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Copyr igh t © 2014 , SAS Ins t i tu te Inc . A l l r i gh ts reserved .
DATA QUALITY IN A BIG DATA WORLD
Jos van Dongen SAS Nederland
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Barcelona
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Copyr igh t © 2014 , SAS Ins t i tu te Inc . A l l r i gh ts reserved .
Copyr igh t © 2014 , SAS Ins t i tu te Inc . A l l r i gh ts reserved .
INCORPORATE DATA GOVERNANCE DEFINE RULES AND POLICIES GOVERNING DATA
Who is responsible to maintain this data?
And where?
Where can I get this
information? Is the
quality of data
improving?
How am I supposed to use this
data?
What data quality
standards should this
data comply to?
Who can approve a
change to the business
data model or reference
data?
Are we compliant
with security,
privacy and risk
regulations?
How to leverage
the value of this data?
Are we making the most out of our data?
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Data Quality?
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DATA MNGT BUILDING BLOCKS DATA QUALITY
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BUSINESS USER BUSINESS GLOSSARY
Trace data from source to consumer and all the
steps in between
Document what has been done to data and how it
has been transformed
Govern who has access to data and who has
consumed data
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DATA QUALITY GOVERNANCE CYCLE
Iterative process where Business
and IT work together on
Data Governance
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DATA QUALITY PROFILE
Interactively quickly discover anomalies in the
data
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DATA QUALITY BUSINESS RULE VALIDATION
Validate whether the data complies to
quality standards
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DATA QUALITY DATA CLEANSING: PARSING & STANDARDIZING
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DATA QUALITY REMEDIATION
Review and resolve issues on a case by
case basis
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DATA QUALITY DASHBOARD
Real-time information when data is out of
compliance with established data
policies
Copyr igh t © 2014 , SAS Ins t i tu te Inc . A l l r i gh ts reserved .
Conclusion
#BigData = Data (duh…)
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…or is it?
§ Most data assets come from within company § Focus on structured data § Look at data to assess what occurred in past § The goal is that each single record is correct § Good database design requires years § Pay attention to „data stocks“* § Business users have to ask IT for analysis § There are clearly defined information requirements for each business process
§ A large proportion of data come from outside § Focus on structured and unstructured data § Real-time analysis to improve the outcome § The goal is that analytics results are accurate § Database as moving target, quick cycles § Pay attention to „data flows“* § Business users conduct analysis themselves § All internal and external data sources are used to gain best insight in a given situation
Traditional data management Big Data Analytics World
Source: Alexander Borek, Data Quality Strategy in a Big Data Analytics World
Copyr igh t © 2014 , SAS Ins t i tu te Inc . A l l r i gh ts reserved .
“By 2017, 50% of all companies in regulated industries will have a Chief Data Officer.”
Copyr igh t © 2014 , SAS Ins t i tu te Inc . A l l r i gh ts reserved .
SAS INFORMATION MANAGEMENT
A single platform. A singular approach to better data.
Copyr igh t © 2014 , SAS Ins t i tu te Inc . A l l r i gh ts reserved .
Copyr igh t © 2014 , SAS Ins t i tu te Inc . A l l r i gh ts reserved .
NOG VRAGEN???
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