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Successful stewardship Presentation
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Transcript of Successful stewardship Presentation
SUCCESSFUL STEWARDSHIP
Improving people, processes and tools for better Data Stewardship
Successful Stewardship
Where does the value come from?
Data Ownership and Accountability
› Data Stewardship is an approach to Data Governance that formalises accountability for managing information resources on behalf of others and for the best interests of the organization
› Data Stewardship consists of the people, organisation, and processes to ensure that the appropriately designated stewards are responsible for the governed data.
Australian companies are not formal
› Stewards may not always be known as stewards – but they are still needed.
› Governance should have a low entry level and not a high compliance cost.
› Carrying out steward tasks should be made as easy as possible.
› Good stewardship should be rewarded.
Australian IT Staff have DIY attitude
› Excel lets someone build their own report.
› Too many governance rules can alienate the DIY staff.
› An organisation wants better sharing of information and better management of information assets; but many experts want to do things their own way.
Australian DIY attitude
Regulators want greater maturityIn order to ensure that data risk management is not conducted in an ad hoc and fragmented manner, a regulated entity would typically adopt a systematic and formalised approach that ensures data risk is taken into consideration as part of its change management and business-as-usual processes.
APRA expects that a regulated entity would implement processes that ensure compliance with regulatory and legal requirements and data risk management requirements. This would typically include ongoing checks by the compliance function (or equivalent), supported by reporting mechanisms (e.g. metrics, exceptions) and management reviews.
Stewardship across Lines of Business
Bu
sin
ess
Val
ue
Data Stewardship Evolution
By IT SystemBy IT System
By OrganizationBy Organization
Pros – Easy of deploymentCons – Propagates fragmentation of data, IT-centric
Pros – Alignment with organization structureCons – Propagates fragmentation of data
By Master Data EntityBy Master Data Entity
Pros – Alignment with enterprise initiatives such as single view and cross-sell/up-sell
Cons – Organization challenges, requires System of Record (SOR)
Data Stewardship as a Competitive
Differentiator
Let anyone take part in Stewardship
Stewardship
Getting Started
Getting Started with Stewardship
Aspiration
› Better data quality› Reduced application development costs› Increased productivity› Reduced compliance issues.
Perspiration
› Subject matter experts are already too busy
› Installation and training costs› Extra roles needed for projects› Takes too long to retrospectively add
governance to existing information.
InfoSphere Information Governance Catalog - Glossary
Benefits:
› Aligns the efforts of IT with the goals of the business
› Provides business context and governance to information technology assets
› Establishes responsibility and accountability throughout the information development lifecycle
› Accelerates information development
› Dramatically increases business confidence in information assets
A meaningful directory of governed information
Hierarchical view and navigation
Glossary example: NBN
A point of interconnection between the NBN and the network of an Access Seeker, as determined by NBN Co and notified to the Access Seeker.
NBN Co Information Paper Access Seeker Accreditation
The connection point that allows Retail Service Providers (RSPs) and Wholesale Service Provides (WSPs) to connect to the NBN Co access capability. In the field, this is the physical port on the Ethernet Fanout Switch (EFS) switch located at NBN Co’s PoI, where an Access Seeker connects to establish exchange of traffic with NBN Co’s network.
NBN Co Website Glossary of Terms
Point of Interconnect (POI)
Short
and easy to read
Long and technical
Business Glossary terms provide a common language description of information used by the University and relationships to put that information into context
Glossary example: University
InfoSphere Information Governance Catalog - Compliance
› Declare the intended behavior of information
› Leverage business terms for defining functional scope
› Communicate precise intent for how information must be managed throughout its lifecycle:
− Data Discovery− Data Modeling− Master Data− Reference Data− Data Quality− Data Archiving− Data Privacy− Data Security− Data Movement− Data Transformation− Data Availability
Declare Information Governance Rules and track compliance
Information Governance Policy
Information Governance Policies & Rules
Data Quality Rule
InfoSphere Information Governance Catalog - Lineage
View end-to-end data lineage and impact analysis across data sources
› One-click view of end-to-end upstream and downstream data flows
› Fast display of complex flows› Advanced filters support
defining scope of displayed properties
› Business Lineage display available for non-technical audiences
› Links to Stewards and Glossary provide business context for graph items
Heterogeneous data flow reporting
How Data Lineage Works
They say “We want end to end date Lineage”
You deliver…Here you go…
They say “That is too complex!”
You ask ‘What do you really want?’
We want to know the rules
To calculate a study load (EFTSL) for a single subject, divide the number of credit points for the subject by 120.
One EFTSL is equivalent to 100 credit points and represents a standard annual full time load.
