Due Diligence: Seven Bridges Genomics (Part 2)

Continuing with the top-down analysis from Part 1, lets look at the cloud genomics analysis industry with a focus on macro-scale phenomena. Seven Bridges Genomics, or any other individual firm for that matter, won't be able to do much about these, other than role with the punches.

In a future post, I'll go micro and drill-down to the unique selling points, enduring competitive advantages and economic moat that make Seven Bridges Genomics value proposition durable and secure (aside: hopefully).

Value Proposition

Provide the tools for scientist to do analysis without having to worry about the details of (1) compute/IT and (2) standardized work-streams.

Those are some hefty assumptions...

(1) Assumes scientists are compute limited.

(2a) Assumes there is a value-add in standardized work-streams

(2b) Which then, in-turn assumes, that there exists standard work-streams.

Making Money

As a private company, I can't do a deep-dive into their financials (aside: woe!), so I have to make some assumptions. From the marketing there seem to be two potential revenue streams...
(A) 'Compute Spread,' basically an interest rate spread but for AWS CPUs. The justify their mark-up over AWS compute pricing based on the perception of value-added. Note that this is a subclass of software as a service.
(B) Consulting

(A) must necessarily dwarf (B). Traditional consulting doesn't scale, which dooms a tech company before it can gain its sea legs / line-cross / other nautical right of passage, i.e. shark VC money. Consulting firms can bootstrap, but that doesn't seem like the growth trajectory they're going for.

 So for simplicity, lets reduce to (A). Taking the spread comes with both top-line and bottom-line risk. 

 Paramount amount them, the bottom-line risk of becoming an AWS whipping boy. You can scream for mercy, not that it helps. Honestly, other than try and take the compute in-house or trade masters.

In-house: Manage to do it even comparable to AWS...ha! 
Trade Masters: High switching cost...if it comes to this, were doomed a long time ago.

On the top-line, they need to either work in an highly inefficient market (alas, big banks) or continuously justify the spread they take through value-add. As I mentioned in the previous post, there is loads of competition with no clear market leader. Market structure will not save them, so value-add they must maintain, less open source eats their lunch.

Macro Swallow

The internet meme of near-misses between whales and humans, including such precious lines as “You’re gunna have to do more than clean that wet suit bro” [Youtube] are the impetus behind this section.

It IS a big ocean; however, there are lots of fishies $£€ to be had in a quite restricted space, the wind-up to a feeding frenzy. There are many ways to die.

Last post I based my mental model on drivers and constraints, but this time around a framework based on relative growth rates seems more suitable. A swallow, in this context, means the facet of growth that trumps the others.

Data swallow

Fail: Value Proposition 1

I/O swallow:

Problem: Impractical to upload data to cloud.

Solution: Co-locate with sequencing centers; however, this requires a) consolidation in sequencing industry (mass-market) or b) working with and servicing big co's exclusively.

Prognosis: Not great. a) Is survivable, but may kill the growth curve. b) Basically become just another IT integrator / service provider. Not scalable. Both mean having an additional whipping masters (AWS + core/big co). 

Storage swallow:

Problem: Impracticable to store data.

Solution: Stream data to be processed in real-time.

Prognosis: Would actually be a boon for Seven Bridges if they could solve the streaming and real-time analysis, as it enhances the value proposition.

Compute swallow

Fail: Tech swings against you

But personal processing power grows even faster:

Problem: New algos or technology lower the compute burden, making the cloud unnecessary. Can go back to on-laptop analysis, where other established firms, e.g. Acelrys, may well eat your lunch.

Solution: Go toe-to-toe away from the cloud. Convince that cloud is worthwhile for other reasons (hassle free, a la Google Docs).

Prognosis: If desktop, grim (infrastructure re-boot). If cloud, fine.

But processing doesn't grow fast enough:

Problem: Can't make money off the AWS spread because tasks are sucking too much compute

Solution: Hope parallelism and clever algo saves you, otherwise...

Prognosis: If AWS can’t do it, neither can you most likely. Hosed.

People swallow

Fail: Value proposition 2

Problem (2a): Scientist don't value your workstreams.

Solution: Hope your compute value proposition holds.

Prognosis: If your API doesn't suck, they build there own in your sandbox IF the compute justification is strong enough. Will become niche for low-end / small-time users, as more sophisticated users disintermediate you and take their algo straight to compute.

Problem (2b): Model fails since there are no standardized workflows. Everything must be custom/application specific.

Solution: Turn into a consulting company.

Prognosis: No scale. Either turn niche, or eaten by a bigger consulting fish with scale in consulting.

