Save time, empower your teams and effectively upgrade your processes with access to this practical Machine Learning-Enabled Data Quality Toolkit and guide. Address common challenges with best-practice templates, step-by-step work plans and maturity diagnostics for any Machine Learning-Enabled 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 Machine Learning-Enabled Data Quality specific requirements:
STEP 1: Get your bearings
Start with...
- The latest quick edition of the Machine Learning-Enabled 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 994 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Machine Learning-Enabled Data Quality improvements can be made.
Examples; 10 of the 994 standard requirements:
- What has been the observed frequency and nature of changes in the back end data structures or new data quality issues discovered during the creation of the dashboard?
- Will the processing use profiling, automated decision making or special category data to help make decisions on someones access to a service, opportunity or benefit?
- Are unintended failures to follow requirements promptly reported, and are personnel and organizations acknowledged for self identification and reporting errors?
- Are there processes in place to determine when a data analysis finding is based on actual system performance and when it is based on data quality issues?
- Has data quality profiling of the required data been completed to determine existing source data quality issues which would limit the metrics reporting?
- Does the installation or activity have adequate administrative capabilities to support operation of the plan in the functions that would be involved?
- Has it engaged in acts of privacy and data protection violations, including unauthorized data collection, processing and any other use?
- Has data harmonization and standardization been conducted for the data elements for paperless trade at your organization level?
- What are your plans for using predictive analytics with machine learning capabilities in your data driven measurement approach?
- How can b2c organizations gain a competitive advantage by predicting future customer behavior and business trends?
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 Machine Learning-Enabled Data Quality book in PDF containing 994 requirements, which criteria correspond to the criteria in...
Your Machine Learning-Enabled 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 Machine Learning-Enabled Data Quality Self-Assessment and Scorecard you will develop a clear picture of which Machine Learning-Enabled 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 Machine Learning-Enabled 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 Machine Learning-Enabled Data Quality projects with the 62 implementation resources:
- 62 step-by-step Machine Learning-Enabled Data Quality Project Management Form Templates covering over 1500 Machine Learning-Enabled Data Quality project requirements and success criteria:
Examples; 10 of the check box criteria:
- Risk Management Plan: Is the customer technically sophisticated in the product area?
- Quality Management Plan: What process do you use to minimize errors, defects, and rework?
- Variance Analysis: What is the incurrence of actual indirect costs in excess of budgets, by element of expense?
- Source Selection Criteria: Does an evaluation need to include the identification of strengths and weaknesses?
- Stakeholder Management Plan: Were Machine Learning-Enabled Data Quality project team members involved in the development of activity & task decomposition?
- Lessons Learned: Was any formal risk assessment carried out at the start of the Machine Learning-Enabled Data Quality project, and was this followed up during the Machine Learning-Enabled Data Quality project?
- Lessons Learned: How efficient were Machine Learning-Enabled Data Quality project team meetings conducted?
- Probability and Impact Assessment: Do requirements put excessive performance constraints on the product?
- Procurement Management Plan: Are cause and effect determined for risks when others occur?
- Team Directory: How do unidentified risks impact the outcome of the Machine Learning-Enabled Data Quality project?
Step-by-step and complete Machine Learning-Enabled Data Quality Project Management Forms and Templates including check box criteria and templates.
1.0 Initiating Process Group:
- 1.1 Machine Learning-Enabled Data Quality project Charter
- 1.2 Stakeholder Register
- 1.3 Stakeholder Analysis Matrix
2.0 Planning Process Group:
- 2.1 Machine Learning-Enabled Data Quality project Management Plan
- 2.2 Scope Management Plan
- 2.3 Requirements Management Plan
- 2.4 Requirements Documentation
- 2.5 Requirements Traceability Matrix
- 2.6 Machine Learning-Enabled Data Quality project Scope Statement
- 2.7 Assumption and Constraint Log
- 2.8 Work Breakdown Structure
- 2.9 WBS Dictionary
- 2.10 Schedule Management Plan
- 2.11 Activity List
- 2.12 Activity Attributes
- 2.13 Milestone List
- 2.14 Network Diagram
- 2.15 Activity Resource Requirements
- 2.16 Resource Breakdown Structure
- 2.17 Activity Duration Estimates
- 2.18 Duration Estimating Worksheet
- 2.19 Machine Learning-Enabled Data Quality project Schedule
- 2.20 Cost Management Plan
- 2.21 Activity Cost Estimates
- 2.22 Cost Estimating Worksheet
- 2.23 Cost Baseline
- 2.24 Quality Management Plan
- 2.25 Quality Metrics
- 2.26 Process Improvement Plan
- 2.27 Responsibility Assignment Matrix
- 2.28 Roles and Responsibilities
- 2.29 Human Resource Management Plan
- 2.30 Communications Management Plan
- 2.31 Risk Management Plan
- 2.32 Risk Register
- 2.33 Probability and Impact Assessment
- 2.34 Probability and Impact Matrix
- 2.35 Risk Data Sheet
- 2.36 Procurement Management Plan
- 2.37 Source Selection Criteria
- 2.38 Stakeholder Management Plan
- 2.39 Change Management Plan
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 Machine Learning-Enabled 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 Machine Learning-Enabled Data Quality project or Phase Close-Out
- 5.4 Lessons Learned
Results
With this Three Step process you will have all the tools you need for any Machine Learning-Enabled Data Quality project with this in-depth Machine Learning-Enabled Data Quality Toolkit.
In using the Toolkit you will be better able to:
- Diagnose Machine Learning-Enabled 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 Machine Learning-Enabled 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 Machine Learning-Enabled Data Quality investments work better.
This Machine Learning-Enabled 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.
