The Full-Stack Venture Capitalist
There is an emerging trend towards the use of data and software to evolve and develop the existing venture capital model. Interesting articles on this topic can be found here on Medium or here on Entrepreneur.com or here on the FT.
Venture firms use different methods to approach the data opportunity and it is evident that there is no single strategy that fits all. Different firms develop a varied approach based on their organisational design, budget and ambition. The topic is becoming increasingly relevant for the Limited Partner community, as they explore methods for conducting due diligence on this new component of a venture firm.
In order to participate in the existing industry debate, the organisational implication of data strategies at different venture capital firms must be considered. The topic can be analysed by leveraging classic literature on introducing innovation to a traditional industry, in this case venture capital. Furthermore, many of the observations below could also be applied to the broader industry of private equity, even though the topic is tailored to venture capital.
In general, there are three different organisational approaches that venture firms may adopt, in order to innovate and invest in their data and software strategy:
1 — Data and software strategy at the associate level (Data Associate)
Several firms hire a data scientist and expect her/him to deliver an associate-level contribution to the firm, with regards to deal flow generation and/or due diligence. Historically, the biggest issue associated with this strategy is retention of the resource and effectiveness of the approach. Often, the data scientist is one of a kind in a firm with zero technological capabilities. Culturally, the resource is extremely complex to integrate, given the lack of a technology-focused seat at the partner table, making it impossible to steer the firm towards real adoption. In this scenario, the software and data approach remains peripheral to the firm and primary processes are unaffected.
Another issue associated with the approach is that data scientists usually lack the software engineering skills required to build and maintain software production systems. As a result, the data scientists must purchase existing data platforms, in order to apply their skills. This is great news for our friends at DealRoom, as well as other platforms on the market, since this method of integrating data science is solely applied on third-party data.
It is unclear if the approach is built to last and the greatest benefit from this type of innovation will probably be focused on supporting the due diligence process, rather than facilitating deal sourcing.
You also need to ask why a talented data scientist would join this type of organisation; what are the implications for her/his career progression? Ultimately, this is the root-cause for low retention rates which are typical for these positions.
2 — Data and software strategy at the partner level (Data Partner)
Some firms invest in software and data by appointing a Partner as the main driver/owner for this new strategic direction at the firm. The main advantage of this approach involves the representation of data and software at the top decision-making level of the firm and therefore the issue gains the right level of visibility.
In this circumstance, a lot depends on the seniority of the Partner involved and whether or not she/he has the right level of organizational authority to push for the necessary budget allocation, along with hiring a small team/task force with a combination of data science and software engineering skills.
There are a few examples in some of the largest venture capital firms, where significant budget has been allocated and a fully functional satellite team has been built (either in-house or in a remote location).
With this approach, firms create a hybrid model between a traditional structure and a more innovative data and software centric structure. In this case, processes are frequently built in parallel and in competition with each other. As a result, the new way and the old way are constantly benchmarked against each other. If the software and data approach becomes increasingly successful over time, there is a strong possibility that the venture firm might shift from legacy processes to a more pervasive use of software and data.
This methodology has often been used by large corporations to inject innovation into their organisation. These corporations sometimes create a task force and give the team maximum autonomy to solve a specific problem, such as building a new product. This organizational solution is referred to as a “startup inside the company”. There are plenty of examples where this approach has miserably failed, but also some successful cases.
The key elements that determine the success of this approach are: 1) the leadership of the Partner assigned to the task force; 2) the level of support from the rest of the partnership; 3) the size of budget allocation based on clear requirements; 4) the reasonable time expectations in terms of being able to see results and return on investment.
3 — Data and software strategy at the firm level (Full stack)
The most advanced approach in adopting software and data involves crafting a green field scenario, and starting a totally new organisation.
In standard innovation literature, this is referred to as the start-up method to innovate, and involves creating a new organization to solve a problem, incorporating a completely new method and in the absence of legacy.
With this approach, venture firms fully re-think the entire value chain of their activities and implement software and data at the center of their strategy. This approach is not about using software and data to incrementally improve the existing investment process, but rather about fundamentally freeing the organisation from any known constraints coming from the industry, in order to develop a totally new investment method.
There are some deep implications associated with this approach. By placing software and data at the forefront, this new breed of venture capital firms possess a structure similar to product companies, as opposed to professional services organisations. The skill set of the employees is very different and the firm itself must maintain a healthy level of organisational tension between software and data on the one hand, and investment decisions on the other. In the end, good looking data visualisations and complex workflows must only be judged if they contribute to making better investment decisions.
Deep Industry disruption will only occur when full stack venture firms become real players and offer a new alternative to legacy firms. This strategy regarding innovation is the most disruptive, but also the most risky and requires the perfect balance of an investment, operational and engineering skill set. Full-stack VCs can only succeed if the founding team possesses the right DNA and combination of skills to enable the development of the correct organizational culture.
Data Associate, Data Partner and Full-Stack are three very different approaches for venture capital firms to explore innovation and invest in software and data. Each of the three innovation models are a possible option, however the adoption of the Full stack VC approach is where true industry disruption will take place.