Be accountable for building and delivering AWS and/or Azure Cloud Platforms infrastructure and solutions to help your clients meet the soaring data and cloud demands of AI and machine learning, IoT, advanced analytics, open source and other emerging digital technologies.
More Uses of the Big Data Analytics Toolkit:
- Develop, maintain, and continuously improve customer churn, customer acquisition, and customer value models to inform go to market strategy and drive improved profitability.
- Develop, maintain, and continuously improve media mix modeling, mass media attribution, multi touch attribution, and other models to support the optimization of marketing spend.
- Ensure you cloud architecture, design and development that supports diagnostic instruments, with a focus on data architecture, big data analytics, microservices, and database services.
- Lead: independently develop customized algorithms to solve analytical problems with incomplete data sets and implements automated processes for efficiently producing scale models.
- Manage it security and data governance to ensure that your organizations data analytics and integration products are effectively secured and that risks are mitigated.
- Become a data expert, utilizing the data warehouse to inform modeling approaches, understand customer behavior, research outliers, and prepare data for usage by the quantitative modeling team.
- Secure your organization provides support by mining data to identify behavior patterns, predict trends, and forecast outcomes to support data driven decisions to drive change in your customer interactions and risk management.
- Direct: partner with business development and account management teams in helping to ensure customer success in building and migrating applications, software and services on the aws platform.
- Devise strategy and engage with solution architects, account managers, professional services and partners to define a database and analytics engagement strategy for aws key accounts.
- Be accountable for working closely with the various teams data science, database, network, BI and application teams to make sure that all the big data applications are highly available and performing as expected.
- Manage work with application management team, and where necessary, other members of the analytics team to efficiently execute larger scale analytic deliverables and operationalize the results.
- 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.
- Recognize and drive opportunities to lead technical considerations in designing Data lakes, Data warehouses, IT operations analytics based on Machine learning methodologies, and similar large scale Data products.
- Be accountable for designing, architecting, and developing solutions leveraging big data technology (open source, aws, or microsoft) to ingest, process and analyze large, disparate data sets to exceed business requirements.
- Orchestrate: behavioral classification, forecasting and prediction, fusion of multiple data sources, or in general improving your current algorithm by more sophisticated data driving models.
- Make sure that your organization serves as a resource to advise management and business stakeholders on use of quality business analytics, tools, and methods to improve efficiency, accuracy, and interpretation of various business metrics.
- Manage work with client management team and your customers to define and take ownership of scope, intermediate deliverables, and timelines around larger scale analytic deliverables.
- Promote technical readiness and capability of database and analytics services by driving organizational initiatives across multiple geographies to develop and share best practices.
- Develop productive relationships with Business Unit leaders across your organization to influence how data integration technology solutions can enable new sources of value.
- Identify recurring problems and bottlenecks that might be improved through upgrades to your software product, new technologies in the analytics team infrastructure, or further research into statistical methodology.
- Manage work with large structured / unstructured data sets, various rest/wms data services, multiple database programs and collection systems, in a modeling and analytical environment, solving hard intelligence problems and issues.
- Ensure you lead development of custom predictive and prescriptive algorithms interfacing with large data sets, based on principles from statistics, machine learning, and operations research.
- Engage to architect and design for your customers data driven applications, and drive the evolution of the AWS Services by innovating around capabilities like Advanced analytics, Machine learning, and Serverless.
- Collaborate with your customer, partners, and AWS engineering teams to solve for enterprise problems like Database Migrations, Data warehousing, Real time analytics, Operational analytics, and Big data processing on the cloud.
- Be accountable for ensuring highly available, secure, and compliant infrastructure to cost effectively support all your business critical needs, whether in on premise, cloud/multi cloud, or hybrid deployments.
Save time, empower your teams and effectively upgrade your processes with access to this practical Big Data Analytics Toolkit and guide. Address common challenges with best-practice templates, step-by-step work plans and maturity diagnostics for any Big Data Analytics 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 Big Data Analytics specific requirements:
STEP 1: Get your bearings
- The latest quick edition of the Big Data Analytics 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 998 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Big Data Analytics improvements can be made.
Examples; 10 of the 998 standard requirements:
- How can simulation modeling be integrated with big data infrastructure, data fusion and data assimilation from a subterranean sensing system to provide near real time analytics and decision support?
- What approaches can be identified when companies explore the potentials of big data in the initiation phase of innovation adoption and what factors influence the choice of approach?
- Does the team demonstrate sufficient actionable knowledge on the policy and regulatory environment that could impede or catapult utilization of research outputs?
- How important is the involvement IT disciplines for new initiatives and projects in the area of business intelligence, analytics, and big data to be successful?
- How can enterprises use big data to make better, faster decisions, while being more proactive, gaining competitive advantage and lowering business risks?
- Is analytics simply a marketing term applied to statistical techniques run against big data or is the analysis of big data fundamentally different?
- Which attributes are most important to your organization when considering technology solutions in the area of big data and analytics?
- How are your organizations assurance providers leveraging big data and analytics to help your organization achieve its objectives?
- Is there a requirement, interest, desire for further consideration of using analytics appliances for big data streams reduction?
- How can big data analytics be used to help the manager to identify the success factor of a specific engagement approach?
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 Big Data Analytics book in PDF containing 998 requirements, which criteria correspond to the criteria in...
Your Big Data Analytics 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 Big Data Analytics Self-Assessment and Scorecard you will develop a clear picture of which Big Data Analytics 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 Big Data Analytics 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 Big Data Analytics projects with the 62 implementation resources:
- 62 step-by-step Big Data Analytics Project Management Form Templates covering over 1500 Big Data Analytics project requirements and success criteria:
Examples; 10 of the check box criteria:
- Executing Process Group: Is the program supported by national and/or local organizations?
- Assumption and Constraint Log: Is there documentation of system capability requirements, data requirements, environment requirements, security requirements, and computer and hardware requirements?
- Project Charter: Dependent Big Data Analytics projects: what Big Data Analytics projects must be underway or completed before this Big Data Analytics project can be successful?
- Variance Analysis: Are there knowledgeable Big Data Analytics projections of future performance?
- Team Member Performance Assessment: To what degree are sub-teams possible or necessary?
- Human Resource Management Plan: Is there general agreement & acceptance of the current status and progress of the Big Data Analytics project?
- Procurement Audit: Are the supporting documents for payments voided or cancelled following payment?
- Source Selection Criteria: What documentation is necessary regarding electronic communications?
- Schedule Management Plan: Has process improvement efforts been completed before requirements efforts begin?
- Executing Process Group: How well did the chosen processes produce the expected results?
Step-by-step and complete Big Data Analytics Project Management Forms and Templates including check box criteria and templates.
1.0 Initiating Process Group:
- 1.1 Big Data Analytics project Charter
- 1.2 Stakeholder Register
- 1.3 Stakeholder Analysis Matrix
2.0 Planning Process Group:
- 2.1 Big Data Analytics project Management Plan
- 2.2 Scope Management Plan
- 2.3 Requirements Management Plan
- 2.4 Requirements Documentation
- 2.5 Requirements Traceability Matrix
- 2.6 Big Data Analytics 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 Big Data Analytics 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 Big Data Analytics 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 Big Data Analytics project or Phase Close-Out
- 5.4 Lessons Learned
With this Three Step process you will have all the tools you need for any Big Data Analytics project with this in-depth Big Data Analytics Toolkit.
In using the Toolkit you will be better able to:
- Diagnose Big Data Analytics 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 Big Data Analytics 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 Big Data Analytics investments work better.
This Big Data Analytics 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.