Creating competitive advantage through data

I’ve been focused on investing in data-centric businesses for almost eight years. Over this time period, my view of what generates true competitive advantage through data has changed.  Where tools and technologies for data storage and management once weighed heavily on my mind, the applications and business models for erecting barriers around proprietary data assets currently dominate my thoughts. And when I took a look at the IA Ventures portfolio companies several themes became clear:

• The power of creating contributory databases, where the value of the Nth contributor leads to a non-linear increase in the value of the data asset due to network effects. Examples in the IA Ventures portfolio include BillGuard and Metamarkets. ThinkNear’s plan is to build and monetize a contributory data asset as well.

• The value of data aggregation, cleansing, normalization, indexing and streaming (data processing platforms), where massive real-time streams can be pushed to the desktop for customized filtering and analysis, made accessible via API for incorporation into live models and indexed and stored for historical analysis. Current portfolio companies in this sphere include Datasift, NewsCred, PlaceIQ, Recorded Future, SavingStar and Sulia.

• The leveraging of platforms for creating valuable and differentiated data assets (data creation platforms), either as a part of the core mission or as an outgrowth of building a customer-facing business. BankSimple, Coursekit and Kohort each fit this description.

• The importance of user experience, user interface and data visualization as tools for maximizing the value of data assets across each of IA Ventures’ key themes. Drew Conway’s joining the IA Ventures team as Scientist-in-Residence is evidence of the importance we place on helping our companies extract the most value from their data assets.

Contributory databases. The magic of these businesses is that a customer provides their own data in exchange for receiving a more robust set of aggregated data back that provides insight into the broader marketplace, or provides a vehicle for expressing a view. Give a little, get a lot back in return -  a pretty compelling value proposition, and one that frequently results in a payment from the data contributor in exchange for receiving enriched, aggregated data. Once these contributory databases are developed and customers become reliant on their insights, they become extremely valuable and persistent data assets. An example of a contributory database is the credit index business of Markit, where they poll dealers for prices on specific fixed income instruments, synthesize the data into a standardized and normalized index, and enable market participants to build products on top of these now industry-standard indices. This was the catalyst for building a multi-billion dollar company. Me likey. A lot.

Data processing platforms. These businesses create barriers through a combination of complex data architectures, proprietary algorithms and rich analytics to help customers consume data in whatever form they please. Often these businesses have special relationships with key data providers, that when combined with other data and processed as a whole create valuable differentiation and competitive barriers. Bloomberg is an example of a powerful data processing platform. They pull in data from a wide array of sources (including their own home grown data), integrate it into a unified stream, make it consumable via a dashboard or through an API, and offer a robust analytics suite for a staggering number of use cases. Needless to say, their scale and profitability is the envy of the industry.

Data creation platforms. These businesses solve vexing problems for large numbers of users, and by their nature capture a broad swath of data from their customers. As these data sets grow, they become increasingly valuable in enabling companies to better tailor their products and features, and to target customers with highly contextual and relevant offers. Customers don’t sign up to directly benefit from the data asset; the product is so valuable that they simply want the features offered out-of-the-box. As the product gets better over time, it just cements the lock-in of what is already a successful platform. Mint was an example of this kind of business. People saw value in the core product. But the product continued to get better as more customer data was collected and analyzed. There weren’t network effects, per se, but the sheer scale of the data asset that was created was an essential element of improving the product over time.

We’ve made helping portfolio companies defining their data strategies and assisting them with creating the differentiated, defensible data assets that will generate value for multiple constituencies a core part of the IA Ventures’ mission. Sexy? No (unless, of course, you think like Mike Driscoll of Metamarkets). Glamorous? Definitely not. Effective? We think so. In today’s world, every business generates potentially valuable data. The question is, are there ways of turning passive data into an active asset to increase the value of the business by making its products better, delivering a better customer experience, or creating a data stream that can be licensed to someone for whom it is most valuable? And the data doesn’t need to be “big” to be valuable, though scale is certainly a helpful dimension when working to create defensible data barriers.

We’re in the early stages of a data-driven revolution, and the models outlined above are simply the current instantiation of where we see opportunities for creating significant value for customers and investors alike. As exciting as the opportunity set is today, I can hardly imagine the scale of the opportunities tomorrow will bring.

