There are many classifications of businesses in the world, from aircraft manufacturers to candy stores. But the bottom line is that everyone is now in the data business.
Already, the lines have blurred between traditionally non-information technology companies and IT companies to the point where you can’t tell the two apart. But what’s really of value is not the software that’s being produced and shared, it’s the data that’s being generated and analyzed. This represents the future of many businesses — again, traditionally non-IT businesses — as they seek competitive advantage in a hyper-competitive global marketplace.
Many of today’s emerging startups recognize that data is where the opportunities are for disruption. As Mallia Wollen put it in a recent New York Times article, it has become a pressing need: “In a world of ever-increasing digital connectivity, ever larger mountains of data are produced by our cellphones, computers, digital cameras, RFID readers, smart meters and GPS devices. The huge quantity of data becomes unwieldy and difficult for companies and governments to manage and understand.”
A classic case now emerging: Retailing giant Wal-Mart has reportedly been hiring a lot of developers to staff its @Walmartlabs unit, formed from its acquisition of Kosmix last May. Kosmix offered a platform for identifying value and opportunities from “Big Data.” Now as @Walmartlabs, the new unit is tasked with assembling data from social networks and mobile transactions to build Wal-Mart’s online sales channel.
What is @Walmartlabs doing with all this data? The company is building what it calls a “Social Genome,” based on millions and billions of tweets, Facebook messages, blog postings, YouTube videos, and more –- essentially a living organism:
“The Social Genome is a vast, constantly changing, up-to-date knowledge base, with hundreds of millions of entities and relationships. We then use the Social Genome to perform semantic analysis of social media, and to power a broad array of e-commerce applications. For example, if a user never uses the word ‘coffee,’ but has mentioned many gourmet coffee brands (such as ‘Kopi Luwak’) in his tweets, we can use the Social Genome to detect the brands, and infer that he is interested in gourmet coffee. As another example, using the Social Genome, we may find that a user frequently mentions movies in her tweets. As a result, when she tweets ‘I love salt!,’ we can infer that she is probably talking about the movie ’salt,’ not the condiment.”
The real power comes in when Wal-Mart is able to combine data on purchase history with data from social networks with actual transaction history. As Anand Rajaraman, who runs @WalmartLabs, put it in an interview with Reuters’ Alistair Barr: “It’s a race to see who can use all this data the best. This will change the retail industry, as well as most other industries.” The transaction history shows what customers have bought in the past, while social networking data has the potential to show what they may buy in the future.
The evolution of today’s businesses to data companies has already been evident at software companies, and it’s only natural that traditionally non-IT companies also move in this direction. As Mike Hoskins, CTO of Pervasive Software, put it at a conference earlier this year: “Is Google a software company? Is Facebook a software company? They’re not, they’re data companies. The value they and many other companies provide to the market is their ability to manage data and provide analysis.”
Then there are the media and information companies — from The New York Times to Bloomberg to Thomson Reuters — who packages and sell information and data in various forms, and already compete with the likes of Google and Twitter on various levels. Amazon and Apple both cover software, data, and retail.
This point was also borne out by in a speech by RedMonk’s Stephen O’Grady, who pointed out that the road to riches is through data. His main point, summarized by The Register’s Matt Asay is that the success stories we see abounding across the business landscape are companies that enable users generate data, and then have figured out ways to monetize that data. As O’Grady put it: “those startups that wish to get big, in the Apple sense if not the Exxon, should begin leveraging collected data as a complementary revenue stream. … The Age of Software was fun. Welcome to the Age of Data.”