Imagine with me: what if novels were written like software. Sometimes it’s useful to approach absurdity and look inside. There might be treasure there. I’ll define software as an executable, a set of instructions, that are interpreted by a machine for some reason. As a data scientist, I think of software as a product, and I think, constantly, of turning data into product. I think of data as inertia and all the code around it as flexible. I worry a lot about the people that use the software (if anybody) and think of them as heterogenous segments. I think of a novel as an executable, a set of instructions, that are interpreted by a human brain for some reason. As[…]
Category: Big Data Science
Jon Evans wrote a piece for Techcrunch entitled: After the end of the startup era. In it, Evans writes: We live in a new world now, and it favors the big, not the small. The pendulum has already begun to swing back. Big businesses and executives, rather than startups and entrepreneurs, will own the next decade; today’s graduates are much more likely to work for Mark Zuckerberg than follow in his footsteps. And, Because we’ve all lived through back-to-back massive worldwide hardware revolutions — the growth of the Internet, and the adoption of smartphones — we erroneously assume another one is around the corner, and once again, a few kids in a garage can write a little software to take[…]
An orthodox Software as a Service (SaaS) business is, in part, math that an organization tries its best to manage. Think about all the math that goes into the construction of a typical SaaS firm. At the core there’s some customer with a job: a goal against which the customer wants to make progress. They can have a mathematical representation in a database somewhere. A bunch of technologists write some code, which is all math, and a bunch of creatives take a few photographs, which expresses itself a mathematical representation, and some data is Created Read Updated and Destroyed in a database somewhere, which is all just more math. And it’s all abstracted by yet more math at the processor[…]
The other I likened the process for taking apart a Job To Be Done to taking a part a lobster. There’s a very effective way to decompose any problem with enough energy. And then I watched The Founder on Netflix and admired the McDonald brothers using a classic technique in management science to refine a system on a tennis court. And I loved it. They really refined hamburger and frenched fry delivery. And then this morning I read that Andrew Ng in working on a new coursera course for AI. And I’m thankful for his initiative and optimism. Out of those three threads, this one post. The Assembly Line The assembly line was an American invention for Americans. It could[…]
Into the trough of disillusionment with the hyped technologies! The canary in the coal mine for me, with respect to BitCoin, is this post. Look, nobody has enjoyed more popcorn around BitCoin than I have. From Coinye to Dogecoin, crypto-currencies have delivered the lulz. Do I believe there’s a slope of enlightenment for crypto-currency? Absolutely. Do I believe that’s imminent? Nope. Banks are apex ruminants. The lessons from BitCoin have to be fully digested before something really good comes out of it. Machine learning. Huge potential and the best is yet to come. The first wave around machine learning gave us Netflix and Amazon. And then the bloom came off the rose a bit. And now there’s deep learning and we’re[…]
This piece from McKinsey highlighted the inflated expectations of big data analytics – “…expectations of senior management are a real issue…but too often senior leaders’ hopes for benefits are divorced from the realities of frontline application. That leaves them ill prepared for the challenges that inevitably arise and quickly breed skepticism.” The listicle (et tu, McKinsey?) summarized below, is somewhat related to that concern: 1. Data and analytics aren’t overhyped—but they’re oversimplified 2. Privacy concerns must be addressed—and giving consumers control can help 3. Talent challenges are stimulating innovative approaches—but more is needed 4. You need a center of excellence—and it needs to evolve 5. Two paths to spur adoption—and both require investment (automation and training) In a fit of[…]
This is a lot of inside baseball. The motivation is to share information while acknowledging that it’s wildly anecdotal. It’s directed at data scientists thinking about business. The Facts Andrew and I founded Authintic in late 2012. We landed three great customers. We met between 1,600 and 1,900 well wishers, competitors and prospective customers. Five major market hypotheses were tested. Revenue was earned and value was generated. Authintic was acquired by 500px in early 2014. The Feels Thrilled. Very excited. And a tad skeptical about the lessons learned. People are terrible about extracting causal factors from an experience. I’m people. So I reckon that applies to me too. A sample size of 1 isn’t authoritative. It doesn’t constitute proof, or evidence[…]
Most figures I found, for the month of October 2012 (Including Mobile): Google’s search market share around 86 to 90% in the United States and 89% globally. Bing is ~7% in the US and ~5% globally. Yahoo 3% US / Baidu 3% globally (China). Search, as a design pattern requires, at minimum, a box into which you enter words or numbers, and a medium to display results, what is today called a Search Engine Results Page (SERP). (It doesn’t really have to be a page at all, which is why I use the word medium. Siri is a good example. Glass is another. That sort of thing.) The more places Google can put that box, the better it is for[…]
Andrew Cherwenka and I soft launched our startup, Authintic, last week. Authintic is an analytics technology company enabling permission marketing. Andrew wrote a much more detailed piece for the Huffington Post on the topic. It’s worth a read. He’s very eloquent and accessible. I have nothing contradictory to add. Nothing really controversial to say. Being at the confluence of three mega-trends is where I’m comfortable. The first is privacy in marketing. The FTC and the EU have made their opinions known. There’s this big fear out there that consumers won’t opt-in. Why the fear? Is anybody doing anything wrong? Common’ people – let’s treat people with respect. The second are advancements in machine learning and processing power. There are more[…]
This series appeared on the Eyes on Analytics blog the week of May 14. It’s consolidated here in part because it was popular. Consider the following two, distilled, points of view: Statement 1: “Big Data Analytics is going to change the way we do business. Sure, a lot of it will be routine “I’m okay!” status updates from sensors, but making sense of the key parts of it, like “help me, I’m failing”, will be extremely useful. Companies that were previously exempt from competing on analytics will be disrupted by new entrants who will compete better, either by being more effective or being more efficient. Big Data Analytics is already having a disruptive impact in marketing, where it never used[…]