The EFTSL of any course can be determined by dividing its allocated credit points by 96. For example, a 12 credit point course has an EFTSL of 0.125 (12/96 = 0.125).
EFTSL = Macquarie full-time load for a Bachelor degree is 68 credit points over 3 years (equivalent of 22.667 per year). To calculate your EFTSL divide the unit value of the unit(s) by 22.667, eg 3 units = 3/22.667 EFTSL = 0.1324 EFTSL.
EFTSL Equivalent Full Time Study Load
Study Load (EFTSL) is a measurement based on a normal full time study load for a year.
At USC 8 courses undertaken per year is equivalent to one (1) EFTSL.
Agile Governance
The Big Data Approach is changing the way we govern data – making it higher risk
TRADITIONAL APPROACH BIG DATA APPROACH
Govern data to the highest standard. Store it, then use it for multiple purposes
Understand data and usage. Govern to the appropriate level. Use it, and iterate
RepositoryGovern to
PerfectionUseData
Data Explore / Understand
Govern Appropriately
Use
Finding Value in MDM
Start the MDM journey knowing what you can get out of it
Maximize 1:1 consumer relationshipsDeliver personalised offers aligned to unique behaviors, needs and desires
Brand reputationRight message every time in market
Marketing productivityIncreased breadth of digital channels, emphasis on cross-sell / up-sell / right-sell opportunities, understanding and embracing ROMI
Deliver value across all touch pointsBuild opportunity for revenue growth throughout marketing value chain
360 Degree View of the CustomerUnderstanding, responding and maximizing each unique customer relationship
Optimize marketing mix Model and plan balancing needs of channels, probability of ROI success and resource constraints
Customer growth and retention Demanding customers, commoditised products and crowded competitive marketplace
Define MDM Value
Big Data Quality Fail
Increased engagement
Increased revenue
Decreased risk
Less ‘gut feel’
More data (when used effectively)
Increase on Churn retention rate (no discounting required)
More newsletter article clicksMore articles read per session
Lookalike acquisition model increasing conversion
Strong Ad revenue growth 20%
10%
Linkage: audience connectionsAny hard links across accounts, Consumer & Household, Fuzzy matching, Enrichment (Single Customer View)
News Corp Example
Presentation to IBM SolutionConnect Event Sydney 2014
Household relationships
› Inspect potential household members and link to confirm relationships.
Employment Relationships
› Inspect relationships between companies and staff.
Using MDM Relationship Inspector
Joseph’sHousehold
Wife of
Daughterof
Sonof
Is the Subsidiary of
SuppliesProduct to
Is Married to
Is theOwnerof
Has anAccount
with
Is Employed by
Defining Value
Consuming Applications
AustraliaAustralia NZNZ ChinaChina IndiaIndiaPortalPortal
Kate Lamb32 George StreetPerth, 6000
Kate JonesPerth, WA 600012/06/1970
Catherine Jones44 Station StreetPerth, WA
Mrs K Lamb32 St. George 06/12/1970
Dr Katherine Lamb23 George StPerth, 600006/12/1970
Miss C JonesStation Street, PerthWestern Australia, 600012/06/1970
Person Entity
Dr. Katherine Lamb
Composite ViewDr Katherine Lamb
32 George St, Perth, WA 6000DOB: 12/06/1970
ANZ Bank › Trying to match customer records across 40 core banking systems and 32 countries.
360 Degree View
› The 360 degree view portal view of a customer as an MDM deliverable
MDM Success as shown by ANZ bank
$50 million to synchronise master data across all core banking
applications
$5 million to create a golden customer record
2 Data Stewards to review candidate
matches and submit data quality fixes
MDM registry management that is constantly improved
using Steward feedback.
MDM Stewardship made easy› The Steward can review what the merged/collapsed customer records will look
like. This is still a “virtual record” and rules can be tweaked and fine tuned.
The Benefits of Customer Matching
Media Organisation
› Matched 16.4m customer records› Found 2.7m duplicates› Found 8m potential household
relationships
Financial Services 2 Day PoC
› Just under 200K customer records› Legacy system matched 561 records› MDM PoC matched 3318 automatically› A further 5840 potential duplicates
found
Critical Success Factors for MDM
› Start with a 360 Degree View use case as this can use a “Best Guess” customer registry.
› Get in place a platform of stewardship and quality improvement around the initial registry.
› Move to more complex uses cases such as MDM applications and MDM synchronisation on top of this foundation.
