AI News, Doing Data Science in a Startup: The Hard Truth

Doing Data Science in a Startup: The Hard Truth

Knowing that startups often keep a single data scientist on staff, my goal for this post is to pass on my personal experience: the thoughts and challenges I’m facing on a daily basis, with the hope that some of you fellow data scientists out there could relate to it.

To be honest, I’m willing to bet that, on average, data scientists are much less of a social being than marketers or professionals in almost any other field for that manner – so don’t be shy, talk with people in the field and try to learn as much as you can about your new domain.

As so eloquently described in Uri Alon’s TED talk, scientific research often means being stuck in a cloud of uncertainty and confusion, and this is a much more stressful state when you’re in a startup and expected to deliver quickly.

When available, I use open source tools rather than trying to implement my favorite machine learning algorithms by myself, as fun as it might be (sometimes, of course, there’s simply no open implementation of the desired algorithm).

If you’re lucky, the startup environment offers a resource you almost never have in academia: a great team of developers at the top of their game, ready to take your core idea, wrap it up and integrate it into the running system quickly and efficiently.

Of course, with great power comes great responsibility: being the single data scientist in a startup, it’s solely up to you to lead the machine learning effort –

Many times, I’ve had to give a reality check when it comes to what can or cannot be done, what could be expected from a predictive engine, how little data might be considered statistically significant and various other kinds of wishful thoughts.

This begs the question: should startup-style research be considered “research?” Frankly, I’m not sure, but here’s what I think: If your work involves combing through and assessing mountains of academic articles, then experimenting with merging your own dataset and ideas with the algorithms described, then research it is.

Let me explain: as we’re entering the dawn of ML and BD (and clearly this is just the beginning), we are giving up our deep understanding of the underlying phenomena and processes in favor of our ability to predict future behaviors/events and make things more efficient.

From Newton through Einstein to Schrodinger (pardon me for being a physicist at birth…), in the past few centuries, science has advanced humanity and taken the center stage away from religion as it brought two gifts to us humans: it brought us understanding of the examined domain (nature) and, with it, came predictive ability.

Understanding the Tech Startup Investments and Partnerships of Munich Re and Swiss Re

In 2016, we put together the following chart highlighting the pace of investments and partnerships by Swiss Re and Munich Re, the two largest reinsurers in the world:

Swiss Re’s tech strategy aims to access new risk pools and manage existing ones more effectively.” Here we take a look at the different investment and partnership strategies Swiss Re and Munich Re have taken with technology companies.

These include industrial IoT platforms Relayr and Mnubo, which created a commercial partnership with Munich Re to create risk management products for Mnubo’s customers making IoT investments as well as WePredict, which planned to co-develop an insurance solution with Munich Re backed by the auto warranty analytics startup’s risk calculations.

Investments In December 2017, Swiss Re CEO Christian Mumenthaler told the FT he was skeptical of the potential for startup investments, explaining: “If we see a start-up that can help us, either it’s very strategic and then we would buy them 100 per cent or copy what they do, or if it’s not that strategic then I think we can collaborate with them.

In addition, Swiss Re will reportedly support the use of LifeScore360 in life reinsurance transactions in the U.S. SpatialKey: In February 2018, the Denver-based company, which provides a geospatial insurance analytics platform, integrated Swiss Re’s CatNet hazard data directly into SpatialKey, giving insurers using the platform access to 10 hazards including flood, earthquake, tsunami, wind, and hail.

On the digital insurance side, Swiss Re has partnered with US-based Coalition, an MGA startup that provides up to $10M in cyber liability coverage, as well as App in the Air (which teamed up with Swiss Re and Chubb on a real-time flight delay insurance) and Cuvva (which partnered with Swiss Re to offer pay-as-you-go car insurance to drivers in the UK.

American Entrepreneurship Is Actually Vanishing. Here's Why

And, while technology is young people’s oxygen, risk may be their carbon monoxide.

In 2014, just 34 percent of 25-to-34-year-olds said fear of failure would prevent them from starting a business, down from 41 percent a year earlier.

“The fear of failure among 25-to-34-year-olds can reflect a greater level of caution, and a preference for more stable employment when there is high uncertainty and a less favorable environment for entrepreneurship,”

From Alibaba to Zynga: 28 Of The Best VC Bets Of All Time And What We Can Learn From Them

After we compiled our list of startup failure post-mortems, one of the most frequent requests we got was to use these posts to figure out the main reasons startups failed.

As Keith Nowak writes in Imercive’s post-mortem: “We were caught mid-pivot – half way between a strategy we knew wouldn’t work and one which we believed could be successful but was not able to be aggressively pursued.

Burn out was given as a reason for failure 8% of the time The ability to cut your losses where necessary and re-direct your efforts when you see a dead end was deemed important to succeeding and avoiding burnout, as was having a solid, diverse, and driven team so that responsibilities can be shared.

