
procured, and installed within client
name environment.
March 1 April 9 April 11 May 2 May 16
We are here
10. 10
9/24/2015
Evaluation Criteria
Based on Market Research, 4 vendors were initially chosen: Trillium, Oracle, Informatica, and IBM.
• Top 2 according to Market Research
• Current technical skillsets at PGE
• Current and future technology
roadmap
• Accenture DQ Expertise
Shortlist to 2 vendors
To further facilitate recommending best fit Data Quality technology for client name, each short listed
vendor’s capability in below mentioned areas were evaluated:
• Architecture & Installation
• Source System Connectivity
• Data Profiling
• Data Cleansing
• Operational Needs
• Metadata Management
Evaluate Informatica
vs. IBM
against various
criterias
Step 1
11. 11
9/24/2015
Data quality assurance is a discipline focused on ensuring that data is fit for use in business processes
ranging from core operations to analytics and decision-making, regulatory compliance, and engagement
and interaction with external entities
Data Quality Market Leaders
Leaders in this space…
• Demonstrate strength across a full range of
data quality functions
• Provide data quality functions that include
profiling, parsing, standardization, matching,
validation and enrichment capabilities
• Exhibit a clear understanding and vision of
where the market is headed, including
recognition of noncustomer data quality
issues and delivery of enterprise-level data
quality implementations
• Have an established market presence,
significant size and a multinational presence
(directly or as a result of a parent company)
Step 1
12. 12
9/24/2015
Advantage and Consideration Inputs
• Leverage market research and analysis to assist
• Identify current technical skillsets at client name
• Understand current and future technology roadmap
• Tap into Accenture data quality expertise to assist in short
listing based on client name requirements
Vendor Shortlisting
From the initial 4 Vendors (IBM, Informatica, Trillium & Oracle), Informatica & IBM were selected for
the RFI and Demo executions, based on key advantages / considerations and Gartner Research.
Advantages - IBM & Informatica
• Top 2 leaders in Gartner’s Magic Quadrants
• Visionary and Ability to execute
• PGE has developers for IBM Datastage and
Informatica’s data movement into OBIEE
• Technology path to continue with IBM and
Informatica
• Largest client install base
Considerations - Oracle & Trillium
• Skillsets not currently supported
• Only Trillium in the Gartner Magic Quadrant
• Trillium currently doesn’t have it’s own data
integration technology
• Oracle data quality and data integration
capabilities are not as mature as ranked by
Gartner
Step 1
13. 13
9/24/2015
Vendor RFI Scoring Summary
Excellent Satisfactory Poor Very poorGood
Based on the evaluation of tool’s strengths across defined categories, Informatica scored higher in the
RFI response. While IBM’s QualityStage solution is a close competitor, Informatica’s responses scored
slightly higher than that of IBM in Usability and Data Cleansing categories.
Criteria Description Informatica IBM
Architecture &
Installation
Includes topics related to system architecture such as load balancing, parallel
processing, scalability, Disaster Recovery, High Availability
Connectivity
Source Integrations / Connectivity focus on compatibility with various types of
sources / source adapters (XML, Oracle, SQL Server, SOA, JAVA, etc.)
Data Profiling
Data Profiling involves performing structural analysis of data, and discovering
column level, table and cross table level data analysis (e.g. % of nulls, value
distributions, etc.)
Data Cleansing
Data quality is the application of business rules and logic to fix data problems
as an integral part of the data integration process.
Operationals
Includes operational processes around data quality program such as
scheduling data profiling/cleansing rules, security, training & support, data
quality monitoring, easy of use, exception alerts, etc.
Metadata
Requirements
Focuses on metadata capabilities of the product such as documentation to
end users allowing them to understand data they view, audit trail for loads
and errors encountered, etc.
