In this episode of CDO Battlescars, Sandeep Uttamchandani, Unravel Data’s CDO, speaks with Keyur Desai, CDO of TD Ameritrade. They discuss battlescars in two areas: Building a Data Strategy and Pervasive Self-Service Analytics Platforms. Keyur is a data executive with over 30 years of experience managing and monetizing data and analytics.
The amount of big data generated around the world by the time you finish this page is limitless. Think about it for a second. Companies everywhere will create an innumerable amount of data right now — customer records, sales orders, chain reports, emails, you name it. Companies need all this data for data analytics — the science of modeling raw data to uncover precious real-time insights about their business. It's like opening a treasure trove.
In 1969, my aunt graduated from university and joined IBM, the dominant player in the nascent tech industry at the time. She remained at “Big Blue” where she met and married my uncle, and rose up through the management ranks, until their joint semi-retirement exactly 30 years later. She recently told me, “the only way you could get fired in those days was to murder someone, embezzle or steal”.
The Cloudera Data Platform (CDP) represents a paradigm shift in modern data architecture by addressing all existing and future analytical needs. It builds on a foundation of technologies from CDH (Cloudera Data Hub) and HDP (Hortonworks Data Platform) technologies and delivers a holistic, integrated data platform from Edge to AI helping clients to accelerate complex data pipelines and democratize data assets.
Once again, Yellowfin has been recognized in the Gartner Magic Quadrant. This is the eighth time we have been recognized and the second year we are in the Visionary Quadrant. As per our knowledge, Yellowfin is also the only Australian vendor to be included.
In a 2020 study performed by Nature Research, 70 different teams of neuroimaging experts were asked to test nine hypotheses by looking at the same MRI data set. You may not be surprised to learn that these teams reached a wide range of different conclusions, in part because no two teams chose identical workflows to analyze the data. With seventy teams, there were 70 different workflows.
Part of being a data professional is pretty simple... you notice when things don't add up. In my case, my Apple Watch and my Peloton aren't on the same data page when it comes to calorie tracking. In this blog, I'm going to deduce why I think it's happening and use Qlik and the Peloton/Apple metrics as the data to support my conclusions.
In what feels like a lifetime ago now, way back in 2012, the Harvard Business Review called out the role of data scientist as the sexiest job of the 21st century. Almost a decade later, does that still ring true?
