Small and medium-size businesses are often intimidated by the cost and complexity of handling large amounts of digital information. A recent study of 541 such firms in the UK showed that none were beginning to take advantage of big data.
That means these firms and a lot of others are at a serious disadvantage relative to competitors with the resources and expertise to mine data on customer behaviors and market trends. What these data-poor companies don’t know is that it’s possible to get a lot of value from big data without breaking the budget. I discovered this while researching my book Too Big to Ignore: The Business Case for Big Data. In fact, the above UK study notwithstanding, I found that plenty of small and midsized companies are doing interesting things with data, and they aren’t spending millions on it.
True that in the past, companies seeking to tap into big data needed to purchase expensive hardware and software, hire consultants, and invest huge amounts of time in analytics. But trends such as cloud computing, open-source software, and software as a service have changed all that. New, inexpensive ways to learn from data are emerging all the time.
Take Kaggle, for instance. Founded in 2010 by Anthony Goldbloom and Jeremy Howard, the company seeks to make data science a sport, and an affordable one at that. Kaggle is equal parts funding platform (like Kickstarter and Indiegogo), crowdsourcing company, social network, wiki, and job board (like Monster or Dice). Best of all, it’s incredibly useful for small and midsized businesses lacking tech- and data-savvy employees.
Anyone can post a data project by selecting an industry, type (public or private), participatory level (team or individual), reward amount, and timetable. Kaggle lets you easily put data scientists to work for you, and renting is much less expensive than buying them.
Online automobile dealer Carvana, a start-up with about 50 employees, used Kaggle to offer prizes ranging from $1,000 to $5,000 to data modelers who could come up with ways for Carvana to figure out the likelihood that particular cars found at auctions would turn out to be lemons. For the cost of the prize money (a total of $10,000), Carvana got “a hundred smart people modeling our data,” says cofounder Ryan Keeton, and a model that the company was able to host easily and inexpensively.
Even a lack of data isn’t an insurmountable obstacle to harnessing analytics. Data brokers such as Acxiom and DataLogix can provide companies with extremely valuable data at reasonable prices. As Lois Beckett writes, “There’s a thriving public market for data on individual Americans—especially data about the things we buy and might want to buy.” For example, a marketing-services unit of credit reporting giant Experian sells frequently updated lists of names of expectant parents and families with newborns.
A number of start-ups are jumping into this space—open-database firm Factual recently closed $25 million in series A financing, led by Andreessen Horowitz and Index Ventures. The company is building datasets around health care, education, entertainment, and government.
Do Kaggle, Factual, and their ilk represent the classic business disruption that will turn the data industry upside-down and make consumer and market information available to every company, large and small? No—they don’t portend the imminent demise of corporations’ in-house data analysis or of high-priced analytics firms. But they’re providing attractive alternatives for companies that can’t afford to—or simply don’t want to—hire their own data scientists.