When we learn to read, we learn the letters of the alphabet and their sounds before making our first attempts to sound out full words. Eventually, these words string together to become sentences, and it’s at this point that the story behind the words becomes articulate.
The process of becoming data literate isn’t that different. You start by learning data points and the meaning behind each one before turning to the metrics they support. These metrics, much like individual words, don’t tell you much until to start to string them together to expose the story behind the data. Therein lies the essence of data literacy: having the ability to extract meaning from data.
Confirmation bias is one of the most pervasive tendencies in human nature.
Bestselling author and professor, Michael Shermer sums up the reason why we are so susceptible to confirmation bias: “Smart people believe weird things because they are skilled at defending beliefs they arrived at for non-smart reasons.”
Confirmation bias is the human tendency to interpret new information as a confirmation of our existing beliefs and ignore it if it challenges our existing beliefs. For example, if you see a glowing object in the night sky and you’re a firm believer in UFOs, you might be convinced you’ve just spotted an alien spacecraft.
The presence of confirmation bias has been well-documented in everything from the 2016 U.S. election to scientific research. And product managing, growth hacking and analytics are definitely not immune to it. Here are some important examples of confirmation bias in product management and analytics and suggestions for how to avoid it.
Solving problems with data is appealing because it’s effective. It builds on measurable standards of success that help take the guesswork about which path to take. So why doesn’t everyone in every company make decisions with data all the time?
Last week, we announced Amplitude 2.0 — the next generation of our product analytics platform. Today, I’m sharing the story behind 2.0: the industry shift in how companies are building products, how our users shaped the way we rebuilt Amplitude, and our vision for the future of products.
Back in March of 1995, Microsoft released a product called Microsoft Bob. Designed with computer novices in mind, Bob was supposed to make it easier for the average person to use a PC. The interface was styled as a virtual home where you navigated between rooms to access different programs, all with the aid of an animated dog as your guide (whose later reincarnation showed up in Microsoft Office as our old friend, Clippy).
Not really sure where the “Door to Public Mouse Hole” leads… Image source.
At QuizUp, an Amplitude customer and the company that put out the fastest growing App Store game of all time, any analytical task taking more than 15 minutes out of someone’s day was automated with ruthless efficiency.
With the help of IFTTT (If This Then That) and Zapier you can do the same for all repetitive tasks at your company, without the need for any coding. Automation is critical not just for raising efficiency, but also to prevent information and tasks from slipping through the cracks.
While we want you to be doing all of your web analytics within Amplitude, we know other tasks need to be done away from our platform. From emailing reports to collecting data or tracking competitors, IFTTT and Zapier can help turn any manual nightmare into an automated dream.
Using data to build better products and companies is an ever-changing science. You have to always confront your assumptions and learn if you want to succeed. Whether you’re an industry veteran or starting from scratch, it always pays to get advice from the best of the best.
Subscribing to email newsletters written by experts on growth and analytics is a great way to build a habit around this kind of learning.
Here are five that stand out from the rest.
Written by entrepreneurs, data scientists, growth marketers and venture capitalists, each one offers unique insight into the process of using data to make better decisions and build a better company.
So you just hired someone to fill an opening for the sexiest job of 2016, a data scientist at your up-and-coming Silicon Valley startup. What if, a few months down the line, you realize your data scientists are actually spending the majority of their time working on something decidedly… unsexy?