Data Mesh Dynamics & Quality Assessment Metrics
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Chapter 1: Introduction to May 2022
Greetings, everyone! As we step into May 2022, April has been a whirlwind of activity for me, filled with travels and numerous face-to-face meetings with clients. This month promises to be just as busy, featuring international trips like the Gartner Data Analytics Summit in London and various engagements across Europe. I’ll also be presenting at SADA's Ground School, The Google Cloud Partner Summit, and Informatica World in Las Vegas, all while celebrating my birthday! Plus, I’m eagerly awaiting the release of Top Gun 2 in the US on May 27th! How's your May shaping up? Meanwhile, let’s dive into the top five stories of the week! Don't forget to subscribe!
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Section 1.1: Tech Insights on Data Mesh
What distinguishes Data Mesh from current technologies? Zhamak Dehghani and David Vellante clarify this in a session on SiliconANGLE & theCUBE. Although I couldn't comment directly on the YouTube channel, Zhamak effectively emphasized that her insights on Data Mesh should not be misconstrued as a validation for vendors to promote their solutions as the ultimate answer. Earlier in January, I addressed this in a piece for VentureBeat, identifying the supporters, skeptics, and misrepresentations surrounding Data Mesh.
Section 1.2: The Impact of Data-centric AI on Management
A noteworthy article by Jessica Davis from InformationWeek, featuring insights from Rita Sallam and Ted Friedman, sheds light on how data-centric AI is set to revolutionize data management. This concept is one of several highlighted in Gartner's 2022 report. It connects to another significant trend—metadata-driven data fabric—furthering the disruption in data management for AI applications.
Key takeaways include:
- Data fabric is designed to actively listen, learn, and take action based on metadata, applying ongoing analytics to both existing and newly discovered metadata assets.
- Data Sharing: Gartner indicates that the trend of "always sharing data" positions data sharing as a crucial business performance metric.
- Governance: Friedman suggests that discussions should not only revolve around data governance but also encompass data analytics governance, integrating artifacts related to AI and its functionalities.
For more details, check out the full article.
Chapter 2: Featured Customer of the Week
This week, I am excited to award my Customer of the Week to Ksenia Khlyustina from Flyr! In this insightful video, Ksenia explains how Flyr employs machine learning to predict demand, establish pricing strategies, and enhance revenue. A must-watch interview conducted by Mikhail! More details can be found here.
Section 2.1: Measuring Data Quality
An essential read by Borna Almasi outlines how to assess data quality with 13 key metrics that you might be overlooking. Some highlights include:
- According to Gartner, 40% of business initiatives fail to meet their expected outcomes due to inadequate data quality.
- There are four subjective data quality metrics: Believability, Usability, Objectivity, and Interpretability.
- Among the nine objective metrics for data quality, four can be largely automated: Completeness, Integrity, Precision, and Accessibility.
For further insights, click here.
Section 2.2: Exploring Data Mesh Operability Patterns
Eric Broda is back with more insights! Find out more here.