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Execution-MiH Encyclopedia →
Enterprise Intelligence
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SECTION - Data Quality |
Any dashboard, scorecard or a report will
be reduced to a NIL value, if the audience don't trust the data
contained therein. In today's world, data is going exponentially complex
and the maze of system & interfaces is reaching "beyond visual
range". With supply chain management, CRM and other practices,
the enterprise boundaries of data are blurring. The regulatory &
disclosure pressures are mounting. Its time that organizations re-in-force
their focus on Data Quality. Data quality has different connotations and
follow the conventional principles of prevention, monitoring and
remedy. Data Quality does not seek perfection, but business-case
driven sponsorship.
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Chapters
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Before one gets onto working on data quality, one has to appreciate
on what is data quality?, why is it so important? and what are the
reasons for data quality failures?
Topics in this chapter : Data Quality Definition- What is Data Quality? → Data Quality Tolerance and Business-Case → Root Cause of Data Quality Issues → Impact of Bad Data Quality →
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Prevention is better than cure. Quality can be much assured by
pro-active assurance controls, while designing your systems and
processes. Avoid bad data through interface controls, data standards,
data models, database & Data processing and business controls.
Topics in this chapter : Data Interface Exchange Controls → Data Entry Input Form Controls → Data Domain and Data Standards Controls → Data Model Entity Relationship Controls → Business Rules Definition → Batch-Processing controls → Business Process Controls → Business Partner Interface Controls → Data Quality Monitoring →
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CRM is one of the most important customers of Data Quality. Organizations
have improved much more on transaction processing data quality in
comparison to Customer related data Quality. Let's look at Customer
Data quality issues, Customer data matching, de-duping, data augmentation
and enrichment.
Topics in this chapter : Customer Data Quality Impacts → Customer Data Challenges → Customer Data Variations → Customer Data Searching and Matching → Customer Data Correction and Techniques → Customer Data Augmentation and Enrichment →
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Data Mapping & Assessment (DMA) is a comprehensive assessment of the state of Data in an environment. This includes assessment of data quality, Data Profiling and Data Flow Analysis. The application of DMA exercise is found for Data Quality Program, Data Conversion, Data Warehousing and wherever one needs to analyze the existing state of Data.
Topics in this chapter : Data Mapping and Assessment → Data Mapping and Column Analysis → Data Model Entity Relationship Analysis → DMA Data flow Analysis →
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In the previous chapters, you have gone through various aspects of Data Quality. Lets see on how to implement them. Data Quality program in a company is a combination of one time initiatives and a continuous, fairly business as usual set of people, process and technology changes which drive an acceptable level of data sanity. This can be at functional level (generally) or at enterprise level (rarely), and it can sweep across business and technology worlds (rarely) or is limited to one of them, with some cascading impact on the other (generally).
Topics in this chapter : Data Quality Program Initiation → Data Quality Program DMA → Data Quality Gaps Root Cause Analysis → Data Quality Program Approach → Data Quality Analysis considerations → Data Quality Approach Finalization → Data Quality Program Analyze Phase and Business case Closure → Data Quality Policy → Data Quality Organization Roles → Data Quality Control Procedures →
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All Sections in " Enterprise Intelligence ."
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