Kobielus Predictions for Data Science in 2017
Tags: 2017 Predictions, Data Science, Data Science Skills, Data Scientist, IBM, IBM Watson, Open Source
IBM Data Evangelist James Kobielus predictions for 2017, including key role of data scientists in survival of their companies. Join industry experts for a live #MakeDataSimple Crowdchat on Thursday December 8 at 1:00pm EST.
Join industry experts for a live crowdchat on December 8 at 1:00pm EST: #MakeDataSimple
As we move into the new year, here are my core predictions for the data science community:
- Data scientists will have the survival of their companies riding on their success: In 2017, more enterprises will task data science teams with building their most strategic do-or-die applications. By the end of the coming year, every CEO who hopes to keep their job will boast of at least one strategic data science-driven application--incorporating artificial intelligence (AI), machine learning, and cognitive computing—upon which their firm’s survival, profitability, or future relevance will depend.
- Data scientists will command the coolest R&D projects in every industry: In 2017, data scientists will be the prime movers behind the most attention-grabbing, prestigious R&D in business, industry and the consumer world—most notably, those projects focusing on streaming media analytics, embedded deep learning, cognitive IoT, cognitive chatbots, embodied robotic cognition, autonomous vehicles, computer vision, and autocaptioning. A year from now, no R&D project in the business world will be funded if it doesn’t include a substantial amount of data science.
- Data scientists will drive enterprise business process management: In 2017, more data scientists will add business process management to their core operational responsibilities. Their primary focus within BPM teams will be as the designers and managers of the predictive analytics, machine learning, and stream-computing algorithms that drive 24x7 real-world experiments and A/B testing within all business processes, both customer-facing and internal. As 2017 rolls into 2018, every BPM team in the e-commerce world will include at least one data scientist who is tasked with continual monitoring and tweaking of a never-ending stream of incremental process changes.
- Data scientists will radically shift their development organizations toward open ecosystems: In 2017, data scientists’ open-source tools and platforms will become the foundation of all enterprise application development. During the year, data scientists will adopt integrated, open, cloud-based development environments. These will, at the very least, incorporate R, Spark, and Hadoop;provide access to a wide range of off-the-shelf and pluggable algorithm libraries; enable API-driven development of composable containerized microservices; automate more machine-learning development pipeline functions; present a notebook-oriented collaboration and sharing paradigm; maintain auditable project logs for algorithmic transparency, and enforce robust tracking and governance of data, models, and other development artifacts. Within increasingly federated data-science development teams, more training data curation will be sourced from open crowdsourcing environments and more fresh data-science talent will come from open communities such as Kaggle and TopCoder. As 2017 fades away and the end of the decade comes into clearer focus, lone-wolf disconnected data scientists will find their careers dead-ended and productivity flatlining unless they plug into increasingly open, federated development ecosystems.
- The data science skills gap will diminish rapidly: In 2017, more traditional programmers and business analysts of all types will enter the data-science field. They’ll do so in recognition of the fact that acquiring data science skills and repositioning their careers in this direction is absolutely essential for them to remain relevant, effective, and employable. These non-traditional and new data scientists will only be allowed to participate in mainstream enterprise data-science programs after a clear “apprenticeship” or “probationary” period of supervision by established data scientist.
By the end of 2017, the developers and analysts who make this transition to full-fledged data scientists effectively will find their coding skills and domain expertise in hot demand in businesses around the world.
b// Want to share your predictions for big data, analytics, cloud data services, data science, and cognitive computing in 2017?
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