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.
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
- 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 752 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 752 standard requirements:
- Has a project plan, Gantt chart, or similar been developed/completed?
- Are customer(s) identified and segmented according to their different needs and requirements?
- What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Machine Learning-Enabled Data Management?
- How do we Identify specific Machine Learning-Enabled Data Management investment and emerging trends?
- Among our stronger employees, how many see themselves at the company in three years? How many would leave for a 10 percent raise from another company?
- What are our needs in relation to Machine Learning-Enabled Data Management skills, labor, equipment, and markets?
- If we got kicked out and the board brought in a new CEO, what would he do?
- If our company went out of business tomorrow, would anyone who doesnt get a paycheck here care?
- Do you monitor the effectiveness of your Machine Learning-Enabled Data Management activities?
- What do we want to improve?
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 752 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:
- Assumption and Constraint Log: Are there unnecessary steps that are creating bottlenecks and/or causing people to wait?
- Project Portfolio management: Governance. How does the organization ensure that Machine Learning-Enabled Data Management project and program benefits and risks are being managed to optimize the overall value creation from the portfolio?
- Human Resource Management Plan: Responsiveness to change and the resulting demands for different skills and abilities?
- Executing Process Group: How many different communication channels does the Machine Learning-Enabled Data Management project team have?
- Issue Log: Are the Machine Learning-Enabled Data Management project Issues uniquely identified, including to which product they refer?
- Quality Audit: How does the organization know that its system for supporting staff research capability is appropriately effective and constructive?
- Procurement Audit: Is it clear which procurement procedure the organization has opted for?
- Variance Analysis: Are all elements of indirect expense identified to overhead cost budgets of Machine Learning-Enabled Data Management projections?
- Procurement Management Plan: Specific - Is the objective clear in terms of what, how, when, and where the situation will be changed?
- Risk Management Plan: Do the people have the right combinations of skills?
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
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.
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.