“Big data.” It’s one of Silicon Valley’s favorite and most annoying buzzwords, yet there’s no data shortage in sight as data companies continue sprouting (and growing) like weeds. It’s clear that data is here to stay, but many product managers are still yet to embrace data-driven product management…and they’re making a big mistake.
Here’s why every product manager should start paying more attention to data ASAP:
Not being a “math person” or a data scientist is no longer a valid excuse to diss data. There’s not much actual math involved in data-driven product management as the vast majority of tools out there right now come equipped with analytics packages that collect and present data in a very clear format. Thanks to these powerful analytics tools, you do not need to be a data scientist to collect and analyze data anymore, and according to a piece published by Harvard Business Review, you don’t even have to be a “math person” to make smart data driven decisions. The HBR article suggests anyone without a basic understanding of statistics take a refresher, while noting that said refresher does not mean going back to school. Nate Silver’s advice? Take a hands on approach to learning about data.
“Getting your hands dirty with the data set is, I think, far and away better than spending too much time doing reading and so forth.”
And even if you prefer to take the textbook approach to data, an abundance of resources have emerged alongside the recent surge in data companies, so people with little-to-no experience to learn how to collect, interpret, and apply data. Take for example, Mind the Product’s guide to analytics for product managers, or if you prefer to see the big picture first, big data fundamentals courses are offered at online education spots as varied as Udemy, Cornell, and Coursera.
Product management, along with many other tech trades, are undergoing a shift where data literacy is no longer just a “nice-to-have” skill, but one of the most important skills of the century. Just take a look at the job descriptions that come up when you search “Product Manager” in your preferred job search engine, it’s difficult to ignore the cries for “analytics” and “insight” in even the most junior roles.But what about good ole’ PM intuition, you ask? While intuitive decision-making will always exist, product managers who rely solely on their intuition are soon to be a thing of the past, as Tomas Chamorro-Premuzic recently wrote:
“Purely intuitive managers may face extinction only if they ignore the valuable information provided by data. At the same time, those managers who are capable of data-driven intuition will remain in demand, and increasingly so.”
So even if your PM spidey sense is excellent, it’s simply not enough to keep up with larger trends. In tandem with data literacy, though, it could make you a better product manager and an even greater asset to your organization.
Product managers may think that unsolicited customer feedback only helps them to understand the vocal minority (the customers who voluntarily share their thoughts and ideas). However, carefully collected feedback combined with other data provides an objective look at every customer, making it an an excellent way to learn more about who your customers actually are and how they’re using your product. Knowledge is power, right?[Tweet "Customer feedback is fuel for ideas. Customer data is fuel for decisions."]Here's how: product managers can make more well-rounded product roadmaps by relying on a combination of feedback and data; customer feedback is fuel for ideas, while data is fuel for decisions. For example, when deciding between updating an old feature or adding a new one based on several customer suggestions, you can look at the lifetime value (LTV) of the customers who’ve requested the update and the LTV of customers who’ve asked for a new feature to determine which initiative would be most valuable to your company. Later, whether you decide to update the feature or to implement a new one, you’ll be able to monitor its usage and impact on customers and determine whether it’s been successful.
One reason we've heard from PMs hesitant to make the jump into big data is that they'll get bogged down in slicing and dicing their data and miss the chance to act. While analysis paralysis is real, it's easily avoidable when you keep in mind what the goals of this data analysis are, decide what the information threshold for a decision is, and always remember that done is better than perfect!