Data sharing predictions for 2022
|Freeman Lightner in Programming Friday, January 14, 2022|
Barr Moses, CEO, and Founder of Monte Carlo gives us her data sharing predictions, what data teams will look like in 2022, how data discovery has emerged, why codeless reporting tools will take the data analytics world by storm, and that data engineers will start treating data like a product.
Barr Moses, the CEO, and Founder of Monte Carlo worked with Fortune 500 companies as VP of Customer Operations at Gainsight to help teams use data as a competitive advantage. Lior Gavish, Barr’s co-founder, was formerly SVP of Engineering at Barracuda where he built fraud detection systems powered by data, ML, and analytics. Moses shares her 2022 predictions with us about data sharing, data discovery, codeless reporting tools, why data analysts and engineers will treat data like products, and much more.
Codeless Reporting Tools, Data Teams, and Data sharing predictions for 2022
“Data sharing, in other words, the ability to distribute data sets between multiple domains or applications in a compliant and reliable way, will become critical to our ability to innovate quickly and drive impact with data. Companies that invest in data sharing will be able to move faster and more nimbly when cross-organizational demands for data increase.”
“For many data teams, data discovery has emerged as a must-have layer of the data stack, but few solutions are stepping up to the plate in an end-to-end and holistic way. Traditionally, data governance solutions have relied on manual input to map lineage and build documentation for critical data assets, something that should (and can!) be automated with the right technologies, like data observability and augmented catalogs.”
“Codeless reporting tools and predictive analytics will take the data analytics world by storm. Companies like ThoughtSpot, Sisu Data, and Canvas are making it more accessible and easier than ever for less-SQL savvy analysts to work with data, while simultaneously freeing data scientists and engineers up from routine ad-hoc requests and dashboard maintenance.”
“The onus on data analysts and engineers to treat data like a product, in other words, embue dashboards, data platforms, and self-service data workflows with the same diligence as we treat SaaS products. This boils down to ensuring that that data and associated data products are securely administered, accessible to the right individuals, trustworthy, and scalable across different domains. Data leaders who figure out how to scale this mindset while keeping data debt at bay will be the real winners.”
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