Data Connectivity 2022

whitePaper | January 1, 2020

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whitePaper | June 16, 2022

It seems like the business world has gone crazy for machine learning (ML) and artificial intelligence (AI), viewing the technologies as the essential keys to the very future of the enterprise. In fact, a recent survey from ESIThoughtLab found that two-thirds of business leaders see AI as critically important for their future. And yet, the respondents report that only 25% of AI projects are in widespread deployment and 40% of all projects are generating negative or no returns.

whitePaper | June 29, 2022

For (oh so many) years, we’ve been hearing about the “promise” of big data. Much of the buzz focused on getting people prepared for the onslaught of bigger data sets – and what IT needed to do to help the business make sense of this information.

whitePaper | May 27, 2021

In many organizations, we need more collaboration between businesses, analytic teams, application developers and IT operations. These teams often work with data in silos and end up duplicating efforts, failing to integrate, or missing opportunities to deliver value from data. In addition to siloed efforts, data scientists are also faced with ever-increasing volumes and speeds of data. And the reality is they’re expected to answer questions just as fast as - or faster than - before. It’s important that we’re using the right data and the right techniques to ensure optimal outcomes. Download this guide to learn more about SAS and open source.

whitePaper | November 10, 2022

In this hyper-competitive era, faster time-to-market plays a vital role in the success of an organization. Any lag accounts for the 'Cost of delay,' impacting the value a product could create otherwise. This is the primary reason teams are adopting an agile approach to software development, testing, and release. Unfortunately, teams that don't follow suit find it challenging to deal with this accelerated timeframe.

whitePaper | March 12, 2020

This point of view is an exploration of the possibilities engendered by rethinking the role of data scientists in the wake of industrial revolution. It might be claimed that current trends in industrial revolution reflect a paradigm shift towards data centric processing with data science playing an increasingly critical role. This point of view also explicitly highlights the potential role of Data scientists as an emerging phenomenon, and then to show some of the benefits that this role can bring as we move towards industrial disruption