There are three broad categories of reasons why people ask for figures. They know what they know and need evidence to support what they know. (Convenient Reasoning) They know what they don’t know, and genuinely need objective evidence in support or against somebody or something. (Decision Support) They don’t know what they don’t know, and are looking for somebody to tell them what they should know, or what they should do. (Exploration) There are three questions an analyst should ask whenever they get an incomplete request for data: What problem are you trying to solve? Who are you trying to convince? What are you going to do differently if you had the evidence? Editorial: Not everybody who engages in convenient[…]
A post made the rounds on HN over the weekend about a DIY kit that enables plants to tweet when they’re thirsty. You can read the post for yourself here. Summary: Order a kit for $99.95, do some sodering, and you’ll get a sensor in the shape of a leaf. The sensor detects the relative dryness of the soil. The sensor will send a signal that ultimately causes the plant to tweet you when it’s thirsty. Editorial: It’s a novel application of sensor technology + internet of things (IoT) + a social broadcasting utility. It solves a practical problem that typically is solved by outloook reminders or having a Twilio App text you every two weeks. It’s another case study[…]
Yesterday, Jeremiah Owyang, with Andrew Jones and Christine Tran – from the Altimeter Group, published “A Strategy for Managing Social Media Proliferation”. It’s so huge and so packed with goodness about Social Media Management Systems (SMMS) that I couldn’t do it justice in three bullet points. I extracted three choice quotes below. Choice quotes: “Corporations should not jump into social media without a clear goal…Instead align goals towards business objectives.” (p. 23) “Unlike one-way marketing of yester-year, company must be ready to engage with negative conversations, often taking them head-on.” (p. 23) “New media efforts will be scrutinized by management as budgets shift. Be prepared to measure.” (p. 23) Editorial: They’re correct in emphasizing the importance of strategy and process[…]
Stephane Hamel posted an excellent article today on “The Three Heads of Online Analytics“. To summarize: To succeed in online analytics, we need an analytics competency center. Start with the business in mind, get the technology in place, and then analyze to generate insights. Analyze your weaknesses and zap them, try adapting ‘paired programming’ that’s common in agile methods. I commented: There are T’s, S’s, and A’s. T’s are technical analysts. S’s are strategic analysts. A’s are Analytics (actual analytics, not fake BI reporting) analytics. Hiring a competent T-A is exceptional. Finding a S-A is getting easier. Finding a good T-S-A is damn near impossible. Most of them are consultants. Web analysts need to identify their weaknesses, be it T,[…]
Audrey Watters wrote a good article about Data Science in 2011 for O’Reilly Radar. Audrey cites three big events / trends: Hadoop, an open source distributed computing framework, became ubiquitous. More Data, More Privacy Problems – citing the Apple scandal as an example. Open Data at an Inflection Point. (With a great shoutout to my friends at BuzzData!) Editorial: Even Hadoop has warts (gasp!?!111shiftoneone), but so far there are good anecdotes about it working out well for many companies, and, you can expect a few horror stories in 2012. The devices we’re carrying and our own behaviors are generating more data than ever – and people – that means you – need to be aware of when they’re consenting to[…]
Steve Miller authored a very good article about Data Science Skepticism over at Information Management. I’ve previously written about Data Science and shared an excellent video about what makes a great data scientist. Both posts are expanded primers on the emerging field. The TL;DR version is: A Data Scientist (DS) sits at the intersection of computer science, statistical methods, and business. I won’t define what Business Intelligence (BI) is. There’s an EMC study making the rounds. Steve Miller takes exception to some portions of that study. To summarize Steve Miller: Findings from the EMC survey made certain statements about BI’s that are unnecessarily polarizing, and should be viewed with suspicion by data scientists (which should be their natural inclination anyway).[…]
Tyler Nichols writes: “I am done with the freemium model“. Tyler divided all the users of his service into two groups: free and paid. He measured the behaviors of each group. He found that the free group was detrimental to his business because: They emailed more questions on average than paid people. They hit the spam button when he emailed them with a follow-up, paid people didn’t. Free customers were not worth the maintenance costs they caused. Hacker News and other communities replied (paraphrased): Free people were not as engaged, and therefore more wreckless. It was a santa letter generator, which has low repeat value after the season. The plural of anecdote isn’t evidence, you’ve added little value to freemium[…]
I used this blog to talk to very specific groups. Sometimes it’s marketers. Sometimes web analysts. Sometimes it was candidates applying for a position. Sometimes it’s data scientists, brandsters, and social analysts. Sometimes this worked. Sometimes I confused the hell out of different audiences at different times. I’ll continue to speak to web analysts through the research committee of the Web Analytics Association, in particular, through a new experiment we’re launching and ongoing Peer Review Journals. I’ll continue to speak and collaborate with ultra niche communities – data scientists, marketing scientists, and open data professionals through christopherberry.ca. Eyes on Analytics is shifting. I’ll be curating content from not just from marketing analytics, but also from further afield. My goal is[…]
It’s worth explaining The Gartner Hype Cycle. It’s topical for 2012. It works as follows: Usually many people invent a technology during the same envelope of time. Somebody really gets hooked on the idea. That somebody executes the technology sufficiently well that it produces a technological trigger. And that gets the ball rolling. Awareness spreads through a single market, and then transmits into adjacent markets. Excitement spreads like fire. People are quick to see potential. Enthusiasm is contagious, and opposing views are downvoted into gray obscurity. Innovators are visionaries. After all, I’m winking, pointing a finger at you, and making a ‘click click’ sound my voice. ‘Hay, click click’. This is an impolite way of saying that ‘ignorance increases’. Hype[…]
You may have read something about ‘Detecting Novel Associations in Large Data Sets’, a paper appearing in Science, 334, 1518 (2011) by David N. Reshef et al.. You can check out the software here. This is an initial commentary and an explanation about what it’s all about. The Longer You Look, The More Likely Error will Find You Take a very large dataset, say, all the customers of AT&T and their calling records 2001-2011, and divide it into to two random but equal sets. Say you didn’t have any hypothesis at all. You just wanted to see what was related to each other in that set. Say, each customer record has 5000 features, including gender, date of birth, credit score,[…]