Data Quality Toolkit

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Be accountable for partnering with internal data source providers and risk stakeholders across all lines of business to complete data investigations and resolve Data Quality issues with the goal of improving the timeliness and accuracy of all Risk Reporting on a continual basis.

More Uses of the Data Quality Toolkit:

  • Ensure you cultivate; respond to system failures; analyze and resolve underlying problems; perform virus Security Operations, distribute software over the network or other similar operations.

  • Control: partner closely with business stakeholders and Data Stewards to identify and unlock opportunities and Data Quality issues identify relevant data sets needed for Data Analytics.

  • Ensure you fully understand and appreciate that things need to be tested out and moved through a multi environment deployment process with engineering rigor.

  • Lead technical expertise in modern Client Digital Engineering and associated technologies, cloud platforms, Data Quality, governance and architecture.

  • Facilitate Data Quality program and implement appropriate changes to systems or processes to correct any Data Quality problems encountered.

  • Provide guidance on the creation and best practices around Data Governance, Data Stewardship and overall Data Quality initiatives and processes, as part of the overall data effort.

  • Drive: work regularly with Information Technology team regarding the development, implementation and monitoring of effective Vendor Management technology systems to drive maximum automation and increase Data Quality and integrity.

  • Be accountable for understanding Data Integration, Data Quality, Data Architecture and Master Data Management, project life cycle phases, best practices, and processes.

  • Audit: monitor data and performance of sales and accounts activities to identify trends, gaps, and opportunities; resolve Data Quality issues if necessary.

  • Be accountable for communicating technical information means translating business requirements into technical plans and translating technical terms into business requirements and actions.

  • Assure your organization facilitates the development and implementation of Data Quality Standards, Data Protection standards and adoption requirements across the enterprise.

  • Support data improvement and Data Quality efforts by highlighting and communicating Data Issues observed when producing data and analytics products.

  • Confirm you enable; understand, conceptualize and implement analytic solutions to improve Data Quality and consistency and to establish procedures and methodology to ensure data accuracy and consistency of use.

  • Guide: review all data received from primary and secondary sources to evaluate Data Quality and availability over time to maintain consistency and accuracy.

  • Direct: review and update Data Quality rules in applications and confirm accuracy and availability of data for decision support, regulatory and Financial Reporting, and compliance monitoring, in coordination with Business Architects by data domain and Data Stewards.

  • Make sure that your project provides direction to teams across the enterprise regarding the development and implementation of the Information Management strategy to support the measuring and monitoring of enterprise Business Needs.

  • Engage in ongoing evaluation of Data Quality, identify anomalies and discrepancies, and contribute expertise to understanding the cause and implementing corrective measures.

  • Be certain that your organization complies; partners with it (and more specifically the Chief Data Officers and the teams) to translate data requirements and business Process Automation to improve Business Rules and drive improved Data Quality.

  • Engage with enterprise Data Management operations to coordinate and execute enterprise Data Governance processes, Procedures And Standards during Master Data Management, Data Quality and Content Management activities.

  • Write scripts to Perform Data Management and Data Quality checks on the huge volumes historical data received, streamline and align with current data received on daily and weekly basis.

  • Lead the development and implementation of Data Quality Standards, Data Protection standards and adoption requirements across your organization.

  • Collaborate with developers on data ingestion to ensure high performing Data Processing, inclusion of Data Quality checks/balances with the expectation of an extensible data model adapting to new project requirements.

  • Oversee: monitor Data Quality, identify issues and trends, oversee remediation plans, implement data controls, and manage Data Quality remediation strategies with Key Stakeholders.

  • Confirm your enterprise provides Business Process, system support and Data Quality governance through data coordination and integration to ensure efficient processes and consistent Data Flows to business and stakeholders.

  • Lead: review requirements and verify controls are in accordance with the trusted sources methodology, with support from Data Stewards and data custodians.

  • Support Data Quality and Master Data Management activities to ensure data from source systems is accurate, current, consistent and fit for purpose.

  • Collaborate with Process Owners/data owners to monitor Data Usage and foster trust in data by driving Data Quality by making sure procedures and rules adapt as data domains/sets expand or business requirements change.

  • Control: research and recommend Machine Learning and Artificial intelligence techniques for delivering actionable insights and to create accurate predictions.

  • Confirm your strategy develops Technical Specifications and project plans to ensure development activities proceed in accordance with project deliverables and time frames.


Save time, empower your teams and effectively upgrade your processes with access to this practical Data Quality Toolkit and guide. Address common challenges with best-practice templates, step-by-step Work Plans and maturity diagnostics for any Data Quality related project.

Download the Toolkit and in Three Steps you will be guided from idea to implementation results.

The Toolkit contains the following practical and powerful enablers with new and updated Data Quality specific requirements:

STEP 1: Get your bearings

Start with...

  • The latest quick edition of the Data Quality Self Assessment book in PDF containing 49 requirements to perform a quickscan, get an overview and share with stakeholders.

