Using Big Data & Algorithms: Six Companies to Imitate

Think for a second about your goals. Maybe you’re thinking about personal goals, or maybe you’re considering how to take your business to the next level. It’d feel pretty great to achieve what you just thought about, right? Now consider this question—how do you get from your current situation to the situation you just envisioned? There are many things that go into achieving success, but a good place to start is to think about successful people or organizations that you can pattern yourself after. Pattern your organization after these six corporate giants, specifically how they use big data and algorithms to improve production, and you’ll be well on your way to achieving greater success.


Finally finishing a series that you’ve invested hours in could free up so much time for you—if there wasn’t another series waiting in your queue that was placed there precisely for you. Fortunately (and unfortunately), Netflix seems to know exactly the type of show that you will enjoy watching next. Netflix has been in the analytics business all the way back when it was shipping DVDs and competing with Blockbuster. Measuring metrics has become an even more integral part of their success since they started creating their own content. Netflix can predict the shows you’ll likely be interested in based on which part of the world you’re in, and they’ve discovered that they have 60 to 90 seconds to get your attention focused on a show. After those 90 seconds, the odds of losing their customers to another form of entertainment go up substantially. Netflix uses data to disrupt traditional TV and the result has been hard to ignore and even harder to replicate.


The online retail giant has entered the artificial intelligence space through their services, products, and even warehouses. Amazon Machine Learning provides companies with the ability to predict and find patterns using data. As far as AI products go, Amazon Echo utilizes an intelligent voice server, Alexa. In the warehouses, Amazon is improving efficiency using a software system called the Anytime Feedback Tool. This tool allows employees to share praise or criticism about their colleagues at any time. The warehouses also use software that guides warehouse workers directly to items that need to be shipped, which makes the task as simple as possible. Products are not organized logically on the shelves, as many other warehouses organize themselves. There is no categorical organization of any kind. Items are constantly monitored thanks to a series of scans at various checkpoints. It’s not conventional; it’s innovative and data driven.


Apple has prided itself on giving its users their privacy, yet they use algorithms in multiple ways. Apple uses differential privacy, which is the statistical science of trying to learn as much as possible about a group while learning as little as possible about any individual in it. Differential privacy enables Apple to collect and store its users’ data in a way that they can gain meaningful insights into what their users want. At the same time, it can’t extract anything about an individual that would constitute a privacy violation.
Like every other mega-corporation, Apple wants to know as much about their customers as possible. In an effort to please their customers, Apple Music compiles data from years of listening to iTunes to understand user tastes and habits. For example, high star ratings and frequent plays over the years push tracks and albums to the My Favorites Mix. My New Music Mix takes listening history into account by surfacing tracks that a user has not yet played. This type of algorithm optimizes Apples chances of satisfying the demands of their customers.


PageRank, the algorithm Google uses to calculate search results, is the system behind Google’s dominance of Internet search. The algorithm works by looking at every link to and from every page on the Internet. Links determine the value of a page. The more links there are to a page, the more the content is supposedly valued. But it also takes into account the PageRank of the pages that links are coming from. So being linked to by a page with a high PageRank is significantly more valuable than being linked to by a page with a low PageRank. On top of all that, it punishes “link farms,” which are vast networks of sites that link to each other in an effort to boost their PageRank. Once again, algorithms are making life better for all of us.


Twitter, as of February 2016, has an algorithmic timeline that has changed the way news is distributed. Prior to the algorithmic timeline, tweets were displayed only in chronological order. Now tweets are displayed chronologically some of the time and at other times displayed the way the algorithm has determined. The algorithm makes the timeline a bit friendlier and more interactive. Have you been away from your timeline for a while? The algorithm will display the most popular tweets while you’ve been away. It ensures that you see more tweets from the people you interact with the most. It also makes sure that the most popular tweets are more likely to be seen by you, which makes it possible for tweets to go viral on an almost unprecedented level. Twitter says the algorithm, which was met with an undeniable level of skepticism when it was introduced, has brought in new users and made the old users more active. The algorithm improved key metrics such as monthly active users, impressions, and time spent on the site.


Airbnb is making some major noise and they’re relying on analytics to do so. Their search algorithm plays a vital role in what they’re able to accomplish. They’ve created probabilistic models to determine where someone was likely to have searched given where they booked, which makes their search algorithm more likely to display the best result for any given set of search terms. They’ve also started using data to increase the overall ratio of female employees in its company by analyzing the company’s hiring data. They realized they were getting plenty of female applicants, but very few of those applicants were actually being hired. So they made some changes by making the interview process more gender-blind and creating a more female-friendly atmosphere for applicants. They increased their ratio of female employees on their data science team from 15 percent to 30 percent back in 2015.

These six companies use big data and algorithms in completely different ways, yet each way is effective and vital to their growth. You will need to determine the best ways to use big data in your company, but be assured that replicating these companies by using big data in any way is a great start to your progression as a company.