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OODA was a technique coined by John Boyd, one of the leading military thinkers of the last 100 years, based on the German’s Blitzkrieg-style warfare which prioritized speed and surprise over the traditional win, hold and grind attrition techniques of trench warfare. After @pmarca tweeted about the concept, I read one of the books on the topic called Certain to Win.

Boyd’s thesis is that leaders of successful teams have to enable their organization to move rapidly, which means empowering people at all levels to make decisions. Speed is a huge asset in confrontations in both business and war, particularly when there is a substantial size difference between two competitors, so the author writes.

Given all the momentum of the NoSQL movement, it would be easy to write off SQL-based technologies as forgotten, or simply standing still. But there’s a tremendous amount of innovation occurring in SQL databases. Amazon’s Redshift, an elastic data-warehousing solution launched in late 2012 is the most salient example.

Redshift’s ability to process huge volumes of data is breathtaking. When running Redshift on solid state drives (SSDs), one team at FlyData queried 1 terabyte of data in less than 10 seconds.

Vik Singh wrote a great post in VentureBeat last week titled “Why Salesforce Needed to Buy RelateIQ” in which he talks about a new era in SaaS, the Predictive Era, the era of intelligent software. We’ve just seen one of the first acquisitions in the category with RelateIQ*, but I believe we will see many, many more for a few reasons.

First and most importantly, prediction provides competitive differentiation in an increasingly competitive market. It’s no longer sufficient in most horizontal SaaS categories to provide a cloud-based alternative with similar features to traditional software incumbents. RelateIQ showed this to some extent in CRM. The company uses natural language processing to reduce data entry. But this trend is as true for email marketing tools as logs analysis, for calendars as lead prioritization tools.

An entrepreneur asked me the question, what is the maximum viable churn for a startup? Within that question, a few others are embedded. How should a founder think about trading off efforts to grow revenue and mitigate churn? What is the impact of account growth on net churn? Startups must walk a tight-rope to balance growth, churn and cash. Below is the framework I use for working through maximum viable churn.
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Aside from a startup’s internal considerations about the right time to raise money, founders should weigh the seasonality of the fund raising market when planning their raise. There’s a rule of thumb batted around the valley that the worst times to raise capital are in the dog-days of summer and after Thanksgiving. As it turns out, this aphorism is only a half-truth.

Below is a chart of the dollars VCs have invested by month of year. I’m using Crunchbase data since 2005 for tech companies in the US. There are a few notable trends in the data.

Last week, I spent some time at HeavyBit, the community for developer focused companies in SoMa, chatting with a few companies reaching scale. Across a handful of meetings, a recurring theme surfaced for these B2D (business-to-developer companies).

How should their sales and marketing apparatuses be built? Do the field sales models of infrastructure companies or the inside sales models of software companies apply when the initial user is a developer?

When I worked as an engineer, I loved crafting code and feeling the satisfaction of having built something each day. But there was one thing about coding I never grew to love, despite its importance: forecasting my coding time.

Every two weeks, I trudged into a planning meeting that exposed my incompetent forecasting. During these meetings, each person in turn would review their commitments for the last two weeks and provide an update. Inevitably, I was wildly off. Chalk it up to inexperience, exuberance or ineptitude, but I never developed the knack.

While the phrase data scientist may be growing exponentially in its usage, and the number of data scientists job requisitions following a similar trend, the definition of the term is hard to pin down precisely.

I wasn’t sure I could define it well until I watched a talk by Hilary Mason, former chief scientist at Bitly, called Dirty Secrets of Data Science at a NYC meetup. During the presentation, she highlighted a chart created by the Data Community DC team that demystifies term data scientist.

As recently as six months ago, it was easy to disregard the Internet of Things (IoT) as just a theoretical market that Cisco measured in the trillions, but whose potential never seemed to materialize. That’s all changing.

The past year ushered in a new era for the Internet of Things for three reasons. First, venture capitalists invested nearly $1B of capital in the IoT in 2013, more than 3% of all VC investments by dollars. Second, the sector witnessed its first IPO, Control4. And third, Nest’s sale to Google last week set the high water mark for IoT acquisitions, measured at more than $3B, firmly establishing the category as a strategic imperative for the world’s largest technology companies.

Each year the National Venture Capital Association and Thomson Reuters release data characterizing the state of the startup market. I’ve analyzed the 2013 data and there are three important trends I observed. All in all, the startup exit market is quite healthy.

1. Startup exit values are increasing more than 7% per year, on average.
2. The number of exits is flat-to-down during the ten year period I studied.
3. The public markets have opened to startups again, doubling their share of exits.