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

Saturday 1 January 2022

Data as an asset

I just read an interesting article about the recipe for success handling data assets. It talks about data as an essential factor for business agility and that it enables competitive advantage. Data is an asset in its own right and organizations must change how data is viewed at a strategic level.  Gartner and Accenture talk about data as the essential focus and ingredient.   This assets become valuable once actionable insight can be derived. The article sets out 15 mantras for implementing data as an asset

  1. Define your Data Strategy with tangible measurable metrics linked to business outcomes with a data architecture blueprint and executable roadmap.
  2. Disrupt business models with AI
  3. Establish the right Data Culture and Architecture with accountability, data curation and data quality competency, frictionless trusted data supply with embedded data fluency across the business and a data taxonomy and dictionary.
  4. Implement DataOps to infuse life into your data with data acquisition and management connecting data creators with data consumers.
  5. Establish Tech Intensity initiatives for Data-Fluency enablement by setting baselines for data literacy skills resulting in data fluency
  6. Establish Data Signals and Patterns Repository
  7. Establish Data Marketplace – for Data sharing and sourcing across ecosystems reviewing the data supply chain and data monetization strategy
  8. Use AI and ML Algorithms
  9. Democratize Data – Secure Data Access and the correct type of BI and BI tools and make data visualization more transparent, intuitive and contextualised.
  10. Data Governance to produce trusted data with data lineage, managed data quality, business meta data and data profiling with risk and privacy policies for compliance.
  11. Establish Data Ethics principles covering things such as transparency, traceability and explainability
  12. Data Observability being the understanding of the health of the data in the system. The data observability pillars freshness and velocity, distribution, volume, schemes, lineage and data security & compliance.
  13. Define Data security and compliance controls
  14. Hire the right Data Engineering and AI talent
  15. Establish a Chief Data Officer and office of CDO

The article finishes stating Data Fluency and empowerment will be the determining success factors in a data-literate world.

No comments:

Post a Comment

Note: only a member of this blog may post a comment.