Key Points

References

Key Concepts

6R strategy for Cloud Transformation and Migration Projects

https://www.lucidchart.com/blog/cloud-migration-strategies-the-6-rs-of-cloud-migration

r cloud migration strategy should include these basic steps:

1. Plan the migration. You need to understand your current environment, why you are migrating to the cloud, determine your cloud server requirements, security requirements, and so on.
2. Choose a cloud environment. Choose the cloud environment you want to use: public cloud, hybrid cloud, private cloud, or multi cloud.
3. Migrate your apps and data. If your plan is effective, moving apps and data to the cloud should go smoothly.
4. Validate the migration. Your cloud migration isn’t successful just because all the data was transferred. To be a success, you have to analyze everything and make sure that everything works as expected.

6R strategy is a framework developed by AWS to help organizations classify applications for cloud migration. The 6R strategies are: 

1. Rehost
   Also known as "lift and shift", this strategy involves moving applications from on-premise to the cloud.
2. Replatform
   A modified version of rehosting, this strategy is also known as "lift, tinker, and shift".
3. Rearchitect
   This strategy involves rewriting applications from scratch to make them cloud-native. This can include breaking down applications into smaller components, such as microservices, and wrapping them into containers for deployment
4. Repurchase
   Also known as "drop and shop", this strategy involves replacing on-premise applications with cloud-native software from a vendor
5. Retire
   This strategy involves removing applications that are no longer needed or productive for the IT portfolio
6. Retain
   If migrating to the cloud doesn’t make sense for your company at this time, you can retain your current environment and revisit a move to the cloud later. For example, to be in compliance, you can’t move data at this time. Or, some of your applications might be too difficult to migrate and you want to keep them until you can come up with a viable and cost-effective alternative.

Raj Grover on Data Transformation Project Questions to Ask linkedin

50 Essential Questions for Leaders to Contemplate Before Embarking on hashtag#DataTransformation

Business Goals and Alignment

1.   What are our top 3-5 business goals for the next 2-3 years? How can data transformation specifically help us achieve these goals?
2.   Are there specific challenges or opportunities we aim to address with data?
3.   How will a hashtag#datadriven approach improve our decision-making processes?
4.   Are our current data practices hindering our ability to achieve these goals?
5.   How will data transformation initiatives be aligned with overall hashtag#businessstrategy?
6.   Are all key stakeholders on board with the goals and potential impact? Do we have executive buy-in and support for data transformation efforts?
7.   How will this initiative impact our current business processes & decision-making practices?

Data Strategy & Data Governance

hashtag#DataStrategy and hashtag#DataGovernance

8.   What is our current state of hashtag#datamaturity?
9.   What type of data is most critical for our success, and where does it reside currently?
10. What are our biggest hashtag#dataquality challenges? How will we manage and maintain data quality throughout the transformation process?
11. What are the ethical considerations surrounding data collection, usage, and storage?
12. Do we have a clear data governance framework in place?
13. Who will be responsible for data ownership, access, and security?
14. How will we ensure hashtag#dataprivacy and compliance with regulations?

Technology and Tools

15. What hashtag#datamanagement tools and technologies do we need to invest in?
16. How will we integrate new data systems with existing legacy infrastructure?
17. Do we have the necessary IT resources and expertise to support data transformation?
18. What is our hashtag#datasecurity strategy to protect sensitive information?
19. How will we ensure data accessibility for all authorized users across the organization?
20. What is our budget for data transformation, and how will it be allocated?
21. What is the timeline for implementation, and what are the key milestones to achieve?
22. How will we measure the success of our data transformation efforts?

Culture and Change Management

23. What is our current hashtag#dataliteracy level across the organization?
24. How will we foster a data-driven culture within the organization?
25. What training and support will be needed to equip employees with data literacy skills?
26. How will we address potential resistance to change and encourage data adoption?
27. How will we measure the success of our data transformation efforts?
28. What communication strategy will be used to keep stakeholders informed throughout the process?

People and Skills

29   Do we have the necessary hashtag#datascience and hashtag#analytics talent in-house?
30   What skills gaps need to be addressed to support data transformation?
31.   How will we attract and retain top hashtag#datatalent?
32.   How will we create a collaborative environment where hashtag#datainsights are shared openly?
33.   How will we incentivize hashtag#datadriven decision-making across all levels of the organization?

Data Sources and Integration

34.   What are our primary sources of internal and external data?
35.   How will we ensure the quality and consistency of data across different sources?
36.   How will we integrate data from various sources into a unified platform?
37.   What hashtag#datalakes or hashtag#datawarehouses will be needed to store and manage data?
38. How will we continuously monitor and improve the quality and completeness of our data?

