1/ We have talked about VC firms investing into using data. Those investments have increased. Much of it is for perception but we are starting to see real impact.
I have also seen even greater investment by mega private equity firms. Why this matters.
I have also seen even greater investment by mega private equity firms. Why this matters.
2/ First you can find examples yourself by typing into Google “Head of data science ______” and fill in your favorite mega PE fund.
You know you have a fav…..
blackstone.com/the-firm/our-p…
linkedin.com/in/nataliyak/
kkr.com/our-firm/leade…
You know you have a fav…..
blackstone.com/the-firm/our-p…
linkedin.com/in/nataliyak/
kkr.com/our-firm/leade…
4/ Privately what I’ve been hearing from all corners of this world is that firms plan to invest meaningfully more into these DS efforts. Today investments range from 1-3 person efforts all the way up to 25 person teams w/ $20m budgets. Those with public arms often spend much more
5/ Why this is happening: Data has the potential to transform private investing - just as it has the public markets over the past 30 yrs (i.e. Renaissance Technologies, DE Shaw, Two Sigma). Sophisticated private managers see the transformation coming. The best are hungry for it.
6/ Less sophisticated GPs say “but data can’t look a CEO in the eye!” rather than focusing on all of the things data can do.
7/ In the past PE firms have differentiated on financial engineering (leverage levels - i.e. KKR, Blackstone), sourcing (i.e. TA Associates, Summit), value creation (i.e. TPG, Bain Capital), and fund size. Those days are gone.
8/ Today it is obvious that so much of the value chain is a) done in the same way it was for decades, b) outsourced to advisors. McKinsey/Bain helps with dili and develops a business plan, PwC helps with QofE and the model, lawyers help negotiate docs.
9/ Where is the alpha in the investing model if so much of the inputs are provided by external advisors, and the process hasn’t changed since Barbarians? Mega PE firms see the need for a new differentiator.
[Great book….less good movie]
[Great book….less good movie]
10/ So what is driving this transformation? Largely fear. Fear that their pts of differentiation no longer exist in sourcing, evaluating, helping co's post close. A data arms race has begun amongst private investors.
I heard once data is the new oil.
economist.com/leaders/2017/0…
I heard once data is the new oil.
economist.com/leaders/2017/0…
11/ So far, much of the value of the efforts is perception/marketing. It’s a shiny object to some LPs and to some management teams when the PE firm rolls in the data science team- or just insights driven by non-Excel based data.
But some efforts are also getting real value too
But some efforts are also getting real value too
12/ Whether real or perceived, the firms have found enough value that they have Hope. But they also have Fear -- Fear that they are going to be disrupted by a new kind of competitor bringing a completely new approach to PE using data. Both Fear and Hope are strong motivators. 
13/ Leaning in could mean Building capabilities internally. It could mean Buying capabilities through parent company acquisition. It could also mean Partnering with the right external group that could help with using data/technology to help with all aspects of private investing.
14/ For you home gamers, private investing includes:
-sourcing
-deal diligence & pricing
-winning the deal
-bespoke portfolio company level projects
-capital markets and exit
-operational excellence
-communication/relationship with LP’s
-Twitter
OK not Twitter.
-sourcing
-deal diligence & pricing
-winning the deal
-bespoke portfolio company level projects
-capital markets and exit
-operational excellence
-communication/relationship with LP’s
OK not Twitter.
15/ To quote one group- “we want to acquire our way out of oblivion.” The asset class must find a way to provide alpha. Let’s go into the Build option first (of Build/Buy/Partner solution possible set):
16/ I predict Building these capabilities internally will be a struggle. These incumbents, like stale incumbents before them, will look at internal data science investments as cost centers on a tight leash. They will want Yes/No answers, when DS usually plays in probabilities.
17/ The tech team will likely be treated as second class citizens- sometimes not located within the same city/state/country as the true power center. They will struggle to exert real influence on decision making. Building internally will inevitably create a dual class system.
18/ The investments required to influence any of the problems in the investing value chain will be meaningful and longitudinal. Eng and PMs must acquire data, build the infra and capabilities for DS to leverage the data, data scientists will build models to generate the insights.
