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 877 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 877 standard requirements:
- Who, on the executive team or the board, has spoken to a customer recently?
- Is full participation by members in regularly held team meetings guaranteed?
- Will new equipment/products be required to facilitate Machine Learning-Enabled Data Management delivery for example is new software needed?
- Who will manage the integration of tools?
- At what point will vulnerability assessments be performed once Machine Learning-Enabled Data Management is put into production (e.g., ongoing Risk Management after implementation)?
- How do we foster the skills, knowledge, talents, attributes, and characteristics we want to have?
- How can skill-level changes improve Machine Learning-Enabled Data Management?
- Are the criteria for selecting recommendations stated?
- Identify an operational issue in your organization. for example, could a particular task be done more quickly or more efficiently?
- Will Machine Learning-Enabled Data Management have an impact on current business continuity, disaster recovery processes and/or infrastructure?
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 877 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:
- Schedule Management Plan: Does the time Machine Learning-Enabled Data Management projection include an amount for contingencies (time reserves)?
- Activity Duration Estimates: Why is it important to determine activity sequencing on Machine Learning-Enabled Data Management projects?
- Requirements Documentation: The problem with gathering requirements is right there in the word gathering. What images does it conjure?
- Stakeholder Management Plan: Who is responsible for arranging and managing the review(s)?
- Activity Duration Estimates: Do you think Machine Learning-Enabled Data Management project managers of large information technology Machine Learning-Enabled Data Management projects need strong technical skills?
- Monitoring and Controlling Process Group: How many more potential communications channels were introduced by the discovery of the new stakeholders?
- Requirements Management Plan: Have stakeholders been instructed in the Change Control process?
- Requirements Management Plan: Define the Help Desk model. Who will take full responsibility?
- Source Selection Criteria: How can business terms and conditions be improved to yield more effective price competition?
- Scope Management Plan: Has allowance been made for vacations, holidays, training (learning time for each team member), staff promotions & staff turnovers?
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.