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The IA Ventures challenge: A start-up investing in start-ups

As a venture investor, one thought routinely keeps me up at night: 

Are we making the right investments?

After the beads of sweat form on my brow, they really get going when I think of the complexity and multi-dimensional nature of the question. Some related issues that keep me awake include:

• Are we seeing the right deals?

• Is our evaluation process effective?

• Does our method of decision-making promote successful outcomes? 

• Are we capturing the data necessary to make a reasoned assessment of the above?

Gulp. Each one of these represents a key strategic initiative, core questions whose answers are necessary for building the best firm possible for the long term. Needless to say, these issues can’t be assessed in a vacuum, as the vectors of time, competitive pressures and market conditions are at play and invariably impact the answers. In short, running a venture firm is very difficult, and it’s successful operation involves navigating a multi-variate thicket of obstacles while optimizing the combination of long-term franchise value and LP returns.

Are we seeing the right deals?

One of the eternal questions in venture capital relates to the “deal funnel:” Should we work to maximize throughput or optimize for quality. There are massive trade-offs between these two approaches, and different strategies subject themselves better to one or the other. For example, a general fund that is seeking to make a very large number of small investments is likely to solve for maximum throughput. They will apply a very basic but robust screen to quickly weed out the definite “nos,” likely do those that arrive with a high degree of “social proof” and spend some time thinking about the rest. Conversely, a more specialized fund (like IA Ventures) works to be crystal clear in its messaging in order to only attract deals that fundamentally fit with its investment thesis. This will yield a smaller amount of more highly curated throughput, providing a more manageable pool of deals that require more in-depth screening before a go/no go decision is arrived at. 

Seems pretty rational, right? Well, if you consider being almost 100% reactive rational. The problem is, I don’t. While having a giant catcher’s mitt is a fine way to gain a sense of what’s trending, it is very hard to identify true gems sitting on your ass and letting the market define your opportunity set. So that means thinking deeply about what the future holds, considering megatrends, and actively seeking opportunities that others think suck or simply don’t understand but really represent a window into the future. It is quite difficult to have this level of conviction and risk tolerance and to subject yourself to ridicule by shunning conventional wisdom. But who said the road to innovation - and riches - was going to be easy? It’s not. Being contrarian and pursuing true innovation requires a lot of hard work, and sitting in your office and simply being a filter is not the way to achieve extraordinary returns IMHO. So bottom line, our approach is to combine a clear domain focus (which generates curated, but reactive, deal flow) with a willingness to try, test and incubate.

Is our evaluation process effective?

The Big Screen. Not easy, when you consider the massive inbound volume of most venture firms (and IA Ventures is no exception). Do we have a clear sense of what we’re looking for (like a checklist), or is the spark of a crazy idea what really gets us going? More importantly, what should get us going? And are we able to glean enough from basic written materials to make an educated judgment as to whether or not an idea is worth digging into? Due to sheer volume, it is impossible for us to engage with more than a small percentage of the companies that reach out to us. Since we’re almost exclusively focused on pre-revenue opportunities, it really is about the entrepreneur, the idea and the vision, not actual performance. And most challenging of all, we are too young to have much data on the efficacy of our evaluation process. We do not yet have an “anti-portfolio,” a series of misses that might be instructive of our fears, biases and blind spots. So we are using our experience and best efforts to choose well, but with precious little data on which to base our decisions.

As a firm, we have a series of well-understood “hot buttons” - a set of attributes we are looking for in an opportunity. These attributes are applied to the top of our deal funnel, sharply reducing the number of potential opportunities that warrant a follow up phone call, meeting, etc. We definitely try to apply the lens of “Is this really differentiated/transformational/addressing a sharp pain point in a large market/etc” when gauging our interest. We are also predisposed towards opportunities that reach us early in the financing process, where we can both play a significant role in the deal and work with the entrepreneur on the plan, milestones and syndicate. The nature of our interaction with the entrepreneur is also instructive of whether we make a good team, and a positive working relationship can help de-risk execution of the plan. While I feel like we’re doing the right things, the jury is still out until we are able to collect the data necessary to validate our process.

