Yesterday, I summarized two statements and asked four questions about Big Data Analytics. Today, we knock off the first question. Did anything go wrong in Business Intelligence generally and Web Analytics specifically? The extreme skeptics towards Big Data Analytics argue that Business Intelligence failed. Others point to cases of success. Who’s right? Today, we see: Major decisions about pricing, like those recently at Netflix, continue to be made without any analytical support (or, the analytics were completely ignored). Few people, to this day, really understand the numbers they’re looking at. (Few can explain the accurate definition of ‘time spent on site’, for instance). Hundreds of thousands of firms continue to compete just fine without any analytics at all, without any[…]
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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 to before, and is gaining huge traction in medicine. There is reason to believe that Big Data Analytics will cause better decision making[…]
John Rauser is a data scientist at Amazon. He put forward a perfectly good definition of Big Data at a conference last week. He said that big data is defined as: “Any amount of data that’s too big to be handled by one computer.” That’s a great definition. I like that. Because if it can’t be handled by one computer, it belongs to a special class of problems that are caused when data is distributed and fragmented. That’s great John, thank you. – Bring on the Anti-Hypers It’s great to use terms like ‘bandwagon‘ and ‘data fetish‘. If you cut through the negativity and really strip it down, you’ll see there’s a concern that storage of data is not the[…]
I believe in markets. Last week’s post on ArtScience Groupe’s sparked a great debate about the use of markets in operations. For the most part, firms compete on the free market. Yet, most firms do not take advantage of the market mechanism in their operations. We certainly have methods to measure the efficiency of markets. We have decades of experience in managing markets. Everybody understands the risks that free markets pose. Why isn’t the mechanism used more often in operations and operations research? *** I’m Christopher Berry.I tweet about analytics @cjpberryI write at christopherberry.ca
TL;DR: Be a human first, a manager second. Source *** I’m Christopher Berry.I tweet about analytics @cjpberryI write at christopherberry.ca
Hank: “In order to be #1 in the iced cream confectionery industry, we need to gross $475 Million next year.” Jack: “Assuming that we continue to grow at the rate that we are, we expect revenue to gross $310 Million next year.” Hank: “Impossible. Your forecast must be wrong!” Jack: “Why do you think that?” Hank: “Because your forecast doesn’t help us hit $475 Million! It has got to be wrong!” There are many methods that marketing scientists use to talk about the future. These include: Projection of current trends forward into the future Generation of a model to explain current trends and projecting that model forward Backcasting Scenario analysis Simulation Hank is disappointed because Jack delivered a projection of[…]
I took in The Avengers this weekend. The data visualization was pretty amazing, complete with very impressive information architecture. Half of a scene was done through a screen. Bruce Banner worked on simulation for about a 20 seconds, and with a single hand gesture, swiped it across the room to Stark’s screen. Stark immediately began working with it. That swipe took all of 1 second. Currently, the amount of time it takes me to do the equivalent with a fellow data scientist is around 4 minutes. (Save, upload files to Duck, verbally say ‘it’s there’, download, open the application, load, run the script, render the view, continue). Many of the digital experiences were three dimensional and could be engaged with.[…]
The point of big data and data science is to: Understand why things happen the way they do Make predictions about the future Seems like an innocent statement? Oh no. No it isn’t. This matters. What’s the problem? Two different groups of people believe in two different things. It can’t be the case that both of the bullet points I stated can be simultaneously right, can it? Origins of the problem Technology emerged that made it possible to make predictions about the future without any understanding. Leo Breiman, towards the end of his life, saw this and then just let it rip. He wrote “Statistical Modeling: The Two Cultures” for Statistical Science, 2001, (16), 3, 199-231.To summarize: It recently became[…]
If you’re in analytics, you should be keep a journal. Or call it a log or running commentary. This is something you keep in addition to your burn list. A journal helps you to: Remember what you explored Exploit more reliably Recall inspiration Some of the best people in analytics are great explorers. They experiment. They examine diversity. The analyze variation. They examine options in the context of systems. They’re great inductive thinkers because they had to learn the hard way to become inductive thinkers. Exploration, on its own, is a very high value activity, but also carries the biggest risk. It’s also the riskiest and the hardest to explain to those who do not explore. Exploration is an attempt[…]
How can you tell if a strategy is working? You measure it of course. To understand what you’re trying to measure, you have to understand the strategy. If you can draw a Strategic Activity System, you can align a measure to each of the outcomes. Take a Strategy Activity System and align an indicator against it. If it can’t be measured directly, what proxy measures are available? That’s the creative aspect of KPI design. Sometimes tricky. Often imperfect. Are the activities that the firm is doing, marketing or otherwise, yielding the benefits that you believe them to be? For instance – are your fares really lower? Are your on-time departures really higher than the competition. Are consumers more loyal? Don’t[…]