Challenges and Risks

39.   What are the potential risks and challenges associated with hashtag#datatransformation?
40.   How will we address hashtag#datasecurity concerns and ensure compliance with regulations?
41.   What is our plan for managing resistance to change within the organization?
42.   How will we handle potential data silos and ensure data accessibility across departments?
43.   What is our backup and disaster recovery plan for data security?

Future-proofing

44.   How will this data transformation strategy lay the foundation for future hashtag#innovation and growth?
45.   How can we ensure our hashtag#dataarchitecture is scalable and adaptable to evolving business needs?
46.   How can we ensure our data transformation strategy is adaptable to evolving technologies and business needs?
47.   How will we stay informed about emerging trends in hashtag#dataanalytics and best practices?
48. What is our plan for continuous improvement and optimization of our data infrastructure?
49. How will we integrate artificial intelligence and machine learning into our hashtag#datastrategy?
50. What are the long-term benefits and competitive advantages we expect from data transformation?

Before you embark on your data transformation journey, feel free to reach out to us at Transform Partner. We are here to dispel any uncertainties and assist you in formulating a clear vision, as well as strategic planning for your data centric endeavors.

Image Source: Data Strategy, Dallas

dbx-data-project-mgt-analytics-HighPerformance-AI-MIT.pdf.  link

1 Executive summary..................................................................4
02 Growth and complexity ..........................................................6
Databricks perspective: The rise of the lakehouse effect.............. 7
03 Aligning and delivering on strategy .....................................9
Data high-achievers.........................................................................................11
Nielsen: data transformation for a data-reliant business..............13
04 Scaling analytics and machine learning ...........................14
A paradigm shift at CVS Health..................................................................15
Barriers to scale...............................................................................................16
Protecting return on investment................................................................17
Technology, democracy, and culture......................................................18
05 Visions of the future ..............................................................19
A CDO wish-list for a new architecture.................................................19
06 Conclusion................................

2024-07-eb-big-book-of-data-engineering-3rd-edition.pdf

Guidance and Best Practices
Databricks Assistant Tips and Tricks for Data Engineers
Applying Software Development and DevOps Best Practices to Delta Live Table Pipelines
Unity Catalog Governance in Action: Monitoring, Reporting and Lineage
Scalable Spark Structured Streaming for REST API Destinations
A Data Engineer’s Guide to Optimized Streaming With Protobuf and Delta Live Tables
Design Patterns for Batch Processing in Financial Services
How to Set Up Your First Federated Lakehouse
Orchestrating Data Analytics With Databricks Workflows
Schema Management and Drift Scenarios via Databricks Auto Loader
From Idea to Code: Building With the Databricks SDK for Python

Key Data Strategies in 2024

Organizations’ top data priorities over the next two years fall into three areas, all supported by wider adoption of cloud platforms: improving data management, enhancing data analytics and ML, and expanding the use of all types of enterprise data, including streaming and unstructured data.

<< should add <<. automated, reactive smart data, self governing data, automated metadata mgt, automated data discovery & registries

Cloud, once considered an optional technology environment, is today the foundation for modernizing data management: 63% of respondents use cloud services or infrastructure widely in their data architecture.

<< should add << cloud neutral strategies including open-source should be first choices for enterprise data mgt

Data Transformation Journey - Data to Value

Image Source: Microsoft

Data Mesh Tranformation Concept - Raj G

Early data mesh adoptions have mostly been derivatives of existing hashtag#datamanagement infrastructure, rather than truly decentralized and domain-oriented data management designs. Most current data mesh architectures don’t fulfill the promise of a truly hashtag#selfService data management infrastructure, nor do they practically answer concerns related to hashtag#federatedGovernance.

Practical Approach:

1.    Embrace true decentralization

2.    Implement a self-service platform

3.    Embrace a domain oriented approach

4.    Establish federated governance framework

5.    Invest in hashtag#interoperability

6.    Foster a hashtag#dataAsAProduct mindset

7.    Invest in data mesh infrastructure

8.    Iterative implementation

9.    Cultural and organizational change

10. Continuous education and support

This solution aims to move beyond simply retrofitting existing infrastructure and towards a truly decentralized, domain-oriented data management design. It addresses self-service capabilities and federated governance concerns while maintaining the core principles of data mesh.

image: CGI 

The Transformation Partner Services Marketing Pitch

To receive more details and similar solutions, best practices, tools and techniques in data and digital transformation, subscribe to our Premium Content. You can either DM on LinkedIn or email: info@transformpartner.com for subscription details.

