Exploring Big Data Business Models & The Winning Value Propositions Behind Them
It goes without saying, innovative, sustainable Big Data Business Models are as pervasive and sought after as they are elusive (i.e. “data is the new oil”). For every startup that designs and implements what amounts to a devilishly simple and effective big data business model (see any social network), perhaps changing the entire landscape with it, there are literally hundreds (if not thousands) of larger, more mature companies looking for ways to monetize their own big data in the hope that they can capture new revenue streams (and compete effectively in the future). Of course some of the larger, mature companies have done quite well in this regard. Apple (40 years old) and Amazon (20 years old), for instance, have vastly different business models. Yet, both companies have built solid business models around big data; both use big data to present to consumers products and services that might be relevant to them. Similarly, Netflix and Pandora, 18 and 15 years old respectively, designed brand new big data business models around understanding and creating value for customers in ways that seemed like magic at the time. So, what’s behind these business models? And, are there other business models that might help other (mature) companies create, deliver, and capture value using big data at the core? The answer (to both questions) is simple: it’s all in the value proposition.
One of my favorite quotes to use when working with both small and large organizations was made famous by Intuit and more recently Uri Levine, one of the Waze co-founders: “Fall in Love with the Problem, Not the Solution.” As simple as this quote is it speaks volumes when considering how mature companies tend to think about utilizing their own big data stores to create new business models. That is to say most mature companies first ask, “What big data do we have today?” followed by, “how might we sell this data?” Looking back on my favorite aforementioned quote, you can probably see the discrepancy here: most mature companies believe there is some mythical marketplace where they can simultaneously sell their big data whilst not pissing off their customers. These assumptions are more often than not wrong. Moreover, while there are LOTS of “problems” to fall in love with when it comes to big data business models, in order to provide some focus, this post highlights three categories of big data business models based on their value propositions and customers (e.g. DaaS, IaaS, and AaaS respectively — figure 1). And, rather than focus on the myriad of ways that a company can monetize the big data ecosystem, like the transport of big data, these business models center on companies that have seemingly valuable big data that they want to monetize in some way.
The first of these business models is focused on providing customers a way to mine their own insights or choose their own adventures if you wish. We can call this business model “Data as a Service” (DaaS — figure 2). DaaS hinges on a value proposition for supplying large amounts of processed data with the idea that the customer’s job-to-be-done is to find answers or develop solutions for their customers. The customers in this case may be solution providers looking to use close to raw data to enhance their own offerings (i.e. value proposition) or even developers wanting to develop niche applications to address consumer pains. The data in this business model is aggregated from the company’s own customers or from outside sources (key partners). As you can see in DaaS’s business model canvas, what is unique about this business model is that the key activities to create, market, and (hopefully) sell a viable value proposition are relatively low cost. And, yes, this probably sounds like the “mythical data marketplace”. However, it should be said, that in order to engender trust among all customers, the most important — and probably expensive — activity in this business model is processing data such that it is stripped of any sensitive customer details. If you’re Twitter, for instance, this would mean potentially removing any private information (maybe even handles) such that the resulting data is valuable in the sense that the big data customer can create some unique value proposition on their own. Once the data in question has been processed/cleaned-up by the companies key resources (or key partners), the rest of this business model is about ensuring the customers are able to get/use the data to enhance their own value propositions. Because the data in this case is only valuable as a support mechanism for customers to create other value propositions, the revenue stream is typically quite a bit lower — and maybe even free in some cases — than what you’ll find in the other business models. Examples of organizations that use this business model are government open data sites, like datasf.org, and commercial vendors, like Gnip, and even Twitter.
The second big data business model, called “Information as a Service” (IaaS), focuses on providing insights based on the analysis of processed data (figure 3). In this case the customer’s job-to-be-done is more about coming up with their own conclusions or even “selling” an idea based on certain information. Additionally, IaaS customers don’t want to or do not have the resources to process and analyze data. Rather they are willing to exchange value for analysis from trusted parties. Unlike the DaaS business model, which is about aggregation and dissemination of lots of processed data for customers to create their own value propositions from, the IaaS business model is all about turning data into information for customers who need something — and are willing to pay for something — more tailored. To do this, key activities must include analysis and data visualization as well as perhaps research that enhances the analysis of data. What’s probably more interesting about this business model is that the value proposition may also be more targeted for specific customer segments. For instance, I would put location information companies, like HERE (now owned by Audi, BWM, and Mercedes), in this category, as one of the business models HERE is selling is maps and other information to companies for use in their own navigation systems. To do this, HERE collects, aggregates, and cleans data eventually turning it into information in the form of stylized, visual maps (i.e. information) that can be sold to customers. From a consumer perspective, many of the health tracking companies, like FitBit, sell products with value propositions that focus on providing analytics based on tracking consumers’ activity. And, like HERE, FitBit collects, aggregates, and turns data into consumable information that can be used by you and me for our own personal analysis (i.e. should I eat that donut or take a walk…or both?!).
The third big data business model is called “Answers as a Service” (AaaS) which is focused on providing higher-level answers to specific questions rather than simply the information that can be used to come up with an answer (figure 4). AaaS customers often need specific direction in order to make decisions. In fact, the customers in this case may be willing to make spontaneous “buying” decisions given the right value proposition (i.e. answer). This business model, as you might guess, is the top of the pyramid when it comes big data. The key with this business model is that given the company’s ability to create real, trusted value in the answers it provides to customers, customers in turn will exchange an increased amount of value in kind. An interesting example in this case is Mint, the personal money management service (online and app). Mint makes it incredibly easy for anyone to provide some basic information, like bank details, for which Mint will track, analyze, and visualize the resulting information for consumer consumption. However, where answers come into play is that if the consumer provides credit card details Mint will not only make available the transaction information for those credit cards, the service also sells the resulting information to other credit card companies in return for the ability to advertise credit cards with better rates (i.e. answers). Whereas, customers typically become queasy with the notion of some company selling their credit card information, in this case there is true, defensible, and desired value exchange being made for the answers returned back to consumers. Likewise, Google’s new Google Photos service will churn through photos identifying people, places, and situations — some of the most personal data there is. However, in return the service creates real value in the form of curated stories, auto-stylized photos, and even cool little GIF animations, all of which can be considered answers in this case. You might ask how these might be considered answerers. The key with this business model is that the value proposition speaks to the pains customers have in doing their own research or curating their own photos, for instance. Hence, with this business model the more value is exchanged in one way or another.
Of course there are plenty of other business model options that can be designed to exchange the value that big data creates. However, if you work or run a company that has what you believe is a big data opportunity, the options above can be used as the first filter or first pass. And, in doing so it is most important NOT to consider the solutions (left side of the business model canvas) first, but the customer problems you wish to solve and the trade-offs that come with solving those customer needs. It should also be said that when it comes to big data business models, the data in question can come from many sources, from aggregated customer data to free and commercial sources; it does not have to come from one single party. For a great, and very deep, read on this subject, check out Big Data for Big Business? A Taxonomy of Data-driven Business Models used by Start-up Firms, by Philipp Max Hartmann, Mohamed Zaki, Niels Feldmann and Andy Neely at University of Cambridge. There is also a great HBR article about big data and customer trust, called Customer Data: Designing for Transparency and Trust, by Timothy Morey, Theodore “Theo” Forbath, and Allison Schoop.
So, what do you think? What value does your company promise to provide customers today? How could knowing more about your customers’ jobs-to-be-done, pains, and desired gains help you build an innovative, sustainable big data business model for the future? Need more?
This article is written by Justin Lokitz