19/ I know a top PE firm that today spends more on parking than they do data (literally). The investment required above will be a massive cultural shift to do from within.
20/ Creating disruption from within rarely works. Likely the firms that win this race will need to Buy or Partner/JV. It will be fueled by Fintech companies that are hungry to help create value in this massive industry.
21/ But to be clear, all incumbents have no choice but to do something.
They all need to get more efficient (read: cut costs) and they must innovate to differentiate.
They all need to get more efficient (read: cut costs) and they must innovate to differentiate.
22/ Ok that’s sillytown Ryan. These firms are cash machines- why would they need to cut costs?
Because fees are going down across the board in mega PE. They are being forced to customize more to create differentiation (at demand of LPs). Thus costs going up.
Because fees are going down across the board in mega PE. They are being forced to customize more to create differentiation (at demand of LPs). Thus costs going up.
23/ As margins are going down (mgmt fee/carry), there is a drive towards consolidation in the industry. Trying to further cut costs.
24/ As the consolidation happens, the mega PE firms are (pseudo) racing to be the first to manage $1T in private assets. But what holds that all together beyond a really great Investor Relations team?
A technology platform that acts as the backbone to the entire operation.
A technology platform that acts as the backbone to the entire operation.
25/ If they can make it work, a technology platform will unlock tremendous value for large firms spanning multiple asset classes.
Today these platforms are bound together by brand and a good IR team.
Today these platforms are bound together by brand and a good IR team.
26/ Tomorrow these could feed data back into a central platform that, when combined with external alternative data, would unlock new insights and capabilities. Have you heard of @blackrock's Aladdin?
blackrock.com/aladdin/offeri…
blackrock.com/aladdin/offeri…
27/ The tech platform could ingest data from all of their respective funds and port c's (and critically: prospective portfolio co’s). i.e. data pulled in by fintech VC group can provide perspective on disruption happening in financial services- critical for that $B dollar buyout
28/ Scale in PE has always come with declining returns. But that’s because, in part, there have been no network effects to scale in private investing. Could a technology platform- that perhaps had data network effects- change that?
29/ The logic would be that as the large private asset mgr feeds data from all angles into the tech platform, the platform gets stronger- with better insights. Allowing the private asset manager to create more value with that technology each passing year- as the platform scales.
30/ As it scales perhaps it can take on more of the value chain. Meaning at first perhaps it only helps with sourcing. Then after the technology develops enough, perhaps it helps with sourcing and due diligence. Yada Yada.
But that’s a lot to Yada Yada.
But that’s a lot to Yada Yada.
31/ Clearly I’m jumping over a ton of technological roadblocks. Not all business models are the same. Insights in 1 industry or 1 size range or 1 asset class, are often not applicable to others.
32/ In addition, not all problems will be solved with technology. Perhaps it helps in finding/evaluating companies, but not in post close. Or perhaps it helps with macro perspectives but less so on tearing apart company financials at first.
33/ The insights wont be automatic -perhaps ever and certainly not at first. It will take some probing and pulling and prodding.
I just said the same thing 3 times.
Darnit step up Ryan
I just said the same thing 3 times.
Darnit step up Ryan
34/ But even if the insights can’t be automated, the descriptive data could still help discretionary investors be more effective. Dare I say Quantamental..? And certainly more differentiated.
35/ There are also cultural roadblocks. Private investors tend to think they are the smartest person in the room - that is why they are entrusted with investing oodles of $. They typically don’t think their decision making can be replaced by computers.
36/ As a result, despite telling all of their portfolio CEOs to lean into technology - private investors might just be the last smart people in the world to adopt technology into their own business. That is changing.
37/ It will start by first augmenting decisions - not replacing them. Think Data Driven private investing but not Systematic private investing. Like the blindspot sensor on your car but not a self-driving car. Yet.
38/ There are so many human decisions that can be augmented, improved, replaced with tech. The Aladdin of the private markets is coming. It will elevate PE industry as a whole and hold a tremendous amount of value for the asset managers closest to the technology.