Step 1
14. 14
9/24/2015
Vendor Demo Scoring Summary
Based on the evaluation of tool’s strengths across defined categories, Informatica stands out as a
superior data quality solution. While IBM’s solution is a close competitor, Informatica had slightly
higher scores in Usability & Operational Requirements, Data Profiling, and Data Cleansing
Excellent Satisfactory Poor Very poorGood
Criteria Description Informatica IBM
Architecture &
Installation
Includes topics related to system architecture such as load balancing,
parallel processing, scalability, Disaster Recovery, High Availability
Source Integrations
Source Integrations focus on compatibility with various types of sources /
source adapters (XML, Oracle, SQL Server, SOA, JAVA, etc.)
Data Profiling
Data Profiling involves performing structural analysis of data, and
discovering column level, table and cross table level data analysis (e.g. %
of nulls, value distributions, etc.)
Data Cleansing
Data quality is the application of business rules and logic to fix data
problems as an integral part of the data integration process.
Operationals
Includes operational processes around data quality program such as
scheduling data profiling/cleansing rules, security, training & support, data
quality monitoring, easy of use, exception alerts, etc.
Step 1
15. 15
9/24/2015
Score Summary
Both the solutions were subjected to weighted scoring evaluation out of RFI and Vendor demo
processes.
Data Quality Tools Evaluation Summary
DQ Categories
RFI Scoring Demo Scoring (Approach D)
Informatica IBM
MAX Possible
Score
Informatica IBM
Score Weighted
Score
Score Weighted
Score
Score Score
Architecture & Installation 33 84 33 84 15 12.84 12.34
Connectivity 29 81 30 84 36 27.64 28.43
Data Profiling 153 348 153 348 36 30.86 26.57
Data Cleansing 180 351 177 348 30 25.36 20.70
Operationals 126 300 126 300
69 49.64 41.32
Metadata Requirements 27 63 27 63
TOTALS 548 1227 546 1227 186 146.34 129.36
Please Note: For Demo scoring, criterias within Operationals / Metadata Requirements categories were combined into one category
Step 1
16. 16
9/24/2015
Score Summary
12.84
27.64 30.86 25.36
49.64
146.34
12.34
28.43 26.57 20.70
41.32
129.36
Architecture Connectivity Data Profiling Data Cleansing Operational Final Score
Demo Scoring (Approach D)
Informatica IBM
Step 1
33 29
153 180
126
27
548
33 30
153 177
126
27
546
Architecture &
Installation
Source System
Integration
Data Profiling Data Cleansing Operational
Aspects
Metadata
Requirements
TOTALS
RFI Response Scoring
Informatica IBM
17. 17
9/24/2015
Based on results from RFI scoring and Vendor demos/scoring, the team
unanimously recommends Informatica as the solution of choice.
Note: This recommendation did not take pricing into consideration.
Recommendation
Informatica
Market
Research
RFI
Scoring
Demo
Scoring
18. RFP Process & Timelines
ThereismoretochoosingavendorthanjustsubmittinganRFPtovendors.AseriesofstepsencompassestheentireRFP
process.
19. 19
9/24/2015
RFP Process and Timelines
Clearly defined timelines specifying expected turnaround times were submitted to both the vendors, Informatica and
IBM, along with the RFP.
Step 2
April 11 April 16 April 25 May 2 May 16April 18
RFP issued to bidders
(email invitation) Client response to bidders Review and Evaluate proposal
Questions from bidders
• Present the project Scope
• Define the activities
• Understand timeline and approach
• PGE submits responses to bidders via
email
• Responses must be submitted by
4:00pm PST
• Bidders submit their final responses to
RFP
• Final responses must be in by 2:00pm PST
• Key stakeholders for RFP, will review and
evaluate proposals.
