Data Engineering Toolkit

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Save time, empower your teams and effectively upgrade your processes with access to this practical Data Engineering Toolkit and guide. Address common challenges with best-practice templates, step-by-step work plans and maturity diagnostics for any Data Engineering 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 Engineering specific requirements:


STEP 1: Get your bearings

Start with...

  • The latest quick edition of the Data Engineering 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 Engineering improvements can be made.

Examples; 10 of the 999 standard requirements:

  1. Will your api analytics solution be used by engineering only, or do you expect other teams like product, marketing, and customer success to also leverage your organizations api data?

  2. How to construct high-quality data analysis tools that are easy to modify and customize, and yet at the same time are able to handle data in the terabyte range?

  3. How could the entire requirements engineering activities be efficiently and effectively automated when the requirements sources are dynamic data?

  4. Have you invested in and supported the development of supply chain knowledge communities where data-driven digital collaboration can grow?

  5. Will you consider extending the implementation timeline to provide more time to address data engineering, report development, and testing?

  6. How often is the analytics/data science team able to generate key insights that get implemented in the target organization?

  7. Is the connectivity for self-service users the same as it would be for IT or is it limited to a subset of data sources?

  8. Can the model be calibrated by using data from managerial judgment or historical data or through experimentation?

  9. What advice do you give to an inside sales leader with no engineering experience to become more data-driven?

  10. Are you protecting corporate intellectual property from industrial espionage or customer data from thieves?


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 Engineering book in PDF containing 999 requirements, which criteria correspond to the criteria in...

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

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

Examples; 10 of the check box criteria:

  1. Quality Management Plan: Have you eliminated all duplicative tasks or manual efforts, where appropriate?

  2. Procurement Audit: Has management taken the necessary steps to ensure that relevant control systems are always up to date?

  3. Scope Management Plan: Pop quiz Ð what changed on Data Engineering project scope statement input?

  4. Probability and Impact Assessment: Is the present organizational structure for handling the Data Engineering project sufficient?

  5. Scope Management Plan: Are all key components of a Quality Assurance Plan present?

  6. Stakeholder Analysis Matrix: What mechanisms are proposed to monitor and measure Data Engineering project performance in terms of social development outcomes?

  7. Project Charter: Success determination factors: how will the success of the Data Engineering project be determined from the customers perspective?

  8. Procurement Audit: Audits: when was your last independent public accountant (ipa) audit and what were the results?

  9. Stakeholder Analysis Matrix: Is there a clear description of the scope of practice of the Data Engineering projects educators?

  10. Change Management Plan: What risks may occur upfront, during implementation and after implementation?

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

1.0 Initiating Process Group:

  • 1.1 Data Engineering project Charter
  • 1.2 Stakeholder Register
  • 1.3 Stakeholder Analysis Matrix


2.0 Planning Process Group:

  • 2.1 Data Engineering project Management Plan
  • 2.2 Scope Management Plan
  • 2.3 Requirements Management Plan
  • 2.4 Requirements Documentation
  • 2.5 Requirements Traceability Matrix
  • 2.6 Data Engineering 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 Data Engineering 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 Data Engineering 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 Engineering 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 Data Engineering project with this in-depth Data Engineering Toolkit.

In using the Toolkit you will be better able to:

  • Diagnose Data Engineering 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 Engineering 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 Engineering investments work better.

This Data Engineering 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.