Things were going great …The problem we would soon find out was that having hundreds of active users in Chicago didn’t mean that you would have even two active users in Milwaukee, less than a hundred miles away, not to mention any in New York or San Francisco.

There are many good ideas out there in the world, but 9% of startup post-mortem founders found that a lack of passion for a domain and a lack of knowledge of a domain were key reasons for failure no matter how good an idea is.

As MyFavorites wrote at the end of their startup experience, “Ultimately when we came back from SXSW, we all started losing interest, the team was all wondering where this was eventually going, and I was wondering if I even wanted to run a startup, have investors, have the responsibility of employees and answering to a board of investors.” If you release your product too early, users may write it off as not good enough and getting them back may be difficult if their first impression of you is negative.

For instance, eCrowds, a web content management system company, said, “We spent way too much time building it for ourselves and not getting feedback from prospects — it’s easy to get tunnel vision.

I’d recommend not going more than two or three months from the initial start to getting in the hands of prospects that are truly objective.” Similarly, VoterTide wrote, “We didn’t spend enough time talking with customers and were rolling out features that I thought were great, but we didn’t gather enough input from clients.

That would be another stopper if we dealt with the problems mentioned above.” Failed founders seem to agree that a business model is important – staying wedded to a single channel or failing to find ways to make money at scale left investors hesitant and founders unable to capitalize on any traction gained.

Looking back I believe we needed to clear the decks, swallow our pride, and make something that was easier to have fun with, within the first few moments of interaction.” Pricing is a dark art when it comes to startup success, and startup post-mortems highlight the difficulty in pricing a product high enough to eventually cover costs but low enough to bring in customers.

Plans based on the accumulated duration of recordings make much more sense for us and the number of subscription showed.” Despite the platitudes that startups shouldn’t pay attention to the competition, the reality is that once an idea gets hot or gets market validation, there may be many entrants in a space.

Mark Hedland of Wesabe talked about this in his post-mortem stating: “Between the worse data aggregation method and the much higher amount of work Wesabe made you do, it was far easier to have a good experience on Mint, and that good experience came far more quickly.

Everything I’ve mentioned — not being dependent on a single source provider, preserving users’ privacy, helping users actually make positive change in their financial lives — all of those things are great, rational reasons to pursue what we pursued.

As Nouncer’s founder wrote, “This brings me back to the underlying problem I didn’t have a partner to balance me out and provide sanity checks for business and technology decisions made.” Money and time are finite and need to be allocated judiciously.

As the team at Flud exemplified, running out of cash was often tied to other reasons for startup failure including failure to find product-market fit and failed pivots, “In fact what eventually killed Flud was that the company wasn’t able to raise this additional funding.

Despite multiple approaches and incarnations in pursuit of the ever elusive productmarket fit (and monetization), Flud eventually ran out of money — and a runway.” Tackling problems that are interesting to solve rather than those that serve a market need was cited as the No.

We had great technology, great data on shopping behavior, great reputation as a though leader, great expertise, great advisors, etc, but what we didn’t have was technology or business model that solved a pain point in a scalable way.”

4 Factors That Predict Startup Success, and One That Doesn’t

Some of the most interesting data on this question comes from an analysispublished last year by the venture capital firm First Round Capital.The firm’s unique data set comprises information on over 300 companies and nearly 600 founders, including founder characteristics such asage, gender, education, firm location, and prior work and startup experience.

it’s a timely reminder of the importance of increasing female entrepreneurship and of the opportunity that VCs may be missing by continuing to disproportionately fund white men.

The data also shows that younger founders and founders with prestigious educational backgrounds or prior experience in large technology companies tend to be more successful.

This sort of data doesn’t substitute for judgment, but to the extent that it can help investors identify things that track with success, data can inform those judgments.

Although female-founded companies represent a greater percentage of First Round’s investments than the national average —startups with at least one female cofounder account for approximately 18% of new VC-backed new ventures in the U.S.— they were still the minority of investments.

(For this analysis, performance refers to the change in market valuation between the initial First Round investment and the end of 2014.) Three of First Round’s top 10 investments of all time, based on value created for investors, had at least one female founder, far higher than the percentage of female tech founders in the data set.

Experience at top tech companiespredicts success as a founder.Before jumping into the startup fray, newly minted graduates should consider a stint at a marquee technology company.

(Interestingly, while going to an elite school correlated with higher financial returns, it did not correlate with a higher pre-money valuation, perhaps suggesting that investors do not view education as quite so effective a pre-screening measure.) The Amazons of the world expect quite a lot of their employees, from technical ability to time spent at the office.

First Round has been alerted to high-potential investments from a wide variety of sources, includingTwitter andin-person pitch gatherings such as”Demo Days.” These nontraditional sources yielded companiesthat outperformed referred companies by 58.4%.

In over 300 investments, we have observed some patterns: The importance of female entrepreneurs in a traditionally male-dominated industry and the benefits of a good education and pre-startup experience are clear.

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