In a previous article, we discussed the new enterprise data architecture spreading like wildfire among the data community “ Data Mesh architecture. We mentioned that it presents a paradigm shift in data architecture that sees the data industry follow suit by moving away from massive data teams prioritising centralised, monolithic data lakes and databases, to one that … [Read more...] about Is Data Mesh Right for Your Organisation?
data lake
“A Man of Principles” – Enterprise Architecture Blueprint for Utilities – Part 3
I am a Man of Principle I am a man of fixed and unbending principles, the first of which is to be flexible at all times “ a quote from former US Senator Everett McKinley Dirksen. Most probably he thought about politics in his role as senator, but the statement would equally suit coming from enterprise architect, too!? In part 1 “ The Good, the Bad and the Ugly of our … [Read more...] about “A Man of Principles” – Enterprise Architecture Blueprint for Utilities – Part 3
Data Science in Commodity Markets
Data Science in Commodity Markets At this conference you will identify the key purpose of data science application to ensure you end with tangible results while reducing time spent on data processing, and build trust in models. Commodity firms sit on vast amounts of data. This coupled with advancing technologies means that you can gain more and more value from this data. … [Read more...] about Data Science in Commodity Markets
Chief Data & Analytics Officer Exchange
The 2020 Exchange will provide Chief Data & Analytics Officers, Chief Data Officers, Chief Analytics Officers, and other leading executives with a three-day event on the forefront of capitalizing on data and analytics in the enterprise as the volume, availability and complexity of data continues to increase and evolve. Unlike other events, the CDAO Exchange offers data … [Read more...] about Chief Data & Analytics Officer Exchange
Data Mining and Engineering in Finance
This'marcus'evans'conference will drive initiatives to build an ecosystem of quality data for trade and risk, through embedding data quality efforts within the use of a central data repository, the application of synthetic and legacy data, the role and limitations of machine learning, and use of appropriate data visualisation techniques. As the volumes of data held by … [Read more...] about Data Mining and Engineering in Finance