Takehome

If you're placing a positive bet on the cloud genome analysis industry, not just Seven Bridges Genomics in particular, you're taking a few implicit assumptions...

1. I/O swallow will not kill the industry in the cradle.
2. Compute challenges are Goldilocks.
3. Bioinformatics is amenable to automation and cross-application standardization.

I'm fairly confident of an all-clear on (2) and (3), but (1) worries me. There are solutions here if the company can pivot fast enough, but I'm not convinced that a start-up, as opposed to a core/big co, has the leverage to pull it off. 

There is also a get out of jail free card...alternate value propositions. 

One that sits quite well for the cloud is integration between different datasets, a task made much easier once all this disperate data is sitting on servers you control. One can imagine mining other peoples data and selling insights. This is NOT consulting in the traditional sense, but scalable returns from data integration and automated analysis.

Only time will tell...

Due Diligence: Seven Bridges Genomics (Part 1)

"Demonstrate your learning capabilities," how exactly to do that, I wondered. Develop a mental model! 

I've spent the past few hours reading about the field of genomics & next-gen sequencing, with respect to one firm: Seven Bridges Genomics. 

First I developed a sense of...

• Promise -- Hard: Routine genomic diagnosis; Harder: Personalized Medicine
• Problem -- Next-gen sequencing Data → Actionable Results
• Solution -- ???

After that, I was bit stuck. How can one summarize an entire field with one mental model, one graphic. 

I thought about...

• Competitive landscape (SWOT)
• BCG Matrix (Definite with ? for most firms)
• Key players (Companies, People, Locations)

But none of those are quite it. What is the root cause of the Problem. Here is a perfectly, imperfect mental model (all mental models are wrong, but some are useful) that seems to be working for me...

I think it comes down to drivers and constraints. Drivers being those things that push a technology forward, which of course require some metric to track changes in status (italics). Constraints being the rate-limiting resource which most hamper development, also complete with metrics (not-shown, but examples would be number of distinct technologies in the pipeline vs. maturity/ETA, cost per base-pair).

Each aspect of the ecosystem, from sequencing → assembly → analysis, has its own unique set of drivers and constraints. 

Now, is there a rate-limiting step in the ecosystem as a whole?  If so, that's as good a place as any to begin with high-impact solution...

• Sequencing -- Cost and time is already falling, with a healthy pipeline of new technologies (i.e. not a 'Pfizer').
• Assembly -- Incremental improvements in Robustness and speed. Throwing more compute (cheap!) at it generally seems to do the trick.
• Analysis -- More data (sequencing & assembly) don't seem to be resulting in more actionable insight. Ding ding. I think we have a winner.

One thing that may temper going for the rate-limiting step is relative easiness of attacking other problems first. They're all hard problems, so lets stick to our guns and go with rate-limiting.

Which brings us full-circle, back to Seven Bridges Genomics and their solution, Igor, a cloud-based analysis framework.

The software is constraint-oriented, knocking down barriers to compute and people. Let our clever architecture and Amazon Web Services (AWS) take care of the computational scaling. Let our clever bioinformaticians do the heavy-lifting, standardizing workflows for common problems, adapting and scaling existing solutions and maybe even banging out something completely novel.

The result -- time and cost savings due to the experience curve effects, standardization and economies of scale. Awesome, no?

It remains to be seen whether they can compete effectively. It's a crowded space, with no clear market leader; but that's a story for Part 2. Other takes here and here.

PS. I also quite enjoyed the play on the Seven Bridges problem (aside: at least I think it's intentional). Change the graph, e.g. bombing a bridge -- which is more or less what they hope to do with the analytics end of things, -- and you can force a solution.

Blogger posting with Markdown using StackEdit

I recently discovered StackEdit, a tool for writing and previewing Markdown.

Now, I’ve been using Markdown for quite sometime, it’s useful for everything from electronic lab notebooks to taking notes on informational interviews.

Finally, through StackEdit’s built-in post to Blogger feature, I’m able to abandon the clunky default interface, and write posts the way they were meant to be written!

Additional benefits include easy code snippets…

def stackedit():
    print 'I love your product!'

Easy inspirational quotes…

“Perfection is Achieved Not When There Is Nothing More to Add, But When There Is Nothing Left to Take Away”

And easy equations (same syntax as $LaTeX$, by the way)…

$\Delta G = \frac{1}{2} k_\mathrm{B} T ln(K_\textrm{eq})$

Needless to say, I’m excited about the switch, and would highly recommend giving StackEdit a try yourself!

