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BiPM ENCYCLOPEDIA →
Business Intelligence →
SECTION - Data Quality →
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CHAPTER - Data Quality Program
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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).
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Topics
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Data Quality Program initiation & Scoping is similar to a typical project initiation phase. This phase includes a project trigger, identifying sponsor, project manager, information gathering and finally coming-up with signed-off project agreement. Due to various reasons mentioned in the topic, objective of this phase is mainly to get the sponsorship for analyze phase, so get a clarity on the plan and costs.
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Data Mapping & Assessment includes data quality checks, data profiling, data volumes analysis and information chain analysis. It is a big subject and organized as a separate chapter. Please refer to chapter of Data Mapping & Assessment. This exercise has benefits of establishing quality baseline, build a more fact based business case and generating enough details to do an appropriate root cause analysis. Please refer to SEPARATE CHAPTER on Data Mapping & Assessment. This is because this as a concept is required for many Data Management initiatives (like Data Warehousing, Operational Data Stores etc..) as well. The outcome of this exercise is the Data Mapping & Analysis Report.
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The root cause analysis of data quality problems is a big subject. There are innumerable methods, techniques and heuristics, which can be applied. Both what and why of data quality leads to firming-up the right approach and plan for quality program. PLEASE REFER the detailed list of possible root causes in a separate chapter Reasons for Bad Data Quality
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Data Quality is like any other business need. The money and the effort is driven by the business case. Therefore one has to come out with the most optimum approach for what, why and when. A Data Quality approach could range from complete overhaul of data and processes down to fixing of a select set of data. For example - it could range from establishing a new health management system down to fixing diagnostic management details of OPD patients over last one year.
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This topics dwells upon the considerations one applies to come-upon most optimum DQ plan. As DQ Business Case has to be very cautious, one has look at the urgency, importance, do-ability, organization readiness etc. into consideration.
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While the previous topic are at conceptual level, this topic dwells upon the practical tips on how to firm-up your plan and how to get it finalized with business stakeholders.
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After the thinking process is over, its time to come out with a comprehensive proposal for implementing DQ program.
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Data quality policy can be like a clause in the constitution of the company, which help people take tough calls in the moments of Devil's alternative. For example, would you give in to sales pressure of technology spend on new CRM v/s implementing a more robust monitoring systems benchmark OR would you invest on redesigning your front end capture systems v/s using more glossy paper for card statement? A policy is useful when it guides people in moments of truth.
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Data Quality Organization is made to fix the responsibility, ownership and accountability. While every one in an organization is responsible for Data Quality, the accountability should be residing with senior and middle management to make it happen. The organization includes Data Quality Council, data owners, data stewards etc.
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These are the procedures to implement data quality assurance, data monitoring and data correction. If Data Quality Policy and Data Quality Organization are the enablers of Data Quality, these procedures drive its success.
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All Chapters in "Data Quality." Section
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