Level of usage of Data Quality Practice Tool-Kit

NOTE- You may refer to ExecutionMiH.com data quality package for more detalils. In-brief, we provide a set of tools to drive your data quality agenda. The question is on, if we can use these tools at a smaller scale?

There is no fixed answer to this. Some of the practice-tools can be enterprise wide (or larger scope compared to a single sytsem) like Data quality Policy, Data Management Standards, Data quality assurance and control guidelines. Some of the work-tools like DMA Management, System data quality assessment can be used for varied scopes. Here is the brief description of the tools in the tool-kit and the levet at which they should be ideally used:

Overall Data Quality Management

These are the foundation elements for pursuing your data quality agenda.

• Data Quality Policy: This tool provides you a template, list of sections, which can be in a data quality policy, along with the examples of the text in each of the sections, which you can use as you create the data policy for your organization.- AT ENTERPRISE LEVEL.
• Data Quality Control Guidelines : This practice-tool enables you to create the data quality control guidelines. It provides you with the flow and also links to our encyclopedia, where all the guidelines are listed in detail.- AT ENTERPRISE LEVEL.
• Data Management Quality Standards: This is going to be among most time consuming piece. This tool gives you template, guidelines and many examples on how to document your universal data domains & Standards, Business Rules and Data Models.- AT ENTERPRISE LEVEL.
• Data Management Stake-Holding and Responsibility Matrix : This is a single reference point on all the inter linkages across systems, functions, processes and Data-Groups. As you make any change in your environment, this is a great reference to understand the stakeholders. It also identifies the owners and sponsors for data-groups, processes, functions and systems.- AT ENTERPRISE LEVEL.
• Data Group Master File: This is the central reference point and universal definition for data-groups. Data-groups are the logically grouped business data, which has to be assigned as business owner.- AT ENTERPRISE LEVEL

Data Quality and System Health Assessment

• Data Mapping and Assessment Management : Data Mapping and Assessment, gives a factual analysis of the current state of your data and its structure. This practice-tool helps you to manage the Data Mapping and Assessment exercise, by capturing the results, categorizing, defining the gaps and also identifying possible root-causes.- AT ANY LEVEL (SUB-SYSTEM, SYSTEM, SYSTEM-GROUP, DATA-GROUP, ENTERPRISE, FUNCTIONAL)
• Data Mapping and Assessment Report : This is the formal output of your Data Mapping and Assessment exercise. This template is filled-up and submitted to the stakeholders for review and sign-off. AT ANY LEVEL (SUB-SYSTEM, SYSTEM, SYSTEM-GROUP, DATA-GROUP, ENTERPRISE, FUNCTIONAL)
• Data Mapping and Assessment WBS : This is the Work Break-Down Structure for Data Mapping and Assessment Exercise. You can use it to develop project plan for DMA exercise.- AT ANY LEVEL (SUB-SYSTEM, SYSTEM, SYSTEM-GROUP, DATA-GROUP, ENTERPRISE, FUNCTIONAL)
• System Landscape Data Quality and Management Health Assessment Tool : This is a comprehensive single point capture of your results and analysis, as you assess your system landscape. It summarily captures the results of DMA, but also includes other factors, which determine the health of the system. This includes the level of controls and DQ assurance mechanics in the systems, and the state of overall data governance and management. AT ANY LEVEL (SUB-SYSTEM, SYSTEM, SYSTEM-GROUP, DATA-GROUP, ENTERPRISE, FUNCTIONAL)

Data Quality Program

Data quality program is a combination of multiple smaller initiatives to address varied gaps, to establish common standards and practices and to create Data quality awareness. Unlike programs related to OLTP transaction based systems, Data Quality program has many more unknowns, as most of the time it is linked to resolving fundamental issues encompassing systems and processes. Therefore we recommend a 'front-heavy' data quality program initiation phase, which invests into assessment, analyzing, solution-finding and prioritizing the DQ gaps. The outcome and recommendations coming out of this phase are then funneled into the data quality program planning and execution phase.

