Investing in data science teams: Where should investors begin?

Establishing a team: A framework for success

A regular topic of conversation with private equity funds is the sustainability of data and technology strategies within their portfolio businesses. The value creation opportunity is clear but the way to execute that opportunity isn’t always as transparent. 

For private equity investors a comprehensive data strategy is a clear value creation opportunity. What is less clear is the way to execute that opportunity: do you outsource or in-house? At what size should you consider investing in your own data science or engineering team? 

Equally, once you have delivered a standalone project, how do you ensure it is fully adopted by your portco teams? Without their uptake, it’s at risk of being seen as an academic exercise, with much of its ROI value lost.  

To ensure continued success when implementing strategic data initiatives, QuantSpark recommends the following framework: 

Identify or appoint a data “owner” within a target portfolio company to champion new data-driven ways of working and to guide external consultants  

QuantSpark’s view is that data science initiatives are as much about cultural transformation as they are the data itself. The project must be tied to a business objective – what’s the point in a having a perfect model if no-one uses it? – and the deliverables must be designed with the end user in mind. Too many predictive churn modelling projects are built to the sponsor’s specifications without taking into account the Customer Success teams who will actually be using it.  

It's often the case that delivering financial return from 1-2 initial data projects is key to winning further buy-in from your fund partners and the wider portfolio. So first, you need to identify a portco where that value can be recognised.

Consider each company’s maturity, existing data infrastructure, and appetite for change when considering where to begin.

Next, you will need to identify an internal champion, someone who recognises the value in data analytics and technology, who can both evangelise for the project and align internal teams with the fund and external consultants to facilitate success. 

Conduct a comprehensive landscape analysis of portfolios to identify further opportunities  

With a 1-2 key wins under your belt, you can begin to expand your approach to value creation through data across the portfolio. Repeat the same process above: consider each company’s maturity, data infrastructure and capacity for change to determine where to focus next.  

At this stage, with the support of the fund, you should look to appoint a data lead to act as the champion within the portco whilst identifying senior sponsors to support the work. C-suite and executives drive change by influencing stakeholders and unlocking resources. Projects succeed when senior leaders recognise the opportunity and help their company embrace new data-driven ways of working. These change agents shape project scope based on business priorities. 

Taking this approach will line up a roadmap of value creation opportunities across your portfolio. As a rule of thumb when considering that roadmap, quick wins build confidence while longer projects align with investment horizons. 

The size of your fund is a good benchmark for investment in a data team: 

Whilst developing the roadmap, consider the longer term investment and approach to delivering each project. The size of your fund is often a good benchmark for investment in a data team: 

• Large funds with substantial investments in mature companies will benefit more from outsourcing to consultants with expertise in enterprise analytics. Project requirements are greater, and portfolio companies likely have larger internal teams to oversee work. 

• Funds focused on a defined, repeatable segment of high-growth companies are prime candidates for developing an internal data science team. Work around churn, growth prediction and CAC/LTV analysis is similar across investments, and smaller companies have fewer internal resources. An in-house team reduces costs over many projects. 

• For diverse portfolios or smaller funds, a hybrid model with a small internal team guiding external consultants on strategy may make sense. The internal team helps oversee and “warm up” portfolio companies to adopt analytics but doesn’t execute the work themselves. They tap targeted expertise from consultants as needed for each investment. 

Remember: Align data initiatives to strategic growth initiatives

Ultimately a framework that considers your fund’s size and strategy whilst identifying and prioritising impactful data initiatives is the best approach to defining data strategy and investment in data teams.

At the same time, remember the key ingredients: whatever size fund, aligning data initiatives to strategic growth objectives and identifying champions to drive the project is the way to ensure success.  

QuantSpark partners with investment and portfolio managers to draw out those key objectives and design roadmaps to deliver real value. Through our QuantSpark Talent team, we embed sustainability into this planning, either through upskilling existing talent or augmenting the team with new hires.