Due Diligence: Tessella

I like to do a bit of public due diligence on companies of interest. Here's a brief example of some highlights from the dossier...

Tessella is a medium-sized technology consulting company that hires a small intake of talented developers each year, many of whom have PhDs. I'm interested in Tessella, and where their past employees have gone. I'd want to use a little LinkedIn based analysis tool for this data-dive [source], but until I get access, I'll have to do it the old fashioned way (read: manually, without a slick API...the humanity!)

Macro trends

At the macro-level, massive turn-over is a negative, but if no one ever leaves, that could be bad too. You'd expect at least a few consultants to fall for a client, and go work for them; it's how ideas spread in a knowledge ecosystem. I'm specifically interested in the Boston-office, so lets compare Boston to total...

Data

Gender assignments are based on name/picture. Some of the gender numbers don't add-up due to the unavailability of this information.

Tessella, All Offices

Past: 276

Current: 193

Current + Past: 36

--> Left company: 240

--> Leaver/Current: 1.2

--> Promotion/Unpromoted: 0.23

Tessella, Boston

Past: 17

Current: 13

   Men: 9

   Women: 3

Current + Past: 2

--> Left company: 15

   Men: 13

   Women: 1

--> Leaver/Current: 1.3

--> Promotion/Unpromoted: 0.18

--> Men/Women: 22/4 = 5.5

No excess turn-over in Boston office

Self-explanatory, but some caveats...

1) Boston office is 10 years old, compared to 30 for the company as a whole; however, LinkedIn has a bias towards more recent events. A priori expect leaver/current to be higher for All offices.

2) The company has grown massively, so most of the people that have ever worked for Tessella have worked there is the past 10 years. Negates (1)

It's a wash; I'd say they're comparable.

Internal promotions are consistent between Boston and the rest of company

0.23 versus 0.18. I'd say these numbers are roughly the same given:

1) Small sample size for Boston

2) LinkedIn quirk. Not everyone listed a promotion as a separate job. I'd only detect a promotion if the person put in a separate entry. I personally know people that don't do this, thus 20 year tenures in their most senior position (for a 40 year old).

3) I wish I had a base-line metric for internal promotion. I'd be interesting to compare Tessella to peers.

White Male dominated

22:4 Men:Women for the Boston office. This is technology consulting after all. No surprises there. Of the male consultants, past and present, all but two are white. Curious how gender and diversity compare to peers. (Vet me LinkedIn!...you don't seem to present gender/racial data to the API, so I'd have to have some fun!)

Micro trends

I'm interested in where people did before Tessella, and where they go afterwards. With access to the LinkedIn API (please give me vetted access!) I could run an analysis on everyone in the company. There are 240 leavers, which is do-able by hand, but 1) I'm lazy (in the good way) and 2) to get a sense, I probably don't need to sample everyone. I'm specifically interested in the Boston office, so that's where I'll start...

Data (with homebrew classification)

Note: I made the bolded function/company classifications to help organize the data. There are of course other-ways to organize. There are only 14 people since I can't access information for the 15th.

Function...

Software Engineering: 5

Informatics/Analysis: 5

   Bioinformatics: 1

   Cheminformatics: 1

   Data Science: 1

   Industry R&D: 1

   Tech Consultant: 1

Project Management: 3

   Project Manager: 2

   Account Executive: 1

Self-employed: 1

Company...

Life Sciences Research: 2

   Broad

   Dana-Farber Cancer Institute

Life Sciences Companies: 4

   Life Technologies

   Novartis (2)

   PerkinElmer

Big Technology: 4

   BBN Technologies (Raytheon R&D)

   IDBS (IT consultancy)

   Microsoft (2)

Small Technology: 3

   Complete (Digital Marketing)

   Extreme Reach (Video ads)

   Tokyo Electron (Semi-conductors)

Finance: 1

   HighVista Strategies (Asset Management)

Some range in functional exits, but they are all technically-aligned.

10/14 exits are day-to-day technical. The 2 project managers work at NIBR (Novartis Institutes for BioMedical Research) and Life Technologies, so I'll assume that they are managing technical projects. The account executive works for IDBS, so I'll assume he's selling and overseeing technical projects. That means substantially ALL of the exits are technical. The biggest jump this group has made was to technical sales and support (account manager). It IS possible to not program day-to-day, but don't expect to stray too far from the technology function.

Some range in industry, but it's really either life sciences or tech.

6/14 in life sciences. 7/14 in technology. It's Boston after-all, I suspect the UK exits would be less life sciences dominated. Within the life sciences and technology silos, there is some diversity between Fortune500, research institutions and SMEs. Some are small, but I wouldn't consider any of them start-ups. I'd be interested in talking to the one outlier in Finance. Don't worry, he's still a techie.

