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Why IBM and Twitter did a data analytics deal

November 3, 2014, 12:22 PM UTC
Twitter's IPO Filing Implies $12.8 Billion Value Amid Growth
A user checks a Twitter feed on a smartphone in this arranged photograph taken in London, U.K., on Friday, Oct. 4, 2013. Twitter Inc.'s initial public offering documents suggested a valuation of $12.8 billion for the microblogging service, underscoring the seven-year rise of a still unprofitable company that has helped revolutionize how people share information. Photographer: Chris Ratcliffe/Bloomberg via Getty Images
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Last week, IBM (IBM) and Twitter (TWTR) announced a data analytics partnership that in essence allows the former to incorporate the latter’s data into its products for businesses. They’re unlikely bedfellows to say the least, but there’s a method to the madness, says IBM’s Alistair Rennie, general manager of the company’s Business Analytics group, and Twitter’s Chris Moody, its vice president of data strategy.

They called me from the Mandalay Bay casino and hotel in Las Vegas. Here’s what they said, edited and condensed for clarity.

Rennie: There are three basic elements. First, we are going to integrate Twitter data with our cloud analytics tool to make it easy for customers to reach it. The second is we’ll team up to make solutions for very specific business needs, such as marketing and customer care. Lastly, IBM will train and certify 10,000 consultants on a global basis to be experts of the Twitter platform.

Moody: Twitter has a lot of relationships with a lot of executives. We have big brand relationships with companies advertising on Twitter. Executives ask how they can be more innovative. We can provide them with data but it’s just that—very large volumes of raw data, so it’s not actually useful to them. How do we get started? How do we get there? That’s not an easy question to answer. Our data is useful, we need someone to come along and build value on top of it and combine it with other data sources.

Twitter is ultimately the most important archive of human thought that has ever existed. It really does represent the voice of the planet. The question I would pose to business leaders is, if you were thinking of a particular business decision, would you want the world to weigh in? If I’m a retailer and my inventory system says 15 items are out of stock, my system can’t tell me which to restock and which to stop carrying. If we ask customers, they could be upset or not talking about it at all. It’s an additional lens into a human decision.

An example on a more macro scale would be: You manufacture computers. Your big challenge is figuring out what to make and how much to make of it. For the former, you can figure out what people are talking about that they most value and what the weaknesses are in a competing product. You can also talk about what you’re building and get a reaction from consumers.

Rennie: Like any application of analytics, the driver is never technology—it’s business outcome. The more data you can bring to that problem, especially when it’s an incredibly unique dataset like Twitter, the analytics become better and the decisions become clearer.

Moody: Customer service probably had its last big revolution in the 1950s and 1960s when call centers were introduced. That opened up a channel for customers to reach out when they had a need. Fundamentally it hasn’t changed—it’s a very reactionary business. Consumer companies are often finding that they’re spending time dragging themselves out of a hole to get the customer back to being okay with their service. If you think about Twitter as a platform, people are openly sharing their experiences with products. So you can really take a new look around doing service with your products and how they’re thinking about your products. If they’re really frustrated with battery life, instead of waiting for them to call you to cancel their contract, you can talk to them proactively and even tie that back into product development.

Rennie: We’re very early in using these platforms at scale. There’s a whole set of interesting use cases: research and development, pharmaceuticals and introducing a new protocol, fine-tuning a supply chain and looking for global disruption of it. Marketing, logistics, human resources, and well beyond.

Moody: At Twitter, it is not uncommon to start a conversation around data and an executive says hey, I keep hearing things I can do with Twitter data, tell me more, and I say, well what are your top five problems, and then we talk about how our data can be informative. We’re talking about high-volume raw data. There’s lots of information in the payload of a tweet, much more than 140 characters.

The benefits for Twitter fall into a couple buckets. Number one, the most important, is that we believe as companies start to run their business operations on Twitter they will naturally understand the platform better and will be more likely to engage on the platform. All kinds of good things happen for Twitter when that happens. It gets our data more deeply distributed inside of enterprises, which we believe will have real benefits in terms of future engagement with our platform, whether through adoption or advertising.

Rennie: From an IBM perspective, we think having this data is a big differentiator in terms of our ability to attract clients.

This item first appeared in the Nov. 3, 2014 edition of Data Sheet, Fortune’s daily newsletter on the business of technology. Sign up here.