Save time, empower your teams and effectively upgrade your processes with access to this practical Machine Learning-Enabled Data Management Toolkit and guide. Address common challenges with best-practice templates, step-by-step work plans and maturity diagnostics for any Machine Learning-Enabled Data Management related project.

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

 

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The Toolkit contains the following practical and powerful enablers with new and updated Machine Learning-Enabled Data Management specific requirements:

STEP 1: Get your bearings

Start with…

  • The latest quick edition of the Machine Learning-Enabled Data Management 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 831 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 Management improvements can be made.

Examples; 10 of the 831 standard requirements:

  1. Are customers identified and high impact areas defined?

  2. Who are the people involved in developing and implementing Machine Learning-Enabled Data Management?

  3. What tools were used to narrow the list of possible causes?

  4. Did my employees make progress today?

  5. Implementation Planning- is a pilot needed to test the changes before a full roll out occurs?

  6. Are there Machine Learning-Enabled Data Management problems defined?

  7. What evaluation strategy is needed and what needs to be done to assure its implementation and use?

  8. Is the Machine Learning-Enabled Data Management scope manageable?

  9. What sources do you use to gather information for a Machine Learning-Enabled Data Management study?

  10. How do senior leaders deploy your organizations vision and values through your leadership system, to the workforce, to key suppliers and partners, and to customers and other stakeholders, as appropriate?

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 Management book in PDF containing 831 requirements, which criteria correspond to the criteria in…

Your Machine Learning-Enabled Data Management 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 Management Self-Assessment and Scorecard you will develop a clear picture of which Machine Learning-Enabled Data Management 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 Management 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 Management projects with the 62 implementation resources:

  • 62 step-by-step Machine Learning-Enabled Data Management Project Management Form Templates covering over 6000 Machine Learning-Enabled Data Management project requirements and success criteria:

Examples; 10 of the check box criteria:

  1. Cost Baseline: Has the actual cost of the Machine Learning-Enabled Data Management project (or Machine Learning-Enabled Data Management project phase) been tallied and compared to the approved budget?
  2. WBS Dictionary: Are significant decision points, constraints, and interfaces identified as key milestones?
  3. Responsibility Assignment Matrix: Are there any drawbacks to using a responsibility assignment matrix?
  4. Requirements Management Plan: Do you know which stakeholders will participate in the requirements effort?
  5. Requirements Management Plan: Is Requirements work dependent on any other specific Machine Learning-Enabled Data Management project or non-Machine Learning-Enabled Data Management project activities (e.g. funding, approvals, procurement)?
  6. Lessons Learned: How effectively and timely was the organizational change impact identified and planned for?
  7. Cost Management Plan: Is there a set of procedures defining the scope, procedures, and deliverables defining quality control?
  8. Activity Duration Estimates: Do procedures exist that identify when and how human resources are introduced and removed from the Machine Learning-Enabled Data Management project?
  9. Team Member Performance Assessment: To what degree does the teams purpose contain themes that are particularly meaningful and memorable?
  10. WBS Dictionary: Intermediate schedules, as required, which provide a logical sequence from the master schedule to the control account level?

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

1.0 Initiating Process Group:

  • 1.1 Machine Learning-Enabled Data Management project Charter
  • 1.2 Stakeholder Register
  • 1.3 Stakeholder Analysis Matrix

2.0 Planning Process Group:

  • 2.1 Machine Learning-Enabled Data Management 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 Management 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 Management 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 Management 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 Management 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 Management project with this in-depth Machine Learning-Enabled Data Management Toolkit.

In using the Toolkit you will be better able to:

  • Diagnose Machine Learning-Enabled Data Management 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 Management 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 Management investments work better.

This Machine Learning-Enabled Data Management All-Inclusive Toolkit enables You to be that person:

 

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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.