“You shouldn’t be collecting Big Data under the premise that more data is better, cooler, sexier, etc.” Pradyumna S. Upadrashta ( February 13, 2015 )
“Hadoop has an irreparably fractured ecosystem.” Joey Zwicker ( 12. February 2015 )
“Big data is not for the feint of heart, you and your team must be willing to master many disciplines in order to be successful. You’ll need understanding of code, hardware, Virtualization, networking, databases (SQL & NoSQL), ETL, Cloud, and more. Don’t fool yourself, you’ll need some serious skills on-board.” Kevin Daly ( 10.11.2014 )
“Any real data analysis involves data manipulation (sometimes called wrangling or munging), visualization and modelling.” Hadley Wickham
“Managing big data for analytics is not the same as managing DW data for reporting. In fact, the two are almost opposites … . For example, reporting is about seeing the latest values of the numbers that you track over time via a report. Obviously, you know the report, the business entities it represents, and the data warehouse that feeds the report. An analysis is more about discovering variables you don’t know, based on data that you probably don’t know very well. Also, a report requires a solid audit trail, so its data must be managed with welldocumented metadata and possibly master data, too. Since most analyses have no expectation of an audit trail, there’s no need to manage one. That’s just a sampling of the differences. The point is to embrace Big Data Management for analytics as a unique practice that doesn’t follow all the strict rules we’re taught for reporting and data warehousing.” Philip Russom ( 2013 )
“Prefer simplicity in algorithm design.” John Langford ( 2005 )
“On a sequential computer, the fast algorithm is the best algorithm, but for new science area, I believe we need more creative approaches for algorithm design in order to extract more valuable insight in real-time.” Fatih Hamurcu ( May 7, 2015 )
“What makes a good metric?
Here are some rules of thumb for what makes a good metric-a number that will drive the changes you’re looking for.
A good metric is comparative.
Being able to compare a metric to other time periods, groups of users, or competitors helps you understand which way things are moving. “Increased conversion from last week” is more meaningful than “2% conversion”.
A good metric is understandable.
If people can’t remember it and discuss it, it’s much harder to turn a change in the data into a change in the culture.
A good metric is a ratio or a rate.
Accountants and financial analysts have several ratios they look at to understand, at a glance, the fundamental health of a company. You need some, too.
There are several reasons ratios tend to be the best metrics:
1 Ratios are easier to act on. Think about driving a car. Distance travelled is informational. But speed-distance per hour-is something you can act on, because it tells you about your current state, and whether you need to go faster or slower to get to your destination on time.
2 Ratios are inherently comparative. If you compare a daily metric to the same metric over a month, you’ll see whether you’re looking at a sudden spike or a long-term trend. In a car, speed is one metric, but speed right now over average speed this hour shows you a lot about whether you’re accelerating or slowing down.
3 Ratios are also good for comparing factors that are somehow opposed, or for which there’s an inherent tension. In a car, this might be distance covered divided by traffic tickets. The faster you drive, the more distance you cover-but the more tickets you get. This ratio might suggest whether or not you should be breaking the speed limit. A good metric changes the way you behave. This is by far the most important criterion for a metric: what will you do differently based on changes in the metric?
1 “Accounting” metrics like daily sales revenue, when entered into your spreadsheet, need to make your predictions more accurate. These metrics form the basis of Lean Startup’s innovation accounting, showing you how close you are to an ideal model and whether your actual results are converging on your business plan.
2 “Experimental” metrics, like the results of a test, help you to optimize the product, pricing, or market. Changes in these metrics will significantly change your behavior. Agree on what that change will be before you collect the data: if the pink website generates more revenue than the alternative, you’re going pink; if more than half your respondents say they won’t pay for a feature, don’t build it; if your curated MVP doesn’t increase order size by 30%, try something else. Drawing a line in the sand is a great way to enforce a disciplined approach. A good metric changes the way you behave precisely because it’s aligned to your goals of keeping users, encouraging word of mouth, acquiring customers efficiently, or generating revenue. If you want to choose the right metrics, you need to keep five things in mind:
1 Qualitative versus quantitative metrics
Qualitative metrics are unstructured, anecdotal, revealing, and hard to aggregate; quantitative metrics involve numbers and statistics, and provide hard numbers but less insight.
2 Vanity versus actionable metrics
Vanity metrics might make you feel good, but they don’t change how you act. Actionable metrics change your behavior by helping you pick a course of action.
3 Exploratory versus reporting metrics
Exploratory metrics are speculative and try to find unknown insights to give you the upper hand, while reporting metrics keep you abreast of normal, managerial, day-to-day operations.
4 Leading versus lagging metrics
Leading metrics give you a predictive understanding of the future; lagging metrics explain the past. Leading metrics are better because you still have time to act on them-the horse hasn’t left the barn yet.
5 Correlated versus causal metrics
If two metrics change together, they’re correlated, but if one metric causes another metric to change, they’re causal. If you find a causal relationship between something you want (like revenue) and something you can control (like which ad you show), then you can change the future
Analysts look at specific metrics that drive the business, called key performance indicators (KPIs). Every industry has KPIs-if you’re a restaurant owner, it’s the number of covers (tables) in a night; if you’re an investor, it’s the return on an investment; if you’re a media website, it’s ad clicks; and so on.” Alistair Croll, Benjamin Yoskovitz ( 2013 )
“Big data should complement small data, not replace them.” Rob Kitchin
“Academic culture teaches you that you’re dumb and that you’re probably wrong because most things never work, nature is very hard, and the best you can hope for is working on interesting problems and making a tiny bit of progress.” Eric Jonas