Provide technology agnostic technical leadership, drive technology stack selection and ensure the project team is setup for success on any number of Open Source, commercial, on premise and/or cloud based Data Engineering technologies.
More Uses of the Data Engineering Toolkit:
- Ensure your organization complies; monitors Industry Trends in data infrastructure, Data Architecture and Data Engineering; Assesses, develops and implements Data Integration tools.
- Collaborate with Data Engineering and visualization engineers to access and manipulate data, account for Data Gathering requirements, and display results.
- Become the expert in working with business leaders and make strategic business decisions based on insights from operational data.
- Initiate: Data Engineering as a function sets the foundation to successfully data intake, Data Management and Data Transformation.
- Establish: partner closely with colleagues in technology to help manage Data Engineering and technology resources for Etl Development.
- Manage work with Engineering teams to check the feasibility of the solution, build stories and architects the solution for the Projects.
- Use models as a starting point for designing and developing technologies that enable new or enhance existing business capabilities.
- Collaborate with the Data Engineering team to optimize data model and architecture to reduce Data Storage duplication, optimize ETL processes, and query performance.
- Be accountable for defining and managing complex System Architectures, understanding API design, and ensuring timely and cost effective completion.
- Contribute to shared Data Engineering tooling and standards to improve the productivity and quality of output for Data Engineers across your organization.
- Confirm your organization ensures common data model design and maintenance, data distribution, consolidation, and integration compliance and Data Engineering and Data Engineering best practices.
- Drive: design and execute analytic projects in collaboration with business, product, Data Engineering, finance, Business Analysts, and other specialists.
- Become the expert in one of the domains of API design, payments processing, risk/fraud detection, Data Engineering, Machine Learning, or blockchains.
- Be accountable for confirming that new analytics adhere to data and platform guidelines created in partnership with platform architects, Data Governance, Data Engineering, and other relevant teams.
- Orchestrate: partner with tech and Data Engineering teams to ensure all data used for Marketing Analytics is clean and reliably updated.
- Secure that your design develops partnerships and work closely with IT execution teams on the development of analytic infrastructures, Data Engineering, or related Business Intelligence enablement efforts.
- Orchestrate: Data Engineering to the core analysis, modelling, transformation and visualization of datasets for online products and backend data platform.
- Use patterns and framework to develop, support and solve problems for enterprise level applications supported by a team.
- Pilot: strategically partners and work with it execution teams on the development of analytic infrastructures, Data Engineering, or related Business Intelligence efforts.
- Evaluate: partner with the analytics and Data Engineering teams to understand problems to be solved with your current data platform, identify solutions, and deliver high quality data foundations to data stakeholders across the business.
- Oversee: partner with your Data Engineering team to build requirements for data infrastructure necessary to facilitate efficient analysis and reporting.
- Provide Thought Leadership on Cloud and Agile transformations for the Data Engineering team and the entire Organization.
- Formulate: partner with Key Stakeholders to identify initiatives and execute solutions to people related business problems using Data Analysis, Advanced Analytics and Data Engineering best practices.
- Ensure you produce; understand and proactively communicate factors affecting business performance to stakeholders by partnering with business leaders, other analysts and Data Engineering teams.
- Create relevant market offerings related to Software Engineering, Data Engineering, Analytics, and Modern Systems Integration.
- Translate business/user requirements into technical requirements, and applies Creative Problem Solving that bring requirements to fruition for a team.
- Initiate: conduct Data Architecture, performance, design and model review for Data Engineering and lead Data Governance functions.
- Manage: communication and skills to share information with teams across the manufacturing site through verbal, written, and visual means.
- Become an advocate for the Data Engineering team by developing and championing Data Engineering practices with the team and with your organization at large.
- Provide guidance and mentorship to managers and individual contributors on the high quality Data Engineering and infrastructure Engineering teams.
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
- 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:
- How can you measure the performance?
- How and when will the baselines be defined?
- Who uses your product in ways you never expected?
- Are you missing Data Engineering opportunities?
- How will Data Engineering decisions be made and monitored?
- What are the top 3 things at the forefront of your Data Engineering agendas for the next 3 years?
- How do you gather requirements?
- What is a worst-case scenario for losses?
- Who needs to know about Data Engineering?
- How do you know if you are successful?
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 994 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:
- Cost Management Plan: Eac -estimate at completion, what is the total job expected to cost?
- Activity Cost Estimates: In which phase of the Acquisition Process cycle does source qualifications reside?
- Project Scope Statement: Will all Data Engineering project issues be unconditionally tracked through the Issue Resolution process?
- Closing Process Group: Did the Data Engineering project team have enough people to execute the Data Engineering project plan?
- Source Selection Criteria: What are the guidelines regarding award without considerations?
- Scope Management Plan: Are Corrective Actions taken when actual results are substantially different from detailed Data Engineering project plan (variances)?
- Initiating Process Group: During which stage of Risk planning are risks prioritized based on probability and impact?
- Cost Management Plan: Is your organization certified as a supplier, wholesaler, regular dealer, or manufacturer of corresponding products/supplies?
- Procurement Audit: Was a formal review of tenders received undertaken?
- Activity Cost Estimates: What procedures are put in place regarding bidding and cost comparisons, if any?
1.0 Initiating Process Group:
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
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.