Workday bets on data science for the enterprise

Phil Wainewright Profile picture for user pwainewright July 15, 2015
Workday this week announced an acquisition and a venture fund to push data science and aims to help its customers embrace even more change

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Workday is a vendor that serves two different masters. On the one hand it has to coax conservative enterprise buyers and business leaders into the cloud. At the same time it has to push the envelope of what can be done with modern financials and HR applications in a way that appeals to fast-moving digital enterprises.

That dichotomy was in evidence yesterday with some big moves to step up its investments and profile in data science innovation.

The company made waves with the launch of Workday Ventures, a corporate venture fund that will invest in data science startups. The fund will be led by Adeyami Ajao, Workday's VP of technology product strategy, who joined Workday when it acquired his own data science startup Identified. The launch of Workday Insights last October, based on Identified's technology, was the spark for the idea of investing further in other startups in the data science field, according to Ajao.

Less noticed but also significant was the announcement that Workday has made its third acquisition, buying Upshot, the controversial winner of a $1 million prize in the Salesforce1 Hackathon at Dreamforce in 2013. VentureBeat reports the move as an 'acqui-hire' of "engineers who are skilled in natural-language processing (NLP)."

Finally, Workday introduced Workday Next, an online hub to find out more about Workday's technology initiatives. Essentially, according to a company blog post the purpose of Workday Next is to explain more about what it's up to in these emerging fields:

When we announced Workday Insight Applications last November, followed by the availability of Workday Talent Insights in April, we went quite deep in our discussions about the technology and strategy behind these applications. We also shared stories on experts within Workday who applied data science and machine learning methods to build these predictive and prescriptive analytics capabilities.

We soon learned that people wanted to know more. Customers, potential employees, media, and analysts let us know they were intrigued by what Workday was doing in data science.

In pushing aggressively into the data science arena, Workday is riding a trend that's becoming prevalent throughout the enterprise software world. Nevertheless, it's pushing far ahead of where most of its customers and prospects are currently thinking. But as Chano Fernandez, VP EMEA, told me when we sat down for a catch-up recently, you don't succeed when you upgrade to a new enterprise application unless you're prepared to change:

Clearly, if you don't change, you don't achieve anything. By doing the same things again you're not going to get different results. So usually there's going to be some sort of change.

The question is not if but when. Some companies take a little bit longer to get there. But I have no doubt that all of them realize that they need to go there. Otherwise they're seeing competitors in the market moving there and becoming more efficient.

I've yet to meet the first company that says, you know what, I'm happy with what I have, I'm growing six percent a year, I don't want to grow more. If they stay there what's going to happen at the end of the day? Someone is going to come and that six percent — if you didn't do anything — eventually will become four, will become three, will become whatever. So you have to change.

Change is usually uncomfortable. People like to be in comfort zones.

My take

Interesting moves by a company that thrives most when its customers dare to leave their comfort zones. Its challenge is working out the best way to get them there.

Disclosure: Workday is a diginomica premier partner.

Image credit: Cloud concept © Bigstock

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