Observation: People Leak on LinkedIn

I wasn't explicitly looking for this, but folks leak loads of information on LinkedIn. If it were possible to scrape and analyse this information, it would be possible to independently audit private company numbers accounts, or infer them if they don't release.

From reading profiles on LinkedIn, I inferred that Tessella will earn £21M for 2013. This is spot on with Tessella's own reporting [source], which (scouts honour) I did not view before the data-dive.

Information I observed from looking at profiles...

• 2014: 250 people.
• 2014: 30% PA growth in life sciences. 
• 2011: His (two) offices (of 8) contribute ¼ of total revenue.
• 2011, 210 staff. 160 earning, 50 admin.
• 2011: Consulting is growing 20% PA, ⅕ of revenue. US is 17% of revenue.
• 2011: Oversaw 37 staff, 4M GBP in revenue. 
• 2010: Grew office to >20, >2M GBP
• 2010: 88% of revenues are repeat business

How to estimate  £21M for 2013

- £4M from his office, which is ¼ of company = £16M in 2011

- 30% life sciences...emphasizing that this is higher than average. Consulting is 20%, that it's mentioned implies it's higher than average. Let's assume 15% PA growth as a modest guess.

- £16M in 2011 * 2 years at 15% = £21M in 2013

Simple example, but with a fire hose of data being automatically parsed, it may be possible to glean much much more.

Other LinkedIn Estimates

3:1 Tooth:Tail

£100k/consultant in revenues

Financials

Overall, the information available in the financials [source], line-up well with LinkedIn. Good to know that folks are bending the truth on their profiles.

There is so much gold in financials. I'm like a kid in a candy shop when I read through them. Not entirely sure it's normal, but I get positively giddy reading annual reports (financial tables first, naturally).

Many SMEs live hand-to-mouth; that's ok for start-ups, where you should be being compensated with a risk premium, but is inexcusable for an established SME. Not the case with Tessella, but it's a good idea to double check.

5:1 Tooth:Tail

(191:40   Billable:Non-billable)

Not sure how this compares to peers. My gut told me the LinkedIn estimate was a bit low. Went and checked. My gut was right. More tooth...woo!

Revenues are £110k/consultant

(21M / 191 consultants from 2013 annual report)

I think this places a natural cap on what a consultant could ever expect to earn. I'm guessing this is ball-park for a tech consultancy shop. Operations houses are pulling in 1.5-2x, while strategy houses are likely billing 3-5x.

Compensation Structure

Ok, you can't get this from the LinkedIn data...

• £25k     Low
• £45k     Median (est)
• £65k     Mean
• £160k   High

Annual report is £12M in wages (+£600k in pension cost) over 191 consultants. That's £65k/consultant in earnings versus (£21M revenue)  Highest paid director at £160k. Entry-level consultant at £25k. A 6x high-low multiple is actually fairly reasonable in this day and age. Though, the directors did gorge themselves a bit in 2012; suspect this was related to the management buy-out though. If you take the distribution of US household income as a guide, the median is about 2/3 of the mean, giving around £45k median wage.

Conclusion

If you're a tech-head, who wants to become/remain a male (kidding) tech-related-head with a technology or life sciences company, Tessella offers good exits. If you wanted to break into other areas of consultancy, say operations or strategy, or into another non-technical functions, one should probably look elsewhere. Loads of money, a la finance, also look elsewhere. 

To their credit, what I've found is exactly what's written on the tin. Tessella doesn't sell anything more, or anything less, than a solid technical training ground for new hires. Solid pass on the basic DD.

Hoping for LinkedIn Vetted API access

Just submitted a request for Vetted API access [source] on LinkedIn to do a little research project on transition probabilities. Signing up as a developer [source] only gives you access to company search and your profile and basic information from 1st degree connections.

I understand the reasons, both noble (e.g. protecting user privacy) and ignoble (e.g. enforcing a closed ecosystem, like they did with CRM, which is quite frankly evil [source]). Will just say it's more hassle than I was expecting for a simple research project.

Basically, I want to identify: 

• People like me (starting from me)
• Where they came from.
• Infer where they are likely to go.
• The co-variables that are strongly correlated (positive or negative) to particular transitions and joint-transitions.

Here's hoping that LinkedIn will provide access for my little research project. There isn't much I can do with basic profile information from 1st degree connections; need the entire network (~200k people) and non-basic profile information such as companies/universities attended to really get going.

