Evolution path for Data Quality Practice Tool-Kit

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Evolution path for Data Quality Practice Tool-Kit
Do we have to implement all Data Quality practice work-tools as an integrated piece or can start with some of them? What is their evolution path?

This page of 'Ask a question' is linked to: Data Quality,

NOTE- You may refer to ExecutionMiH.com data quality package for more detalils. In-brief, we provice a set of tools to drive your data quality agenda. The question is on if we can use these tools at a smaller scale and adopt them in sequence instead of all it once?

The answer is that you can adopt the tools sequentially and at varying scale. We would recommend the following evolution path for each of the tools. Here is the list, a small description and evolution path:

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 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.

Evolution path- No evolution path. Needs to be done ASAP, and is the first step in pursuing your data quality agenda.

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.

Evolution path- No evolution path. Needs to be done ASAP, and is among first step in pursuing your data quality agenda. Refer Data Quality Guidelines are no-brainer.

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.

Evolution path- Start with top 8-10 data entities like Customer Entity, Invoice entity, product entity, vendor entity....After the same is accomplished, work on their adherence and change management. Follow-up with other entities at the later stage.

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.

Evolution path- Start with assigning business ownership for top 10-12 data-groups (like customer, vendor, invoice, GL, product, location, vendor...)

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.

Evolution path- Start with top 10-12 data-groups. Focus on quality and change management. Follow-up with others.

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, categorizinge & defining the gaps and also to identify possible root-causes.

Evolution path- There is no question of evolution path in this. You can start applying this tool with a single initiative and expand its use to a wider base with time. As this is a work-tool, growing it gradually and testing out its application before popularising it is a prudent approach.

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.

Evolution path- Same as above.

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.

Evolution path- Same as above.

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.

Evolution path- Do it at least for one large system or a data-group (for example doing data quality assessment for all customer data), and not for a sub-system.

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 ' 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.

Evolution path- No fixed evolution path. If you plan to go for a data quality program, this is a very good template to use.

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.

Evolution path- No evolution path. Use it for every data quality program, where you are seeking funds and sponsorship.

Data Quality program proposal and agreement : Once you have got your recommendations agreed and funded, you can submit you 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.

Evolution path- No evolution path. Use it for every data quality program, as you get into execution mode.

Data quality Program WBS : This is the list of activities involved in a data quality program. You can use it to create your project plan around a data quality program.

Evolution path- No evolution path. Refer it for planning your Data Quality program.

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.

Evolution path- Do it with 2-3 important and reasonable sized initiatives, before expanding its use.

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.

Evolution path- Do it with 2-3 important and reasonable sized initiatives, before expanding its use.

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.

Evolution path- You can popularize its use from the day 1.

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.

Evolution path- Start witha key system-group. For example sales management systems (sales channel, sales compensation, sales campaign.). Expand after the learnings of use. The true value of this tool will be when it is used at an 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.

Evolution path- Do it for top 400-500 objects in the enterprise. If you are starting with a group of systems, do it for top 150 odd objects.

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 weight 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...

Evolution path- Do it for few sets of data quality gaps and expand its use after developing your comfort.

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.

Evolution path- Same as above.

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.

Evolution path- No evolution path. Popularize its use from the day 1.

Data Monitoring Request Form : This is a request form, which one captures all aspects of Data Monitoring.

Evolution path- No evolution path. Popularize its use from the day 1.

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.