Does our method of decision-making promote successful outcomes?

Now this is where it gets very tricky. Different partnerships have starkly different views on how a deal gets approved. They also have different cultures with respect to individual “check writers” versus a firm approach. Some firms want consensus. Others will not do a deal if there is consensus. Some firms have very rigid time frames around which funding decisions can get made: others are more free-form. How a firm makes decisions can define a culture and a partnership, and is not a matter to be taken lightly. At IA Ventures, we take a team-managed approach to investing. There are no individual check-writers. We invest as a group. We do not strive for consensus or have hard and fast “thumbs up/thumbs down” rules. Deal deliberations are active and ongoing at each stage of the evaluation process, and by the time we are considering issuing a term sheet we’ve all hashed it out pretty well. It is rare that all four of us are equally pumped about a deal, which is good. Because if we’re all psyched, it probably means the idea isn’t sufficiently differentiated to disrupt a market. In fact, we revel in conflict and dissent because it forces us to look at all sides of an opportunity, and to guard against the groupthink/rose-colored glasses associated with a “hot” (read: popular) idea. I think we do a pretty good job on this front.

One thing we don’t do, an idea that my smart friend Phin Barnes and I were kicking around last week, is empower an individual to meet an entrepreneur, fall in love, have a strong gut feel and make a commitment on the spot. The benefits: a willingness to fund orthogonal ideas without over-thinking and detailed analysis; a forced focus on the entrepreneur and their power to make an idea come to life; a special bond with the entrepreneur by showing deep confidence and conviction in their idea by offering an immediate commitment; and the signaling effects of having a firm and culture that supports such instinctive and passionate decisions in favor of the entrepreneur. If one truly believes that success in stage investing is heavily dependent upon the quality and passion of the entrepreneur and a contrarian take on the market, then having this as part of an investment program might be both rational and effective. I’m not there yet, but it is certainly a provocative and interesting approach to deploying an amount of high risk, high return capital.

Are we capturing the data necessary to make a reasoned assessment of the above?

It is early days, but we have built instrumentation and processes to help us collect data on the dimensions discussed above. While there is little doubt in my mind that smart venture investing - specifically seed stage investing - is impacted by a heavy dose of art with a modicum of science, we want to collect as much data as possible about how we do what we do to ensure that we’re using all the information at our disposal. Are we seeing the deals we want to see, and what portion of the deals are because we went out and got them as opposed to them coming to us? Are we doing a good job investing in companies consistent with our mission, some of which are off the beaten path? Are we taking enough risk, and do members of our firm have the opportunity to be hard-core champions of a deal and to get it done in the face of dissent? Do our dashboards give us useful and actionable information about how we should be running our business? All of these metrics are important for doing the best job possible and building the best long-term business at IA Ventures, for the benefit of our entrepreneurs, our LPs and our firm colleagues.

Being a start-up investing in start-ups is no easy task. But we’re trying to be thoughtful about it, try new things and iterate rapidly with the benefit of data. Sounds a lot like what we expect from the start-ups in which we invest. Makes sense…

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Skating to where the puck is going

There is a question that has been rattling around my brain for the past several months, and has recently become a hot topic of conversation within our firm:

How does one build a highly-respected, sustainable firm that really makes a difference in venture investing? 

There is a lot tangled up in this question, including:

• How we are perceived in the marketplace - do entrepreneurs actively seek out IA Ventures as their partner of choice? Do we work hard for our companies and actually add value to their efforts? Do other venture firms want us as strategic partners in their syndicates? Are we viewed as forward-looking and risk-taking or more opportunistic?

• How are we looked upon by current and prospective LPs - are we pursuing a strategy that is highly profitable, differentiated and robust? Are we investing for near-term opportunities or pursuing a longer-term, thematic strategy?

• How do we feel about ourselves - are we doing work that is fulfilling, makes us proud and leverages the full extent of our capabilities? Are we in this for the next 20, 30 years and beyond?

My friend Chris Dixon recently tweeted the following, which is remarkably in-line with my though process (highlights my own):

Top tier VCs are top tier for a reason. Smarter, longer term thinking, greater integrity.

