It's time again to share your predictions for 2017. I did my homework and came with these 10 predictions. I invite you to post your predictions in the comment section, or write a blog about it. Ramon Chen's predictions are posted here, while you can read Tableau's prediction here. Top programming languages for 2017 can be found here. Gil Press' top 10 hot data science technologies is also worth reading. For those interested, here were the predictions for 2016. Finally, MariaDB discusses the future of analytics and data warehousing in their Dec 20 webinar.
My Predictions
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Point 6 - Google Analytics
I do not really believe they have not recognized these issues - namely robot / bot artificial traffic. Filtering these are simply does not match their primary business goals: click -> paid visit = revenue. While site owners / Ad placers are not yelling (so the real human conversion is still acceptable) the bigger numbers generate bigger revenue.
OK; at the very end this caused by "people", but not human error type of behaviour...
Really interesting predictions Vincent - it does seem that the progress made in 2016 will see a shift in data handling and data integration technology in 2017, particularly towards the incorporation of data virtualization as noted by Gartner:
“As data integration architectures continue to shift from physical bulk/batch movement to virtualized and real-time granular data delivery, data and analytics leaders must intertwine integration styles to match all requirements of business changes.” (The State and Future of Data Integration: Optimizing Your Portfolio...)
I agree with Eric below in so far as the need to "shift from a technology focus to one of true organization transformation" - for that shift to be realized, business intelligence must to be accessible at an enterprise level for technical users and business users alike to be able to make informed decisions.
Data virtualization creates a virtual data layer, connecting to all data sources (of all types, formats and structures) and publishes this data making it accessible by all consuming applications. The full capabilities of DV have not yet been realized at an enterprise level - but the many issues companies will face in 2017 regarding big data analytics, data governance and organization transformation can be solved quite simply through data virtualization technology...watch this space.
2016 has been another year of heavy buzz and non-productive disruption -- even as Big Data slips into Gartner’s Hype Cycle’s “trough of disillusionment.”
In trying to sort out all of the new pressures and opportunities in the broad area of data science, I believe that the lessons learned in 2016 will motivate an era of reset, reflection, comprehensive assessment, prioritized project planning, full team collaboration, and strategic implementation at the enterprise-level toward longer-term residual gains with predictive analytics in 2017. I truly hope that organizations have learned the costly lessons in 2016 of abruptly redirecting to grab seemingly low-hanging fruit, accommodate the latest buzz and vendor hype, or respond to social media whims and rants.
Most of all, if organizations are to succeed and sustain in predictive analytics, they need to shift from a technology focus to one of true organizational transformation. That transformation requires vendor-neutral training; longer-term vision; uncomfortable change management; strategic consultation; a framework for operating and collaborating at the enterprise level; and a view on returns beyond the next quarter’s results.
The organizations that commit to that level of transformation in 2017 (healthcare or otherwise) will be the leaders in 2018. They will be the ones who have operationalized the shift from gut-level decisioning to automated and targeted data-driven decisioning. This shift will also free up their highly valued SMEs to focus their time, talent and experience on more creative and meaningful endeavors.
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