Save time, empower your teams and effectively upgrade your processes with access to this practical Azure Data Lake Toolkit and guide. Address common challenges with best-practice templates, step-by-step work plans and maturity diagnostics for any Azure Data Lake 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 Azure Data Lake specific requirements:
STEP 1: Get your bearings
- The latest quick edition of the Azure Data Lake 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 799 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Azure Data Lake improvements can be made.
Examples; 10 of the 799 standard requirements:
- Will the big data cloud environment support scale-out, shared-nothing massively parallel processing, storage optimization, dynamic query optimization, and mixed workload management better than alternative deployment models (e.g., on-premises appliances, software on commodity hardware)?
- Will it allow users to retrieve their data and their application artifacts and to have strong assurance that the cloud service provider will delete all copies and not retain any materials belonging to the cloud service customer after an agreed period?
- What are the potential areas of conflict that can arise between organisations IT and marketing functions around the deployment and use of business intelligence and data analytics software services and whats the best way to resolve them?
- How likely is it that a particular approach will reduce the cost of deploying and managing Big Data analytics and maximize the productivity and efficiency of IT operations over the deployments expected useful life?
- In what manner and under which conditions is it feasible to apply effective control measures to Big Data implementations and gain increased certainty over the validity of data analytics outcomes?
- Thanks to the evolution of the IoT, machine learning is advancing at a rapid pace. Machines are getting smarter and collecting data themselves, which poses the question; who will manage the data?
- How many predictive analytics functions are measured explicitly on improvement in predictive accuracy, with the CEO keeping an eye on this (retention, acquisition, risk, pricing models) ?
- How do you protect the assured, proper, and consistent collection, processing, communication, use and disposition of personally identifiable information in the relation to cloud services?
- Will the big data cloud environment better support on-demand provisioning, scaling, optimization, and execution of diverse data and analytic resources than alternative deployment models?
- Will it protect the assured, proper, and consistent collection, processing, communication, use and disposition of personally identifiable information in the relation to cloud services?
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 Azure Data Lake book in PDF containing 799 requirements, which criteria correspond to the criteria in...
Your Azure Data Lake 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 Azure Data Lake Self-Assessment and Scorecard you will develop a clear picture of which Azure Data Lake 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 Azure Data Lake 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 Azure Data Lake projects with the 62 implementation resources:
- 62 step-by-step Azure Data Lake Project Management Form Templates covering over 1500 Azure Data Lake project requirements and success criteria:
Examples; 10 of the check box criteria:
- Procurement Audit: Are employees with cash disbursement responsibilities required to take scheduled vacations?
- Schedule Management Plan: Is a pmo (Azure Data Lake project management office) in place and provide oversight to the Azure Data Lake project?
- Risk Audit: Is the number of people on the Azure Data Lake project team adequate to do the job?
- Source Selection Criteria: When is it appropriate to conduct a preproposal conference?
- Responsibility Assignment Matrix: Too many as: does a proper segregation of duties exist?
- Process Improvement Plan: What actions are needed to address the problems and achieve the goals?
- Scope Management Plan: Has appropriate allowance been made for the effect of the learning curve on all personnel joining the Azure Data Lake project who do not have the required prior industry, functional & technical expertise?
- Quality Audit: Does everyone know what they are supposed to be doing, how and why?
- Team Member Performance Assessment: What variables that affect team members achievement are within your control?
- Human Resource Management Plan: Is it standard practice to formally commit stakeholders to the Azure Data Lake project via agreements?
Step-by-step and complete Azure Data Lake Project Management Forms and Templates including check box criteria and templates.
1.0 Initiating Process Group:
- 1.1 Azure Data Lake project Charter
- 1.2 Stakeholder Register
- 1.3 Stakeholder Analysis Matrix
2.0 Planning Process Group:
- 2.1 Azure Data Lake project Management Plan
- 2.2 Scope Management Plan
- 2.3 Requirements Management Plan
- 2.4 Requirements Documentation
- 2.5 Requirements Traceability Matrix
- 2.6 Azure Data Lake 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 Azure Data Lake 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 Azure Data Lake 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 Azure Data Lake project or Phase Close-Out
- 5.4 Lessons Learned
With this Three Step process you will have all the tools you need for any Azure Data Lake project with this in-depth Azure Data Lake Toolkit.
In using the Toolkit you will be better able to:
- Diagnose Azure Data Lake 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 Azure Data Lake 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 Azure Data Lake investments work better.
This Azure Data Lake 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.