Smarter? Hard to say. We stick to our knitting around our thematic focus which I think has helped us be smart in the deals we’ve chosen to do. Integrity? I’d like to think that those with whom we’ve engaged, be they portfolio companies, near misses or quick rejections, believe we’ve dealt with them in an open, honest ethical and transparent manner, regardless of whether we’ve made them happy. This also applies to other venture firms who have shown us deals, those whom we’ve brought into syndicates and other less-structured interactions. Longer term thinking. This is the criterion that keeps me up at night…

As the principal of a venture firm that gets a lot of deal flow, not to mention that we are a start-up in a sense, we constantly find ourselves playing defense. The vast majority of our time is spent on the filtering aspect of the deal business, expending substantial effort working to streamline the process at the top of the funnel in order that we can identify the highest-potential opportunities in an efficient manner. Perhaps we should be thanking our lucky stars that we have such problems: deal flow that tends to overwhelm our screening process, leading to very intense discussions about the allocation of time between existing and prospective portfolio companies. And since we make existing portfolio companies top priority, it places a premium on how we engage with entrepreneurs and identify the opportunities most likely to make it all the way through the funnel. Sounds rational, right?

The problem is, my thesis is that great venture firms neither live in the past nor the present: they live in the future. Thinking deeply about megatrends. Considering what people and businesses will need in years from now. Identifying shortcomings in current products and infrastructure. Taking into account macro factors and global capital flows. Living in the future is risky and hard, but promises the richest rewards for those who are best at prediction and have the capital and the stomach for the risk. Assuming my thesis is correct, the problem then becomes: how does a busy venture firm have the time to not merely field interesting ideas and to learn from passive flow, but to positively impact flow by actively seeking out and seeding big ideas that will impact the future but don’t look that way today? This is the magic I am looking for. Big. Future. Contrarian. Impossible for the faint-of-heart to grab onto and ride for the next 3-5 years.

This approach has nothing to do with diversification through portfolio effects, and everything to do with making a series of concentrated bets that are legged into as data is collected, execution is demonstrated and market opportunity becomes apparent over time. This kind of thinking is shaping and re-shaping our strategy at IA Ventures, and represents an acknowledgement of what we really want to be: a deeply engaged partner with management in pursuing big, honking ideas that many if not most think are nutty at the investment’s inception. But it has become clear to me that the greatest returns are reserved for those who are willing to “go rogue,” to pull a Costanza, to do the opposite of conventional wisdom. And when I look back at my public markets experience the same is true. Heavily trafficked ideas tend to end in tears, while those with well-researched, deeply-held thematic views tend to come out on top. I’ve merely translated my prior public markets experience into the venture asset class. I had never thought of it this clearly before but I believe this to be true. And away we go…

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The Role of UI/UX in the Big Data Revolution

This week’s Strata Conference was a truly magical event for those immersed in the world of Big Data. Congratulations to Tim O’Reilly, Gina Blaber and the rest of the O’Reilly team for throwing a fantastic event. It was great to be a part of it and I’m looking forward to being deeply involved in the next edition, New York style. It also afforded myself and the IA Ventures team the opportunity of spending quality time with our fellow data geek-masters (and mistresses) such as Hilary Mason, Mike Driscoll, Drew Conway, Bradford Stephens, Flip Kromer and others with whom we consumed many fine (and not so fine) beverages and eats. Such an assemblage of brain power and personality is seldom observed in nature, but Strata had it in spades.

When reflecting back on the conference, the hallway meetings and late-night conversations, one feature of my myriad mind-bending explorations emerged: the importance of interface design and user experience in helping display the power and value of sophisticated Big Data technologies and analytics. This theme also emerged in a discussion I had with Mac Slocum of O’Reilly. I find that I never learn so much about what is going on inside my head as when I write or am interviewed, as being forced to let stream-of-consciousness flow minimizes the effect of preconceived notions and biases.

