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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


Barcelona


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?


Data Quality?


DATA MNGT BUILDING BLOCKS DATA QUALITY


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


DATA QUALITY GOVERNANCE CYCLE

Iterative process where Business

and IT work together on

Data Governance


DATA QUALITY PROFILE

Interactively quickly discover anomalies in the

data


DATA QUALITY BUSINESS RULE VALIDATION

Validate whether the data complies to

quality standards


DATA QUALITY DATA CLEANSING: PARSING & STANDARDIZING


DATA QUALITY REMEDIATION

Review and resolve issues on a case by

case basis


DATA QUALITY DASHBOARD

Real-time information when data is out of

compliance with established data

policies


Conclusion

#BigData = Data (duh…)


…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


“By 2017, 50% of all companies in regulated industries will have a Chief Data Officer.”


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