Voice Shopping in Vehicles

          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.

           At CES 2018, voice technology proved it will be integrating even further into our lives. Car manufacturers announced new voice assistant features. Google and Amazon are battling it out to introduce their voice assistants into our cars; which means the days of tapping a screen to check directions or look up info while driving will be long gone. This is unsurprising, as over 32 million people drive cars in the UK, meaning they’ll be reaching a wide audience. Instead, drivers will speak and their car will speak back.

         Fifty percent of all searches will be voice searches by 2020, according to comScore. As we become accustomed to the ease of speaking, rather than typing, consumers will change the way they interact with their surroundings. It’s no wonder big names already want in; this is something that will really change our journeys.

DataOps

             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.