Transform Partner is your trusted navigator in the turbulent seas of data and digital transformation. In an era where businesses are inundated with information yet starved for hashtag#insights, we stand as a beacon of clarity and hashtag#innovation. Our consultancy brings together a team of seasoned experts with deep industry knowledge spanning government, banking and finance, healthcare, retail, and manufacturing sectors.

We don't just offer generic solutions; we craft bespoke strategies that align with your organization's DNA. Our approach begins with a comprehensive assessment of your current digital landscape, identifying bottlenecks, untapped opportunities, and potential risks. We then leverage cutting-edge hashtag#analytics and predictive modeling to forecast industry trends and market shifts, ensuring your transformation journey is future-proof.

When you partner with us, you're not just getting a roadmap; you're gaining a co-pilot for your digital journey. We provide hands-on support at every stage, from strategic planning and change management to technical implementation and post-launch optimization. Our agile methodology ensures that your transformation initiatives can pivot swiftly in response to market changes or new opportunities.

Let's collaborate to turn your digital vision into reality. With Transform Partner, you're not just keeping pace with the digital revolution – you're leading it. Reach out today to schedule a personalized consultation and take the first step towards a transformative future.

See STH ISR and STP processes for reference value propositions

Data Solution Lessons Learned - Rag G

25 hashtag#DataManagement Lessons Based on Common Industry Challenges and Best Practices:

1. Lesson from a hashtag#Retailer: Customer hashtag#DataAccuracy is Crucial
2. Lesson from a hashtag#Healthcare Provider: Strict Adherence to hashtag#DataPrivacy Regulations
3. Lesson from a Financial Institution: Importance of Timely Reporting
4. Lesson from an E-commerce Platform: Personalized Recommendations Depend on Quality Data
5. Lesson from a hashtag#Manufacturing Company: hashtag#IoT Data Requires Scalable Solutions
6. Lesson from a Tech Company: Agile Data Management Supports Rapid Development
7. Lesson from a Government Agency: hashtag#DataSharing Enhances Public Services
8. Lesson from a Marketing Firm: Attribution Challenges Demand hashtag#DataIntegration
9. Lesson from a Transportation Company: Real-time Data is Essential for Operations
10. Lesson from an Educational Institution: Student hashtag#DataSecurity is a Priority
11. Lesson from a hashtag#Telecom Company: hashtag#DataUsage Patterns Drive Service Improvements
12. Lesson from a Hospitality Industry: Personalized Customer Experiences Require Comprehensive Guest Profiles
13. Lesson from a Utility Provider: Predictive Maintenance Relies on Historical Data
14. Lesson from a Social Media Platform: hashtag#DataMonetization Requires Ethical Practices
15. Lesson from a Research Institution: Collaborative Data Management Enhances Discoverability
16. Lesson from a Logistics Company: Supply Chain Visibility Requires Real-time Data
17. Lesson from a Start-up: Scalability is Critical from the Beginning
18. Lesson from a Nonprofit: hashtag#DatadrivenDecisionmaking Improves Impact
19. Lesson from a hashtag#SaaS Provider: Regularly Audit Data Access and Permissions
20. Lesson from a Legal Firm: Document Management Streamlines Legal Processes
21. Lesson from an Insurance Company: Data Integration Simplifies Claims Processing
22. Lesson from a Pharmaceutical Company: Data Accuracy is Crucial for Regulatory Compliance
23. Lesson from a Real Estate Developer: Location Data Enhances Property Valuation
24. Lesson from a Gaming Industry: Player hashtag#Analytics Drive Game Development
25. Lesson from a Consulting Firm: hashtag#DataGovernance is a Continuous Process

These lessons highlight the diverse challenges and best practices in data management drawn from various industries and real-world scenarios.


Image Source: Eckerson Group

hashtag#TransformPartner – Your hashtag#DigitalTransformation Consultancy

Jim Mason - More Keys for Data & Digital Transformation Projects

VCE model

Data Use Cases in Scope

ISR with SGAAPS

BEP >. VCRS, FACTUR3DT.io

Data Maps, Catalogs

Data Providers

Data Consumers

Data Sources and Dependencies

Data Roles & Authorizations

Data Usage History

DQA - Data Quality Assessment ( for Purpose )

RAS ( NFRs ) for Data 

Data Services Catalogs

Data Services Life Cycle

Data Governance by Type, Role

Data Trusts for use cases

Potential Value Opportunities

Potential Challenges

Candidate Solutions

Step-by-step guide for Example

sample code block

Recommended Next Steps

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