Data Operations (DataOps) is rapidly
emerging as a discipline for organizations that continue to struggle with the
management of data as a shared business asset. DataOps brings a set of
data engineering principles which borrow from the DevOps software development
movement. The intent is to deliver “rapid, comprehensive,
and curated data” to business analysts and decision-makers.
The dataOps is a new approach to the end-to-end data
lifecycle, which applies new processes and methodologies to data analytics.We expect 2019
to be a breakthrough year for DataOpsapproaches as
firms strive to derive value quickly and efficiently from their data assets. We
also believe that companies will increasing use machine learning to integrate
and improve their data environments, as we described in a post about GlaxoSmithKline.
Agile software development helps deliver new analytics faster and with
higher quality. DevOps automates the deployment of new analytics and
data. Statistical process control, used in lean manufacturing, tests and monitors
the quality of data flowing through the data-analytics pipeline. Growing enterprise interest in DataOps has spawned a
robust ecosystem of vendors. To date, over $50M has been invested in companies
who market a wide array of DataOps product and services.
Voice shopping in vehicles may arrive. I predict that we’ll be able to use our voices to shop during our commutes, thanks to the seamless integration of voice interfaces in cars. We could see billboards prominently using voice shopping calls to action. Amazon released its Alexa Auto Software Development Kit in August, which allows you to summon Alexa for navigation, controlling entertainment options, building a grocery list, etc. Very soon, I think we can expect voice shopping in vehicles because it’s really as simple as retailers adding an Alexa skill for voice shopping.
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