So much of Strata and, in fact, the dialogue around Big Data in general is focused on hard-core technologies, bleeding-edge analytics, data manipulation and consumption via APIs. The truth is, however, that much of the complexity and depth of Big Data analysis only comes to life and becomes actionable when presented in a clear and intuitive manner. This places a huge premium on start-ups with awesome UI/UX skills. And when I started to reflect on the IA Ventures portfolio - BankSimple, BillGuard, Kinetic Global, Metamarkets, PlaceIQ, Recorded Future, Sulia and TraceVector - almost all of our investments have an essential focus on interface design and user experience to extract value from extremely complex data-driven architectures. In my talk with Mac I used the example of BankSimple as a firm with a core focus on UI/UX - so much so, in fact, that the company really grew out of the question “What do consumers really want and how can we optimize their retail banking experience on mobile devices?” and developed an architecture and set of business processes to deliver on this value proposition. But such thinking isn’t merely the province of B2C; it also applies to those selling to the enterprise. Metamarkets ingests terabytes of publisher data in real-time and performs sophisticated analysis to provide them with powerful, actionable information that impacts inventory pricing decisions. The importance of the design and usefulness of the Metamarkets dashboard can’t be overstated; several large, global publishers are dependent upon this information, and it is Metamarkets job to make it readily consumable, easy to understand and immediately actionable. Without a great interface, the power of massive data and valuable analytics wouldn’t be nearly as profound.

Another interesting feature of these Big Data companies is their mixed DNA: world-class hackers and data scientists together with data visualization and user experience experts. Clearly these worlds overlap; many a great visualization guru is a top-flight data scientist. It’s just that getting these multiple personalities and skill sets to work together in a seamless manner to drive value to the client, whether they be consumer or business, is no mean feat. But these companies have been able to instill the importance of “customer first” within their organizations, forcing the intersection of real-time actionable information with a great user experience in perfect harmony. Now THIS is a Big Data revolution: giving the props not only to the data engineers but to the data depicters. Algos are great, but a picture is worth a thousand words.

This line of thinking has been reinforced in a book I read recently, A Whole New Mind by Daniel Pink. The essence of his thesis: right-brain (conceptual) thinking will become increasingly important to the West where much of the left-brain (analytical) tasks are being commoditized and outsourced to Asia. Most great data scientists I know are a synthesis of right and left-brain attributes: super powerful analytical minds but with a rich creative streak that extends into elegant code, unusual and insightful analytics and highly effective visualizations of complex data sets. And I believe the market has spoken. Great UI/UX people are in high demand, as are the most creative and efficient coders and data hackers. And these people aren’t 2x or 3x better than the merely good: they are 50x or 100x more valuable. Supply and demand are massively out of whack, and I fully expect this to continue unless our educational system moves away from rote memorization into critical thinking and celebration of orthogonal ways of looking at problems. Time will tell, but the role of the US in the Big Data revolution may well depend on it.

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Don’t fear the enterprise

A recent question on Quora concerning Enterprise 2.0 got me thinking about the attractiveness of the enterprise as a target for investment. I’m not exactly sure what Enterprise 2.0 means, but if it connotes helping the enterprise do stuff better - communicate both internally and with customers, store, retrieve, display analyze and monetize data, etc. - while leveraging the latest in distributed and cloud computing and advances in database architecture, then I view this as an extremely attractive space and incredibly fertile ground for investment. WIth all the focus and hype around the consumer and social media, the enterprise has, at least from a PR perspective, been sorely neglected. 

We at IA Ventures view the enterprise as being ripe for disruption, and it is fascinating to see which enterprises are proactively engaging in “creative destruction” while others are fighting the inevitable until the bitter end. It is our job as forward-looking investors to help the enterprise transition from old ways of doing business, both internally and externally, as seamlessly and painlessly as possible. Some will be willing and able to go through wholesale transitions - ripping out old databases and moving to the cloud, integrating social media into internal and external communications, breaking down silo’d data and creating a holistic data store that can be efficiently be mined for business intelligence applications, etc. - while others will stick their toe in the water until the tsunami forces immediate and dramatic maneuvers.

I think the enterprise provides a generational opportunity for the venture industry, but it takes a strong stomach and a comfort with longer sales cycles and the complexities of selling into hierarchical organizations. It might also take more investment dollars to collect the necessary data to prove or disprove the investment thesis. This isn’t about lightweight web apps and rapid A/B testing with real-time feedback; it requires an entirely different mind-set. But if you can get your mind around this and the enterprise is in your DNA, it is an exciting place to be.

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