The digitization of business continues apace; every company is challenged by competitors that are leveraging the Internet. The market pressure to invest in disruptive product and service development projects leveraging the cloud is at its highest point ever. Marry this need with new technologies to manage and understand the massive amounts of data that companies are generating, and suddenly there are all kinds of new commercial opportunities. In fact, entirely new business models emerge. Gartner is forecasting technology spending on Big Data to grow to $232 billion by 2016. And that’s just the tip of the iceberg as it doesn’t even estimate the value-creation opportunity of Big Data.
Leading companies are now moving beyond the challenges of how to make Big Data available in one place for managing it to allowing hundreds or thousands of people to use it in ways that create real business value. Companies have started to understand the data they have and are building strategies around it. Their focus: How can we productize the information and make money from new businesses or services based on it?
Three ways to generate value out of Big Data
We’ve recently observed leading companies using three main strategies for generating value from their data.
1. Create a business model around a service that helps traditional companies take advantage of their own Big Data
This strategy is especially great for startups. An example is edo interactive, a Silicon Valley company that created a service for credit card companies. edo interactive analyzes consumers’ purchasing patterns residing in the Big Data of its credit card company clients along with what the consumers are doing in real time. As edo observes a consumer buying something at a store, it delivers a digitized discount offer right at the point of the transaction.
Although the credit card companies possess the Big Data, some are currently unable to capitalize on it. So startup edo created a service that consumes Big Data coming from its customers (credit card companies) in a way that the credit card companies want to pay for it as a service. Someday the credit card companies may decide to do this for themselves and thus lower the cost of this service. But until they reach the point of overcoming their legacy operations, they will buy such services from more nimble, innovative startup companies like edo.
2. Aggregate data from various sources and make it available to organizations that want to consume it
New business models are also arising from this data-aggregation strategy, both in for-profit and non-profit organizations. Not all of the models generate revenue, but all generate value for the users of the data information.
Medical Data Vision develops management support software for hospitals and health clinics throughout Japan. MDV offers an evidence-based medicine service that collects and shares medical data from public and private healthcare centers across Japan. MDV aims to improve the quality of medical care by analyzing and sharing medical results with pharmaceutical companies for epidemiological studies, market research and further drug development.
University of Oxford Clinical Trial Service Unit (CTSU) extracts and analyzes data that pertains to the causes, prevention and treatment of premature death and disability worldwide. They study the causes and treatments of chronic illnesses such as cancer, heart disease and strokes. The Oxford CTSU curates many thousands of pieces of information from over half a million volunteers and makes it available anonymously to the worldwide scientific community. Scientists all over the world can then use that data to find out more about the causes of illnesses, the links between the causes and diseases, and hence improve prevention and treatment. To do this, Oxford CTSU had to build a Big Data IT solution to identify, manipulate and extract exactly the data that researchers need and make it available to them within extremely fast timescales.
3. Analyze consumer behavior data to generate revenue from targeted advertising
Social media companies have truly led the charge in commercializing Big Data. Many of them are investing in understanding the behavior of the people using their media and then feeding that behavior data into tailored advertising/promotions and content generation.
Badoo, the world’s largest and fastest growing social network for meeting new people, is a free service that generates its revenues from advertising sales. Badoo is an example of a social media company now using its customer behavior data to generate content targeted at the interests of specific users.
IsCool Entertainment is an online gaming company that created a new business model around Big Data. They analyze which games people like and what they’re doing with online games so they can present targeted advertisements from their customer companies that want to advertise to consumers as well as shape their product development of new games as advertising platforms. As with all advertising, the more you can advertise to the right people, the more you sell and the more money you make.
Uncovering Big Data opportunities
Until recently, many companies lacked visibility into their Big Data. Either they were unable to capture it or unable to store it. In some companies, the data didn’t exist. Where it did, people often lacked access to it. It was in some kind of rarified data center environment or it was too expensive to get in there and use it in a practical way for the business.
Traditional companies vs. startups
Traditional companies are transitioning their IT to be able to commercialize Big Data opportunities, but startups are leading the way, for sure. They don’t have any kind of legacy IT baggage, so they are able to innovate and create new business models ahead of the curve of the traditional companies.
Traditional companies are realizing they have to get on board now like the startups or they’re going to lose the value of the data that their own business is generating from their standard product lines and services. As I mentioned at the beginning of this article, they need to invest in R&D for a disruptive product and or service leveraging their Big Data. But this is not like a typical IT project where there is an ROI for a certain amount of spend. This is an R&D game where the company determines where there is a market for the potential data service or product, which then helps in the calculation of the appropriate amount to spend on R&D for developing the new product.
A word of caution for someone in a large company who is trying to build a new business unit on a Big Data commercial opportunity or for an entrepreneur launching a new company in this arena: Be sure you marry your vision as a business strategy with the technology strategy.
Tools for capitalizing on Big Data
The new digitized business model generates tons of data, as do new mobile devices. Devices and technologies are now generating data about when and how much time people spend consuming it. Plus transactions of buying online are generating data (what did the consumer buy, how much was she willing to spend, how far in advance did she buy or how long did she wait before buying). We now have information coming from all different angles that didn’t exist before.
Companies need the tools to be able to handle all this data and capitalize on it. edo interactive, the company I mentioned that is delivering a service to credit card companies, is using Actian’s Vectorwise real-time information-management infrastructure. Its high-performance analytic database allows companies to manage Big Data and deliver it either into business intelligence tools or into application services (such as what edo provides).
With the new data types and volume of data, pricing of traditional database solutions is not affordable for small businesses and startups — the companies that are nimble enough to quickly create new business opportunities from Big Data. Vectorwise was built to optimize the Intel x86 chip architecture, making its hardware requirements a fraction of the cost of leading solutions. It’s extremely affordable and ideal for startups, which often change and iterate their applications. (See the SandHill.com article, “Big Data Doesn’t Have to Cost Big Money.”)
Where Big Data commercialization is headed over the next three years
Today we hear a lot of talk about Big Data: “What are we going to do with it? and “How can we get on top of it and commercialize it before our competitors do?” Those conversations will continue through probably the next three to four years. But starting in the next two years, we’ll see the conversation start to shift because organizations will be challenged with the “Big Information” that Big Data is creating.
We’re going to have a lot of information in the future. If we were to collect every piece of information ever generated by every society today, it wouldn’t compare to what we’ll be facing in two to three years. This will change the game to another level of innovation. At that time, we’ll not be focusing just on infrastructure build-out for new business models but, rather, on application development of new analytic products and services for the always-on, anywhere connected consumer.
There will be a new spurt of application and business model development around the gargantuan volume of information. It’s visible on the horizon now. Around 2015 the market will shift to “Big Information” and application development as the next phase. And as we’re seeing today with efforts to commercialize Big Data opportunities, some companies will be faster and some slower in capitalizing on Big Information app development.
Kevin Cox is vice president of Corporate Marketing at Actian Corporation, a leading analytics database company helping companies tackle Big Data. He previously led leading strategic marketing initiatives for SAP NetWeaver, SAP’s line-of-business marketing and SAP HANA. Earlier he worked for prominent marketing agencies. He founded Ogilvy & Mather Interactive Singapore and the digital arm of Doremus San Francisco. He was named a top digital marketer of 2011 and 2012 by B-to-B, the Magazine for Marketing Strategists.