• Data Quality Program Initiation Proposal : You can use this template to fill-up DQ program initiation proposal. The data quality program initiation phase requires some level of funds, as you do analysis, and solution-finding on Data Quality Gaps. Therefore, having a good DQ initiation phase proposal will help.- AT SYSTEM-GROUP, FUNCTIONAL, DATA-GROUP OR ENTERPRISE LEVEL. GENERALLY IS NOT DONE AT A SYSTEM LEVEL OR SUB-SYSTEM LEVEL
• Data Quality Program Initiation completion Report : Based on the findings of DMA, System health assessment and DQ Gaps Management and Tracking tool, you get all what you need to firm-up your findings and proposal.-AT SYSTEM-GROUP, FUNCTIONAL, DATA-GROUP OR ENTERPRISE LEVEL. GENERALLY IS NOT DONE AT A SYSTEM LEVEL OR SUB-SYSTEM LEVEL
• Data Quality program proposal and agreement : Once you have got your recommendations agreed and funded, you can submit your DQ program proposal, with all the trappings of a typical program. The TOC includes objectives, deliverables, timelines, resources, communication frame-work, risk management plan etc.-AT SYSTEM-GROUP, FUNCTIONAL, DATA-GROUP OR ENTERPRISE LEVEL. GENERALLY IS NOT DONE AT A SYSTEM LEVEL OR SUB-SYSTEM LEVEL
• Data quality Program WBS : This is the list of activities involved in a data quality program. You can use it to create your program plan around a data quality program.-AT SYSTEM-GROUP, FUNCTIONAL, DATA-GROUP OR ENTERPRISE LEVEL. GENERALLY IS NOT DONE AT A SYSTEM LEVEL OR SUB-SYSTEM LEVEL

Data Quality Assurance and Gaps management in an Initiative

These tools are used to track and manage the DQ assurance in an initiative. The scope is all the objects (input forms, data entry forms, business processes...), where you need to ensure Data Quality.

• Data Quality Assurance Method-Level Tracking tool: This tool tracks the adherence to the DQ Assurance mechanisms at different phases of an initiative. It is the big picture reference for various stakeholders to review, know, sign-off to the level of adherence. AT INITIATIVE LEVEL, SYSTEM OR SYSTEM-GROUP LEVEL
• Data Quality Assurance Specs, Design and Deployment object-level tracking tool : This tool tracks the adherence to the DQ Assurance mechanisms at different phases of an initiative at object level (input forms, data entry forms, business processes...), where you need to ensure Data Quality. This tool feeds data into the Method-Level Tracking tool. AT INITIATIVE LEVEL, SYSTEM OR SYSTEM-GROUP LEVEL
• Data quality Assurance Checklist for an initiative : This is a conscience keeper checklist to see that DQ assurance methods are being incorporated in an initiative. AT INITIATIVE LEVEL, SYSTEM OR SYSTEM-GROUP LEVEL

Data Quality Assurance and Gaps Management in Business as Usual

• Data Quality Gap prioritization, Approach finalization, planning and tracking tool : This is a tracking tool, which is used to manage and track ALL gaps, data quality initiatives and data quality programs. This tool enables you to prioritize a gap, document and analyze the alternatives to address the gap, estimate the effort, assign the gap to an initiative and track its closure. -AT SYSTEM-GROUP, SYSTEM OR ENTERPRISE LEVEL
• DQ Assurance Object-inventory Tracking: This tool tracks the adherence to the DQ Assurance mechanisms for all key objects (input forms, data entry forms, business processes, and data entities). If a stakeholder wants to know the state of DQ controls in the environment, this is the central reference.- AT SYSTEM-GROUP, SYSTEM OR ENTERPRISE LEVEL
• Data Quality Risk Assessment Checklist: A lack of data quality does not always mean high risk. Similarly, less than perfect solution to address a data quality issue may still be acceptable given its cost-benefit. Risk assessment is driven by many factors. This checklist enables the user to weigh the risk of the DQ issue and its possible solutions on factors like- Volume and Value risk, Speed of deterioration, criticality of data, cascading of DQ gap to external stakeholders, probability of incidence etc...- AT ANY LEVEL
• Data Quality Gap Impact Assessment Tool: This tool enables you to assess and quantify the business impact of a data quality gap, given its risk. While the Data Quality Risk Assessment checklist is more of a back-end review for the analysts, the output of this tool goes to the business owners and CIO, for final decision. A Data Quality gap may be felt by a single function, but its actual impact could be cross-functional.- AT ANY LEVEL
  

Data Monitoring and Data Correction

• Data Monitoring Checklist : This checklist helps you to ensure a holistic data monitoring. As you conduct Data Monitoring, you can refer while planning, testing and execution stages. - AT ANY LEVEL
• Data Monitoring Request Form : This is a request form, which one captures all aspects of Data Monitoring.- AT ANY LEVEL
• Data Correction Checklist : This checklist helps you to ensure a holistic data correction. As you conduct Data correction, you can refer while planning, testing and execution stages. - AT ANY LEVEL
• Data Correction Request Form : This is a request form, which one captures all aspects of Data Correction. - AT ANY LEVEL