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Scoop.it!
An agile approach to overhauling data architecture improves speed, flexibility, and innovation.
Farid Mheir's insight:
WHY IT MATTERS: I agree with those 6 recommendations. 
Scoop.it!
Few organizations take full advantage of data generated outside their walls. A well-structured plan for using external data can provide a competitive edge.
Farid Mheir's insight:
WHY IT MATTERS: after you're done enabling analysis of your internal data - from eCommerce, ERP, etc. - you should look at external data - weather, demographics, etc. - to provide context and generate useful insights.
Scoop.it!
To succeed with data and analytics initiatives, enterprises need a comprehensive view of critical technology capabilities. We believe these Hype Cycles draw a holistic view of technologies — including the new addition of lakehouse architecture as a trigger, which can make data and analytics programs modern, broadly applied, richer in functionality and successful.
Farid Mheir's insight:
WHY IT MATTERS: insights to remain buzzword compliant in the world of data management ;-)
Scoop.it!
At first glance, the data fabric and the data mesh concepts sound quite similar. Meshes are often made from a type of fabric, after all, and they are both malleable items that can be lain atop things–in this case, your IT systems that are subject to the ever-growing data crush. But there are fundamental differences to these two approaches, so it’s worth taking some time to learn their differences.
Farid Mheir's insight:
WHY IT MATTERS: a great overview of 2 popular data architectures.
Scoop.it!
An agile approach to overhauling data architecture improves speed, flexibility, and innovation.
Farid Mheir's insight:
WHY IT MATTERS: data is the fuel for digital transformation. Changing the data architecture of organizations to support real-time quality data requires a number of fundamental changes that often requires changes in infrastructure, legacy systems and more importantly, governance and user behavior.
campbell's curator insight,
July 18, 2020 8:22 AM
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Scoop.it!
Expert data scientists and other professionals working in data science roles require capabilities to source data, build models and operationalize machine learning insights. Significant vendor growth, product development and myriad competing visions reflect a healthy market that is maturing rapidly.
Farid Mheir's insight:
WHY IT MATTERS: machine learning is all about data and data analysis so no surprise that data analysis tools are exploding these days. The quadrant is interesting to ID the big players but jump to the end of the report for a market analysis that is worth the read.
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Curated by Farid Mheir
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