The Evolution of Big Data
Do you ever wonder how exactly companies like Facebook, Amazon, and Netflix are able to predict what types of friends, books, and movies you are going to like with such incredible accuracy? When you think about it, some of these companies probably know you better than the people you actually share a roof with.
How is it that these companies can know you so well, despite never actually meeting you in person? The answer lies in the way that they are able to collect and process what is known as “big data” in a way that gives them insight into who you are and what you are looking for. The ability that these companies have to customer-tailor their businesses and marketing efforts for each individual customer gives them a tremendous competitive advantage.
Volume – Collecting Big Data
There are a number of popular V-words that are used to discuss big data, but the most common one to lead off the conversation is volume. The amount of data that is being created and collected today is remarkably higher than it was last year, and exponentially higher than it was even just a few years ago. Words like “zetabytes” keep needing to be created in order to describe the rapidly growing amount of data that is stored on servers around the world.
Whatever business you find yourself in, consider all of the information you are collecting from each and every sale you make. It is likely that every time you have upgraded your equipment, more data points were introduced and recorded for each transaction, and the fact that cloud storage continues to come down in price is making big data storage more economical.
Velocity – Grabbing Streaming Data
As technology has improved in recent years, our ability to collect data has also improved. Trying to monitor data from hundreds of different sensors in an automobile was unheard of in the 1990s, but processing that type of data is commonplace today. The ability to monitor data coming to us at high speeds gives us the ability to react quickly and possibly alter our approach well ahead of our competitors.
Variety – Different Types of Data
In the past, a retail store would be considered advanced if it was able to track the sales of specific products and adjust their stock levels accordingly. Today, those same stores are using loyalty programs to learn which customers are buying which items at different times of the day, week, month, and year. On top of that, the most sophisticated big data analysts are also tracking external sources like Twitter, Facebook, and YouTube to acquire even more information about their target customers.
Examples of Big Data in Action
One of my favorite applications of big data is the way that Major League Baseball is now collecting information about every single pitch, hit, throw, and run in every game. The teams are then processing this data and using sophisticated analysis techniques to improve both their personnel and in-game decisions. It used to be enough to know that Randy Johnson had a sub-3 ERA and could throw the ball 100 miles/hour into the 9th inning, but now we can break down exactly how much of a pitcher’s ERA over the course of a season was their skill versus the skill of the other eight fielders.
Other great case studies on big data include UPS placing GPS trackers in every one of their brown trucks and then recording huge savings on fuel because they were able to use that data to optimize their routes. President Obama’s re-election campaign also used big data to identify the volunteers who could have the biggest impact on the election.
While your business might not be able to use big data to quite the degree that Major League Baseball, UPS, and the President of the United States did, there are almost certainly a few ways that you could collect and analyze some of the data that is passing through your company on a daily basis. With the constant improvements in technology and the low cost of storage, even small businesses are now able to harness the power of big data. If you aren’t looking into how big data can help your bottom line, your competitors probably are!
Source: https://www.linkedin.com/pulse/20141203142737-25701104-the-evolution-of-big-data?trk=prof-post
