Narrative Science’s Predictions for Artificial Intelligence, Data and Innovation: Hits and Misses

January 12, 2016 Stuart Frankel & Kris Hammond

2016 Artificial Intelligence Predictions

“Never mistake a clear view for a short distance.” - Paul Saffo

End of year predictions can be bittersweet. They allow us to reflect on the year that has passed, what we’ve accomplished (or didn’t accomplish!) as well as allowing us to be hopeful of what is to come. Sometimes that hope takes form in predictions that wildly overshoot what is actually possible to occur in the very near-term.

So, what realistically will happen in 2016 based off of what we’ve experienced this year?

Before we crank up the prediction machine, we decided to assess our predictions from last year to learn what proved true, where we missed the mark, what areas had early indicators of change but we were a bit too early and what it all means for 2016.

Recap of 2015 Predictions

1) Artificial intelligence will step into the mainstream: Nailed it!

2) “A picture is worth a thousand words” is a marketing pitch: Not quite yet.

3) Democratization of data will become the democratization of information: Missed the mark.

4) The end of the data-hoarding era: Not quite yet.

5) Data scientists aren’t as sexy as we thought: Not quite yet.

1) Artificial intelligence will step into the mainstream: Nailed it!

Major tech companies such as Google, Facebook, Amazon and Twitter made huge investments in artificial intelligence, almost all of Gartner’s strategic predictions included AI and headlines repeatedly declared that AI-driven technologies were the next big disruptor to enterprise software.

There is still a long way to go but the formidable level of investment this year made it clear that AI-powered business and consumer solutions are on their way to being widely accepted everywhere.

What does it mean for 2016?

New AI inventions will explode - As artificial intelligence stepped into the mainstream, another change took place. Companies that made huge strides in AI, including Facebook, Microsoft, and Google, open-sourced their tools.

For 2016, new inventions will increasingly come to market through companies discovering new ways to apply AI versus building it. There will also be an explosion in startups with entrepreneurs now having access to low-cost quality AI technologies to create new products.

2) “A picture is worth a thousand words” is a marketing pitch: Not quite yet

Visualization investments continued to thrive in 2015, but there was also a growing recognition that good data analytics is in fact storytelling. Industry thought leader Tom Davenport said it accurately, “whether your analytical stories are told by human or machine, the key is to recognize the importance of simple and clear storytelling in the communication of quantitative analysis.”

What does it mean for 2016?

BI platforms will enter a new era - Helping to maximize the value of data and scale the amount of stories that can be told, natural language generation capabilities will begin to be integrated into BI platforms. Modernized platforms will increase the reach of analytics within organizations as the average user will be able to quickly understand and act upon insight.

3) Democratization of data will become the democratization of information: Missed the mark

We predicted that difficulties related to the average user interpreting data would lead to mass demand for information versus more data. This isn’t occurring just yet as companies are as data-hungry as ever. That said, we are beginning to see a shift toward companies questioning how they’ll actually use all the data they’re amassing.

What does it mean for 2016?

CEOs will demand transparency from intelligent systems - Paired with the growing trend of intelligent systems being used to provide answers, there will also be a growing belief that the data isn’t enough; users will want context too. Communication capabilities will increasingly be built into advanced analytics and intelligent systems so that these systems can explain how they are arriving at their answers.

4) The end of the data-hoarding era: Not quite yet

We believed that companies would begin to stop focusing on data collection and increasingly focus on the insight from the data. While this has happened to some extent, we’re seeing a shift towards companies being more nuanced with their data and focusing on collecting segmented sets as it pertains to their business objectives.

What does it mean for 2016?

(Data) size doesn’t matter - Facing unprecedented volumes of data and complex global infrastructures, big companies will kick off efforts to merge disparate data sets.

Paired with improved, advanced analytics, we’ll see a movement away from big data hype as businesses will also focus on understanding their small data, or datasets that contain very specific attributes,to determine current states and conditions and make more immediate business decisions.


5) Data scientists aren’t as sexy as we thought: Not quite yet

Job postings for data scientists actually increased in 2015, so we were a little too aggressive in our prediction. However, it is a challenging role to fill due to a variety of reasons (i.e. job descriptors vary so widely, they’re expensive, etc.), so companies are finding new ways to solve their data science needs.

What does it mean for 2016?

Portions of data science will increasingly be automated - The option of implementing a scalable automated data science system and training an analyst to use the system will increasingly be a popular choice. Data scientists will still be in demand in 2016, but we don’t think the filling the role will be as urgent as in 2015.

And our new prediction for 2016 (because who doesn’t like ‘new’?) is...

Innovation labs will become a competitive asset

With the pace of innovation accelerating exponentially, large organizations in industries like retail, insurance and government, will focus even more energies on remaining competitive and discovering the next big thing by forming innovations labs. Innovation labs have existed for some time but in 2016, we’ll begin to see more resources devoted to innovation labs and more technologies discovered in the labs actually implemented in their parent company.

Stay tuned for how we do and check back in December 2016 to hear our hits and misses.


Click here to view download and share our predictions in an infographic format.

 
Previous Article
Technology Explained: Talking Artificial Intelligence with Fast Forward Labs' Hilary Mason
Technology Explained: Talking Artificial Intelligence with Fast Forward Labs' Hilary Mason

View an Artificial Intelligence-focused segment from 'Technology Explained', co-hosted by Kris Hammond, Chi...

Next Article
Stop Drowning your Data Scientists in Drudgery
Stop Drowning your Data Scientists in Drudgery

Kris Hammond discusses the need to utilize the technology of today to do the rote tasks that you might have...

×

Get Narrative Science blog posts in your Inbox

Keep an eye out for your confirm email!
Error - something went wrong!