Points of View

Crack your cloud-data integration conundrum with co-innovation

Apr 1, 2020 Reetika Fleming

Cloud data ingestion is the bane of every company trying to run large-scale analytics and ML programs. Every global business wants to have data-driven analytics built into the core of their organization. The intent is very clear: our research has found that 85% percent of enterprises view analytics as one of the top three strategic imperatives for market success, and a strong 81% have executive leadership teams aligned on an analytics strategy. Many have started down the path and invested in tools and platforms specifically for analytics, machine learning, and business intelligence. Parallel investments in foundational technology modernization also have a major impact, including cloud data migration, data lake creation, and core systems modernization.

 

Beneath the hood, however, not having quality data is a much larger underlying problem that can stall the most ambitious of analytics programs. The cloud is the way to go for data storage today, especially to run machine learning models that rely on large datasets. Data migration to the cloud is easier said than done, as every CIO has experienced over the last decade. In fact, as a recent HFS study on smart analytics found, data assimilation remains the biggest pain point across the entire data-to-analytics lifecycle.

 

Data assimilation—collecting, cleansing, and sorting data and making it available in the cloud—for analytics and ML is the least mature function in enterprises today. This holds true for banking and financial services organizations as well, with only 12% of decision-makers rating their organization’s data assimilation as mature. And yet, it is the most crucial and fundamental step for organizations on their way to becoming more analytically driven. Developing this capability will require organizations to make focused investments and undertake continual change management with the help of expert partners.

 

In this document, HFS outlines the case example of Infosys and its banking client, which have done just that. The two companies have co-developed a product, Juniper, to solve cloud data ingestion and management once and for all—for the bank and, potentially, the rest of the banking industry, through an open-source offering in the works.


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