For those who aren’t aware, “big data” is the term given to the large amounts of data (both structured and unstructured) that businesses must deal with daily. These data sets can be analyzed to help business owners make more accurate decisions and implement new strategies.
From the outside looking in, it would be fair to assume that the more data a company has, the better, as the company in question will have a larger sample size to pull from, and as a result, their data will be more accurate. However, that isn’t always the case, as learning how to handle big data effectively has become a very challenging task for many businesses around the world.
Big data: In a nutshell
Thanks to the internet and the massive amounts of information available online, big data has revolutionized entire industries and made things possible that would have seemed like science fiction just a few decades ago. Nowadays, we can map disease outbreaks in real-time, explore the visible universe, and even use data from wearable devices to aquire instantaneous feedback about our health.
Even companies like Netflix are in on the act, using big data to generate billions of dollars by discovering customer behavior and buying patterns before using that information to recommend movies and TV shows based on their subscribers’ preferences.
Although, it’s important to point out that the term “big data” is relatively new, after only being first coined back in 2005 by Roger Mougalas (shortly after he created the term Web 2.0). Mougalas described big data as so expensive and complex that it is practically impossible to analyze and process using traditional methods.
As time moved on, big data problems have only become more challenging, as we are now producing more data than ever before, and it’s increasing exponentially. So much so that if you burned all of the data created in just one day onto DVDs and stacked them on top of each other, they would be tall enough to reach the moon – twice!
With that in mind, let’s take a look at the two main significant data challenges of our time and how they can be conquered.
Handling rapid data growth
One of the most pressing challenges that businesses face is figuring out how to cope with the massive quantities of data bombarded with every day. It isn’t as easy as going through the data sets at your own speed. If an organization is to derive some value from its data, it must find a way to process and arrange it quickly, which is much easier said than done.
These days companies have data flying at them thick and fast, and they cannot process it faster than they receive it. This leads to inefficient organization and can even be detrimental to the company’s productivity. After all, the amount of data we have is believed to double each year, so it’s no surprise businesses are struggling to keep up.
For companies that handle large amounts of big data, their best port of call would be to elicit the services of a data technology provider that specializes in dealing with these challenges, such as a cloud data warehouse. These services allow the company to sit back and let the cloud provider manage the service and take all of the heavy liftings out of their data processes, freeing up valuable resources that can be used elsewhere.
So, what is a cloud data warehouse? Well, these services act as a single repository for all of your data. They are usually distributed as a managed service in the public cloud and are designed for analytics, scale, and ease of use, almost acting as a one-stop-shop for your data storage and processing needs.
Converting data into insights
Companies must not only learn to cope with the ever-increasing volume of data that floods their systems, but they must also learn how to turn data into insights. That’s because data, on its own, can not provide any inherent value to an organization if it is not adequately analyzed. It’s what each company does with the data that matters.
To obtain tangible, high-quality insights from different data sources, each organization must examine all factors, variables, and data sources, ensuring that they consider everything. This is easy; they can find new ways to integrate the data into their company and put the insights learned into practice.
For this to happen, a business must ensure that it has the right data professionals on hand who are experienced in working with the tools and making sense out of enormous data sets, such as data scientists, data analysts, and data engineers. Of course, this requires a significant investment, but it’s a necessary step to improve business decision-making with data-orientated insights.
Data is growing faster than ever before, which is why companies must find new ways to harness large data sets and use any insights learned to improve their processes.
This can be a difficult task, but with the help of the right data management tools and experienced employees, mastering the art of data analysis can go a long way to boosting a company’s productivity and increasing its bottom line.