The previous post on Tessella [source], is a very basic example of what I want to do (on a much larger scale of course) if I get vetted.

Will be using LinkedIn Python API [source]. As an aside, the code itself is beautiful. 

LaTeX versus Word for Resumes (Part 2)

A year ago, I made the switch to LaTeX for my resumes [source]. The absolute dominance of LaTeX over Word for long structured documents, like my doctoral thesis, motivated the change.

Now that I've used LaTeX resumes and cover letters for about a year, I thought I'd share some insights. In that time, I've discovered a few surprises you should consider before making the switch:

PRO: Comments are your friend.

One of the beauties of LaTeX is that it is a markup language. Ideally, you can work from one master resume per industry, and use comments to quickly customize. Since the document is structured, in terms of sections and experiences, it's also trivial to reorganize, especially with a folding text editor (Plug: I use vim with a souped-up .vimrc).

NEUTRAL: Quickly change layout and static elements

Again, since it's a structured document, changing your resume format is quite easy; however, in the year since I modified the res.cls to suit me, I have not once changed the styling. Likewise, by inputting your header and other (relatively) static information from another source file, you can very easily propagate changes through to your industry-specific documents. I have modified the header precisely once, to include this blog's URL.

NEUTRAL: Time vs. Effort

Once you have a working template, you will never have to worry about spacing and alignment again. Unfortunately, the start-up time for LaTeX is far longer than for Word. On the whole, I'd say it's a wash.

CON: Recruiters WILL request a MS Word version of your documents.

This absolutely baffled me. I send you a beautifully presented, structured PDF, and you ask for Word -- arbiter of broken formatting and non-portable character sets.

CON: Companies WILL send you Word documents to fill-in

More bafflement. Despite the abundance of streamlined recruiting platforms (large companies) and LinkedIn/email (start-ups), a surprising number of large companies will ask you to fill-in your details, for the Nth time I may note, in a company-specific Word document. To add insult to injury, many also contain non-backward compatible macros. Personally, I avoid Windows boxes unless a company insists on a Visual-studio development environment, so this has the additional complication of using Word through Wine (limited macro support) or Open Office (very screwy formatting).

CON: Job engines WILL ruin your formatting.

pdflatex generated PDFs are fully searchable, yet job engines -- takes a moment to glare at Jobvite, Monster and USAJobs -- discard the human-readable PDF and make a happenstance conversion to raw text. A part of me understands why a firm would want to standardize to ASCI: saves disk space, eases parsing, etc. However, it's more or less an admission that a human will never read your materials. PDFs are far more human friendly, yet they are discarded. Actions, not words, judge ye by.

Hack Solution: ASCI Resume

I hate to admit it, but a way around the cons is to keep an ASCI resume. Unfortunately, this obliterates many of LaTeX's advantages. You can either preserve the .tex as a single source of truth and  convert, or maintain two independent documents. Shameful to admit, but this is the route I take on an as-needed basis.

3rd Way: Markdown

I haven't explored this option, but a single source of truth in Markdown using Pandoc could work brilliantly.

PS. My industry specific LaTeX resume examples are posted to [GitHub].

Must Read Review of Piketty by Milanović

Piketty’s "Capital in the 21st Century" has been all the rage of late, since the English language translation was released in April with articles hitting the FT, Economist, WSJ, and pretty much every other publication, high and low.

I haven't had the delight of gorging on all the econometric goodness just yet, but I have read a goodly number of reviews, the most technically advanced, while still lay readable, of which is "The return of patrimonial capitalism: Review of Thomas Piketty’s Capital in the 21st century" by Branko Milanović: http://mpra.ub.uni-muenchen.de/52384/

 I thought the review was better than its pulp counterparts on several points:
- Outlines the algebraic mechanics of Piketty’s models
- Isn't afraid to present a data dive to support key points
- At 20 pages, presents succinct coverage of a mammoth book without being curt.

As an English review of the original French, it's been out for sometime. Recommended for those of us who haven't gotten round to the original just yet. As for me, it'll be my post-thesis-submission treat.

Sad, but true: Given the uncontroversial assumption that there is no political will to slow, let alone reverse, wealth condensation and the further rise of the rentier class, the only personally actionable advice is to 'marry well.' Unfortunately, if you're not 'so endowed' yourself, good luck indeed [source]. In the meantime, I think I'll read up on Rastignac. Gotta love the public domain [source]

PS: Full disclosure, I do have a university background in economics and econometrics, but I really don't think academic training is a prerequisite, just a healthy interest in the dynamics of capitalism.