Today, I had that experience when reading about a new startup, Textio, raising $8 million to build out its text analysis solution. At present the technology is focused on the language used in human resources, to help improve job descriptions, for example.
The firm is applying artificial intelligence (AI) to ‘understand’ what goes on in job descriptions, and to guide those writing the descriptions to better achieve the company’s hiring goals. In the example above, the phrase is geared to attracting higher-quality female candidates, based on the qualities we associate with ‘love’ and ‘passion for learning’.
The company is planning to expand the targets of the Textio AI to other domains, and it’s HR tool is in use by leading tech firms already. All very interesting. But the thing that caught my eye in the Xconomy article was this:
Textio tracks language and other document attributes, looking for patterns that correlate with increased applications and applications that lead to screening interviews, as well as language that is more likely to attract male and female applicants. “Textio recognizes 50,000 distinct phrases that change the number, quality, and diversity of candidates who apply,” [founder Kieran] Snyder wrote in a November blog post. “The list of effective phrases is changing constantly as the market shifts.”
For example, name-checking “big data” in an engineering job listing just two years ago attracted significantly more applicants. Today, however, job listings that include that now-cliché phrase perform an average of 30 percent worse than those that leave it out. Meanwhile, the phrase “artificial intelligence” has come to the fore in the strongest-performing tech job listings during the last six months.
Wait a minute: big data is now cliché? I was surprised to read that.
A little research brought other information to light, that suggests that AI is eclipsing big data as a hot field of inquiry. As more companies have invested in big data initiatives they are wising up to the idea that data — even when there is a great deal of it — is passive. It’s not the data that has value, it’s the analytics that matters. That means we aren’t in a big data economy, but an algorithmic, machine learning, or AI economy. That’s where we are likely to get real leverage.
And that’s why even in 2014, I’ve come to find out, big data credentials had already started to drop in value, as reflected in a Foote Partners report:
According to Foote’s survey of more than 200,000 IT workers in the U.S. and Canada, pay premiums for 31 noncertified big data skills (such as Hadoop, MapReduce, Hbase, Hive, NoSQL, data mining, and base SAS) fell an average of 3 percent in over the second half of the year, but still ended up slightly for the year, thanks to healthy gains from January to June. Pay for 37 big data certifications (such as those from Cloudera, MongoDB, Teradata, and others) grew more than 10 percent for the year, but lost some value as the year went on.
Basically, it seems like ‘big data’ is becoming ‘data’, full stop. The ‘big’ is becoming superfluous, and the analytic side is where the heat is. And we’ll see the greatest returns on tightly focused analytics. As Shawn Rogers, Chief Research Officer, Dell Statistica, recently said:
One could already argue that the ROI of advanced analytics is highest when applied to targeted, vertical market use cases. This will continue to be the case in 2016 and beyond, with manufacturing – particularly regulated manufacturing – leading the way. Advanced analytics platforms will be increasingly relied on not only to uncover insights that help optimize processes, but to verify and validate those insights in accordance with regulatory requirements.
That pragmatic insight into where we will see near-term gains can be balanced with more global and strategic notions. Kevin Kelly, the founding editor of Wired wrote a visionary piece that I often refer back to, The three breakthroughs that have finally unleashed AI on the world. He makes the case that AI will become our generation’s electricity, writing ‘AI will be supremely boring, even as it transforms the Internet, the global economy, and civilization. It will enliven inert objects, much as electricity did more than a century ago’. But he also points out this boring stuff will be the wellspring of much future innovation:
There is almost nothing we can think of that cannot be made new, different, or interesting by infusing it with some extra IQ. In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI. This is a big deal, and now it’s here.
Yes, and now it’s here. And it is showing up in the hiring data at US companies, where AI skills are commanding the premium that big data know how used to.
This post was written as part of the Dell Insight Partners program, which provides news and analysis about the evolving world of tech. For more on these topics, visit Dell’s thought leadership site Power More. Dell sponsored this article, but the opinions are my own and don’t necessarily represent Dell’s positions or strategies.