Organized in a Data Driven improvement cycle RDMAICS (Recognize, Define, Measure, Analyze, Improve, Control and Sustain), check the…

  • Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation

Then find your goals...

STEP 2: Set concrete goals, tasks, dates and numbers you can track

Featuring 999 new and updated case-based questions, organized into seven core areas of Process Design, this Self-Assessment will help you identify areas in which Data Quality Improvements can be made.

Examples; 10 of the 999 standard requirements:

  1. Do you recognize Data Quality achievements?

  2. Can you do Data Quality without complex (expensive) analysis?

  3. What is the scope of Data Quality?

  4. How do you assess the Data Quality pitfalls that are inherent in implementing it?

  5. Are the Data Quality requirements testable?

  6. Who makes the Data Quality decisions in your organization?

  7. Who is responsible for errors?

  8. What do you want to improve?

  9. What is the Data Quality problem definition? What do you need to resolve?

  10. Are supply costs steady or fluctuating?

Complete the self assessment, on your own or with a team in a workshop setting. Use the workbook together with the self assessment requirements spreadsheet:

  • The workbook is the latest in-depth complete edition of the Data Quality book in PDF containing 994 requirements, which criteria correspond to the criteria in...

Your Data Quality self-assessment dashboard which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next:

  • The Self-Assessment Excel Dashboard; with the Data Quality Self-Assessment and Scorecard you will develop a clear picture of which Data Quality areas need attention, which requirements you should focus on and who will be responsible for them:

    • Shows your organization instant insight in areas for improvement: Auto generates reports, radar chart for maturity assessment, insights per process and participant and bespoke, ready to use, RACI Matrix
    • Gives you a professional Dashboard to guide and perform a thorough Data Quality Self-Assessment
    • Is secure: Ensures offline Data Protection of your Self-Assessment results
    • Dynamically prioritized projects-ready RACI Matrix shows your organization exactly what to do next:


STEP 3: Implement, Track, follow up and revise strategy

The outcomes of STEP 2, the self assessment, are the inputs for STEP 3; Start and manage Data Quality projects with the 62 implementation resources:

  • 62 step-by-step Data Quality Project Management Form Templates covering over 1500 Data Quality project requirements and success criteria:

Examples; 10 of the check box criteria:

  1. Cost Management Plan: Eac -estimate at completion, what is the total job expected to cost?

  2. Activity Cost Estimates: In which phase of the Acquisition Process cycle does source qualifications reside?

  3. Project Scope Statement: Will all Data Quality project issues be unconditionally tracked through the Issue Resolution process?

  4. Closing Process Group: Did the Data Quality project team have enough people to execute the Data Quality project plan?

  5. Source Selection Criteria: What are the guidelines regarding award without considerations?

  6. Scope Management Plan: Are Corrective Actions taken when actual results are substantially different from detailed Data Quality project plan (variances)?

  7. Initiating Process Group: During which stage of Risk planning are risks prioritized based on probability and impact?

  8. Cost Management Plan: Is your organization certified as a supplier, wholesaler, regular dealer, or manufacturer of corresponding products/supplies?

  9. Procurement Audit: Was a formal review of tenders received undertaken?

  10. Activity Cost Estimates: What procedures are put in place regarding bidding and cost comparisons, if any?

Step-by-step and complete Data Quality Project Management Forms and Templates including check box criteria and templates.

1.0 Initiating Process Group:

2.0 Planning Process Group:

3.0 Executing Process Group:

  • 3.1 Team Member Status Report
  • 3.2 Change Request
  • 3.3 Change Log
  • 3.4 Decision Log
  • 3.5 Quality Audit
  • 3.6 Team Directory
  • 3.7 Team Operating Agreement
  • 3.8 Team Performance Assessment
  • 3.9 Team Member Performance Assessment
  • 3.10 Issue Log

4.0 Monitoring and Controlling Process Group:

  • 4.1 Data Quality project Performance Report
  • 4.2 Variance Analysis
  • 4.3 Earned Value Status
  • 4.4 Risk Audit
  • 4.5 Contractor Status Report
  • 4.6 Formal Acceptance

5.0 Closing Process Group:

  • 5.1 Procurement Audit
  • 5.2 Contract Close-Out
  • 5.3 Data Quality project or Phase Close-Out
  • 5.4 Lessons Learned



With this Three Step process you will have all the tools you need for any Data Quality project with this in-depth Data Quality Toolkit.

In using the Toolkit you will be better able to:

  • Diagnose Data Quality projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices
  • Implement evidence-based best practice strategies aligned with overall goals
  • Integrate recent advances in Data Quality and put Process Design strategies into practice according to best practice guidelines

Defining, designing, creating, and implementing a process to solve a business challenge or meet a business objective is the most valuable role; In EVERY company, organization and department.

Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?'

This Toolkit empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Data Quality investments work better.

This Data Quality All-Inclusive Toolkit enables You to be that person.


Includes lifetime updates

Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.