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Thanks for being part of this Data Architecture Blog and Community.  In this post I want to discuss the importance of building a Data Community in your organization and how Azure Data Services can help nurture that Data Community and drive solution adoption and success.  

 

How do you convince someone in an organization to be engaged in a Data Community and contribute to the many assets required to meet an organizations vision for Data and Analytics Solutions?   I love the term Tom Sawyering.  Not the deceitful part of getting someone to do my work (I believe in doing my own ironing, painting, data wrangling, upskilling, etc.), but the recognition that to be able to paint a fence at scale you need more than one painter.  I know this firsthand, as I have been recruiting my wife to help paint our house this summer.

 

Wikipedia definition of Tom Sawyering someone https://en.wiktionary.org/wiki/Tom_Sawyer

"To convince someone to volunteer, especially to do something which one should do oneself."

 

To compound this scaling issue, organizations have many projects and initiatives so there is more than one fence to paint.  To get the job done you are going to need resources from outside your immediate team or department.  So how do you attract people to author content for Data and Analytics Solutions?  More importantly how do you foster contributors that don’t end up disillusioned and feeling like they have been Tom Sawyered.  Azure Data Services can help with faster time to value, and the innovation and agility focus of these solutions is going to attract a Data Community.  How do you upskill the Data Community?  The Microsoft global skilling initiative can help with that.  Members of the Data Community also need good business problems to solve to provide the necessary focus and opportunity to build proficiency and upskill to meet the challenge.  In this time of Covid-19, necessity truly is the mother of invention so you need to be innovative in your approach in building your Data Community - upskill the organization with both students entering the workforce, and by reskilling the existing workforce.

 

To get started whether a green field initiative (not super common), or by more commonly extending, expanding, or modernizing an existing Data and Analytics Solution you need resources - people (labor), and infrastructure (buildings, electricity, tools, hardware, and software, or cloud platforms like Azure).  To be successful with people resources for a solution you need to have customers/users who are both authors and consumers of data and content.  To be successful with infrastructure resources you need technology, tools, hardware, and software that are flexible, agile, and elastic because of business volatility and changing demand.  Azure can help achieve this economic benefit by providing elastic infrastructure and reducing sunk costs in building, power, and servers.  Azure can also help achieve a financial benefit by improving cash flows and creating a variable vs fixed cost structure.  In my opinion the most important is the agility benefit that comes with IT and Business Transformation and faster time to value.  This innovation approach vs the cost savings provided by the economic and financial benefit is what brings the extended resources to the Data and Analytics Solution.  This business opportunity and innovation is what attracts people to the solution, but the cost saving can also be opportunities to reinvested to help with resourcing more people for required roles for the solution. 

 

If you are moving an existing data solution from on premises to Azure this cost saving may be delayed as you pay to keep the production on premises solution going until you compete migration.  So, for a time you pay to keep the as-is data solution running while you build the new to-be Data and Analytics Solution – kind of like a double mortgage.  The size of and adoption by the Data Community is going to be a big part of the justification for continued investment in the solution so you best get them onboard early – early involvement also builds ongoing commitment and ownership.  The following diagram depicts how Azure Data Services can extend an organizations infrastructure, and how the Data Community can extend the workforce required for creation, ongoing maintenance, and growth of Data and Analytics Solutions.

 

extendDataSolutionResources.PNG

 

How does Azure make this possible?  This diagram illustrates how the cloud vendor manages more of the infrastructure and how that can provide a faster time to value, standardization, and lower cost.

 

cloudplatformstack.png

Demystifying Service Offerings from Microsoft Azure, Amazon AWS, and Google Cloud Platform

By Charlie Crocker

https://www.kdnuggets.com/2019/06/understanding-cloud-data-services.html

 

Instead of just lift and shifting on premise data solutions to IaaS, bridging more workloads to PaaS Data Services on Azure and SaaS BI Services like Power BI can also come with people resource savings and enable existing resources to focus on activities higher up the stack.

 

cloudRolesRequired.png

Demystifying Service Offerings from Microsoft Azure, Amazon AWS, and Google Cloud Platform

By Charlie Crocker

https://www.kdnuggets.com/2019/06/understanding-cloud-data-services.html

 

While Azure Data Services provide infrastructure benefits Power BI provides self-service capabilities that can help anyone in the organization be more engaged with data.  Users can easily collaborate with others to Tom Sawyer some additional volunteers and build a data-driven culture and Data Community.  Azure and Power BI also provide differentiated capabilities for users in the Data Community who have a diversity of needs.  Examples by User Role:

 

Data Analyst – Power BI, Notebooks (Jupyter in Azure ML, Databricks, Synapse Analytics)

Data Scientist – Notebooks (Jupyter in Azure ML, Databricks, Synapse Analytics) and ML models deployed to the Kubernetes service AKS

Data Engineer – Azure Data Factory, Databrick Notebooks, Synapse Notebooks

DBA – Azure SQL Database, PostgreSQL, MySQL, Synapse as well as Database Management tools like SQL Server Management Studio (SSMS) and Azure Data Studio.

Developer/Software Engineers – Relational Databases like those mentioned for DBA role, NoSQL Databases like CosmosDB as well as Integrated Development Environments and Code Editors

 

Just like painters of trim, main walls, lower story, second story, and chimney (35 ft up) on a house are going to need tools like different sized paint brushes and ladders, paint, buckets.  A Data Community needs different tools.

 

Data and Analytics Solutions require a Data Community and Azure Data Services to successfully solve business problems!  Put hands on keyboard and do the work.  Do the work and learn the Skills.  Just like Mr. Miyagi in Karate Kid used Paint the Fence to upskill Daniel https://www.youtube.com/watch?v=R37pbIySnjg

 

Enjoy the rest of your summer!

 

Darwin

2 Comments
Microsoft

Great post! Tks for sharing @Darwin Schweitzer !!! We also have the AI Engineer, one of the most popular certifications we have today. Tks!

Rodrigo,  That for adding the AI Engineer.  I should have included that with Azure ML, Azure DevOps, Cognitive Services, and Azure Databricks,  as well as Integrated Development Environments and Code Editors as tools for the AI Engineer.

 

Regards,

 

Darwin