Welcome

Passionately curious about Data, Databases and Systems Complexity. Data is ubiquitous, the database universe is dichotomous (structured and unstructured), expanding and complex. Find my Database Research at SQLToolkit.co.uk . Microsoft Data Platform MVP

"The important thing is not to stop questioning. Curiosity has its own reason for existing" Einstein



Tuesday, 14 September 2021

Data Governance Podcast

 

It was amazing to take part in my first podcast about the benefits of Data Governance and how to get started in Coeo Conversations with Justin Langford.  

The data field is such an exciting place to be at the moment. Data Governance is more than just compliance, it is about managing the whole ecosystem. I thought I would run out of things to say talking for 30 minutes in the podcast on governance, however that was not the case. I hope you find the podcast interesting, informative and fun.

Looking forward there is an exciting digital event coming up Maximize the Value of Your Data in the Cloud: Achieve unified data governance with Azure Purview . The event is Tuesday, September 28, 2021 | 9:00 AM-10:00 AM Pacific Time (UTC-7) Register here

Monday, 13 September 2021

Data Platform Virtual Summit 2021 Keynote


The Data Platform Summit has started. An excellent session covering the Azure data stack identifying when to use each tool and the key innovations with SQL. It was nice to see an explanation of when to use what tool. With so many tools now it becomes hard to know which is the best choice.  This useful chart was shown.



Azure SQL Edge



SQL Server 2019

Solves the modern data challenges 
  • Data visualization and big data clusters
  • Modern platforms with compatibility
  • Built-in machine learning and extensibility
  • Intelligent performance
  • Layers of security and complain
  • Business critical availability

SQL Linux/Container

Containers are portable and can run anywhere containers are supported. They are lightweight with reduced disk, CPU and memory footprint. They have a consistent image of SQL Server, scripts and tools and are efficient with faster deployment, no patching required and less downtime. 

Azure SQL

SQL Server vs. Azure SQL PasS are 
  • business continuity, high availability, automated backups, long term backup retention , geo-replication
  • scale, advanced security, version-less, built in monitoring and built-in intelligence. 

There are Azure SQL Editions for general purpose, business critical and hyperscale



Azure Arc
Bring Azure data services to on-premises, multi cloud and edge with Azure Arc. Azure Arc enabled SQL Managed Instance has many advantages.




The value of the Cloud provides additional tools such as

  • Azure Defender to protect your data
  • Azure SQL Database Ledger for blockchain
  • Telemetry across all your assets with Azure Monitor SQL Insights

Then to help with migration there are tools available

  • Azure Migrate to discover and assess your SQL Server assets
  • Migrate Inline with Azure Data Studio
  • Migrate online with Azure Database Migration Service or Log Replay Service

A few tools were not mentioned that form part of the data suite such as Azure Cosmos DB. The expansion of tools and options has grown significantly over the last few years so it is always good to assess what business objective you are trying to achieve and select the right tool.
 

Friday, 3 September 2021

Data Strategy: where are we and what is the answer to the ultimate question

Originally published here

Do we know where we are going? Have we asked the right questions? Without a roadmap, we will not arrive at our destination. The first step relies on discovering where we are, what we need to be successful and where we need to go. We need to create a roadmap to enable a path forward. With that roadmap, there is a need to assign owners of tasks throughout the data journey. Data Strategy is a top-down approach closely aligned with business strategy.

Gartner define a 'Data Strategy' as a highly dynamic process employed to support the acquisition, organization, analysis, and delivery of data in support of business objectives. Whereas DAMA defines Data Management as The development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.

Many organisations do not have data strategies in place, although they may be working on areas that would sit under that umbrella. Deciding on what the core data principles are, can help an organisation quickly adapt to the data-centric culture.


As an example a set of data principles could be: 

  • All data is owned, managed, secured, and governed
  • Data is managed throughout its lifecycle
  • Data is available and visible whenever needed
  • Information is an asset
  • Use a data catalogue for visibility
  • Data is fit for purpose and meets the business need
  • There is a single version of the truth
  • Data skills training for people to use data effectively
  • Data ethical standards are followed

 

Before any type of strategy is created a business key stakeholder must champion the idea and a person identified to own the strategy, such as the CDO. The data strategy should be maintained and enacted through the data governance team and other working groups.

Data strategy is a framework that is built around the data to amalgamate the assets to create a source of trusted data to allow process efficiencies, increase confidence in the data and create opportunities for innovation. The Data Management strategy could be aligned with the Data Management Association and the DAMA Body of Knowledge (DMBOK), to enable consistent practices and verifiable decision making.

It is important to have an agile data strategy, thus creating a short-term strategy, so the immediate benefit can be gained by the business. Then working on a longer-term target strategy, once the gap analysis is complete and strategic imperatives are identified.​ A couple of core areas to also review are data governance, data ethics alongside data culture and data skills. The technology side for data collection, data storage, data processing and data output may need updating, but if a need exists for technological change, it will be due to the alignment of business and data strategies and identification throughout the process. To enable that agile approach to data strategy using a Boston matrix with the MoSCoW prioritization technique is very successful.

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DAMA lists deliverables from strategic planning as:

  • Data Management Charter (Vision, business objectives, guiding principles. success measures, risks, operating mode. A business plan to use the information to create competitive advantage and to support enterprise goals).
  • Data management scope statement (goals and objective for planning, organisation roles, responsibilities clarified)
  • Data Management Implementations Roadmap (programs, projects and tasks, road map and milestones). Requires a data management program strategy a plan for maintaining and improving the quality data integrity access and security and mitigating risks.

Taking all of this into account using systems thinking to gain that holistic view there are three areas that should be covered for success: business data strategy, IT data strategy and operational data strategy.

If you haven’t started creating a data strategy or already have one, it is worth reviewing the current state to ensure an agile actionable plan is in place for continuous improvement.