Bidders final response to RFP
• PGE would finalize on the DQ tool
decision
• Client would enter contract
negotiations with each vendor
• Client would prepare final SOW
details
• Client would proceed to execute the
contract with chosen vendor
SOW-Contract
• Bidders should submit questions to RFP
by 12:00PM PST
We are
here
21. 21
9/24/2015
DQ Tool Evaluation – Other Factors
Functionality Vendor solutions should meet the Functionality needs for the data cleansing efforts at client
Solution can be installed within the time and cost constraints of the CET Data Quality Project timeline
Speed to
Install
Discussion & Evaluation of Vendors Against Overall IT Criteria
Resources to maintain the solution can be readily staffed, whether internally or from the external marketplace
Skillset
Availability
Strategic Fit
Vendor solution should fit strategically into the overall IT Portfolio and Strategy, considering both today’s and
tomorrow’s needs
Step 3
Pricing
Licensing and Pricing agreements will be evaluated to determine Total Cost of Ownership and best fit with
project budgets
Speed to
Purchase
Solution can be purchased and procured within the time and cost constraints of the CET Data Quality Project
timeline
23. 23
9/24/2015
Next Steps
While vendors respond to RFP and rest of the RFP process is on-going, client name management
envisions to gain traction on additional tasks towards the DQ Tool selection.
Receive RFP responses and
pricing details from vendors.
Doug from Purchasing is working
towards this effort.
Note: Since Informatica would be a
new product, ironing out pricing details
for this solution would take longer.
Target Date: 4/18/2014
Onwer: Client Executive
Review tool selection process and a summary of positives
/ negatives of each toolset with Cam Anderson.
Target Date: 4/24/2014
Owner: Client PM
Review the process and reasons for final decisions with
IT leadership.
Target Date: 4/25/2014
Owner: Client Director
25. 25
9/24/2015
Demo Scoring Summary
Data Quality Tools Evaluation Summary
DQ Categories
Approach A Approach B Approach C Approach D
Informatica IBM Informatica IBM Informatica IBM Informatica IBM
Architecture &
Installation
11.82 12 6 to 15 4 to 15 12.84 14.43 12.84 12.34
Connectivity 26.14 29.36 18 to 36 18 to 36 27.64 31 27.64 28.43
Data Profiling 30.86 26.57 27 to 36 18 to 33 30.86 26.57 30.86 26.57
Data Cleansing 22.86 17.93 17.5 to 28 9 to 25 25.36 20.7 25.36 20.70
Operationals 43.32 35.14 22.75 to 58 10.5 to 54 50.2 41.32 49.64 41.32
TOTALS 135 121 na na 146.9 134.02 146.34 129.36
The team scored demos based on 4 different approaches. Regardless of the scoring approach taken,
Informatica Data Quality (IDQ) seems to have been rated higher by all evaluators.
Below is the scoring summary across all approaches.
26. 26
9/24/2015
Demo Scoring Summary
12.84
27.64 30.86
25.36
49.64
146.34
12.34
28.43 26.57
20.70
41.32
129.36
Architecture Connectivity Data
Profiling
Data
Cleansing
Operational Final Score
Vendor Demo Scoring Summary
(Approach D)
Informatica IBM
12.84 27.64 30.86 25.36
49.64
146.34
2.16
8.36 5.14 4.64
19.36
39.66
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
200.00
Informatica Variance from Highest Score
Possible
Variance from
Highest Score
Possible
Informatica
12.34
28.43 26.57 20.70
41.32
129.36
2.66
7.57 9.43
9.30
27.68
56.64
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
200.00
Architecture Connectivity Data
Profiling
Data
Cleansing
Operational Final Score
IBM Variance from Highest Score Possible
Variance from
Highest Score
Possible
IBM
Out of 4 different scoring approaches for vendor
demo evaluation, Approach D was unanimously
chosen as the approach to be used.
Regardless of the approach chosen, however,
Informatica ranked higher based on client
team’s vendor demo scoring.
27. 27
9/24/2015
Client Name currently holds some licenses for Catapult DQ toolset that was used
to limited extent by the Wave 1 project and have been used somewhat by the
Manufacturing group.
The tool was considered for the DQ initiative for CIS Implementation, however, it
did not fit the needs due to:
Catapult DQ Tool - Findings
Factors Issue Description
Cost Licensing fee is based on # of records (expensive)
Niche Is a niche player for EAM data (Equipment records,
locations, BOMs, Materials, PM Plans, etc.)
Cleansing limitations Does not have capability to cleanse Customer data