Successful Data Quality
Data Quality Profiling, Monitoring and Scorecards
Finding Data Quality Problems is now EasyA data quality assessment identifies problems before the design and build phase
Low Dates 19/10/1918
High Dates 31/12/9999
Missing Dates
Columns without nulls
Columns we can ignore
Blank Values
Cross System Assessment Example
Making Cross System profiling easier:
›Distributed heterogeneous sources
›Handle situations where there is no documentation on data
structures
›Gain a rapid understanding of data relationships
›Create data quality metrics from profiling
›Detect confidential data elements
Cost Prohibitive Alternative Solutions:
›Manual spot checking of data
›Hand coding
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How do you understand enterprise data relationships?
Data Quality Example
What happens when identify data quality rules is an IT lead process:
Table Data Steward
Source Table Name
Source Column Name
Error Text
ErrorCondition Number
Risk Data Coordinator Dim_Facility AccountBaseNumber has length outside acceptable range 20105701
Risk Data Coordinator Dim_Facility AccountBaseNumber is null 20105702
Risk Data Coordinator Dim_Facility AccountName is null 20105801
Risk Data Coordinator Dim_Facility AccountNumber has length outside acceptable range 20105601
Risk Data Coordinator Dim_Facility AccountNumber is null 20105602
Risk Data Coordinator Dim_Facility AccountOpenDate is in future 20106301
Risk Data Coordinator Dim_Facility ApplicationScore has value = 0 20107801
REQUESTED_FLD
The REQUESTED_FLD column is for past, current and future requests for grant money. The length frequencies reveal some very large requests - a 12 digit request for 2014 and five records with an 11 digit request.
Medium Futher investigation is required to determine whether these are valid values. Due to the large requests, it appears summarised data may be incorrectly included in the dashboard, which would be performing its own aggregation and totalling.
RDO_REF RDO_REF – has three different versions of an empty field. It has 145 values set to “#N/A” and 39 set to “NA” and 676 set to <null>.
High It is not desirable to have three different versions of “non applicable” turning up in dashboard reporting so either the source needs to be cleaned up to be consistent or an ETL data load rule is needed to convert all three to the same value of “N/A” – “Non Applicable”.
RDO_REF There are two main patterns of data for values in the RDO_REF column and this usually indicates different rules at different times. There are 6557 values set to the format of ANNNNNNN such as R0015838 and there are 1178 values in the format of NNNN such as 1279.
Medium This mixture of alpha numeric codes and numeric codes may not belong together in Dashboard reporting.
Defining the Business Impact is Important
Attaching a cost to a DQ Rule
BirthDate is null or zero
BirthDate age is out of bounds
If this rule is important then what is the business impact of it failing?Whey should managers and stewards care?
Data Quality Example
Putting Data Quality into business terms
Defining the Impact
Vendor item code data was provided in all data files. Results showed a minimum match of 28.6% and maximum match of 100%. Net content and unit of measure data was provided in all files. Matching varied from 0% to 99.6% for the two fields.
Varying vendor item code formats and special characters such as dots and dashes are found to be used frequently but are often not supported by healthcare IT systems nor used in supplier systems.
Example DQ Scorecard
Stewardship Business Process Example
Detect DQ
Exception
Steward Opens
Exception
Steward Repairs
Data
Data Quality Change Request
submitted
Data Quality Change
Approved
Support fix data quality
problem in source
The Stewardship Center is where a team of stewards log in and review the data that failed data quality checks. It manages a team of stewards, subject matter experts and support staff so they can investigate and fix problems.
Manage stewards: View and collaborate on MDM and DQF data quality problems in the Stewardship Center
A steward can accept or reject a
data change
A fix can be applied
automatically or manually
Data work flow: Set up custom stewardship workflows
Let Stewards Multi Task
DW Load Exceptions
MDM Duplicate Candidates
Reference Data Checks
Data Quality Success Factors
› Focus on data quality issues with a real impact.› Make it easy to collect data quality metrics.› Make it easy to be a steward across different facets of data quality.› Put in a combination of people, processes and tools that lets you tackle data
quality in a consistent way.› Make your stewards more useful.› Make your non-stewards better stewards.
FRESH IDEAS…
TO YOUR BUSINESS WITH… TO YOUR CUSTOMERS WITH…TO EXTERNAL TOUCH POINTS
LICENSING IMPLEMENTATION TRAINING APPLICATIONS ANALYTICSINFRASTRUCTUREDATA ASSETSWEB
SOFTWARECOMPONENTS
TECHNOLOGY DISCIPLINES & SPECIALTIES
CRITICAL SYSTEMS &RESOURCES
TRANSFORM YOUR BUSINESS THROUGH TECHNOLOGY
CONNECT REQUIREMENTSTO KPIs
DESIGN SMARTERSOLUTIONS