Given how much we’ve come to rely on the internet for work, school, entertainment, business, and other purposes, it comes as no surprise that a staggering amount of data is generated each day — around 2.5 quintillion bytes of data, in fact, as estimated by an IBM report. Because of this, many companies end up hiring a data analytics agency in the Philippines just to help them sort and analyze this vast amount of data.
This mass of data is aptly called big data and is instrumental for figuring out patterns among online users, illuminating new insights depending on which industry the data is gathered from. For example, big data in healthcare is used to develop better treatments and diagnose diseases with better accuracy.
As you can imagine, this huge amount of data isn’t easy to handle and parse through. Here’s an overview of what common challenges are associated with big data and what measures are being taken to overcome them.
1. Inability to handle its sheer amount of volume
The most obvious challenge when dealing with big data is figuring out how to manage the sheer volume that’s generated each day. At times, companies will find themselves simply hoarding big data without having the means to sort, analyze, and interpret them. As more data gets added every day, the harder it becomes for companies to scale up and deal with them. This renders the insights that could’ve been gathered from all that information moot, seeing as they remain shelved with no clear purpose on how to use them.
The best solution for this is to invest in infrastructure capable of managing big data. This could mean acquiring computing devices that have the capacity to hold all that data or using cloud services for the same purpose. Whichever you should go for depends on your business requirements, though a hybrid solution can also be a good middle ground.
2. Unpredictable data quality
Quantity doesn’t always equal quality, as not all data being gathered is useful or even coherent. However, because of how much there is to go through with big data, sifting through which ones contain insightful information and which are invalid or of poor quality can take tremendous time and effort. Judging the quality of data wrongly can lead to false or contradicting information, so it’s crucial that a proper data quality system is put in place.
To this end, there needs to be an efficient organization and filtering system that would eliminate invalid, incomplete, or duplicate data. By filtering out such inconsequential entries, it becomes easier to parse through data with actual useful insights to offer.
3. Integrating multiple sources and formats
Data comes in many forms. For example, overall data gathered from a social networking site may include photos and videos uploaded by each user, comments posted per day, frequency of posts, amount of times posts are liked or shared, number of account sign-ups, and how long a user engages with the platform, among others. As you can imagine, all those can be measured in different ways and thus result in different variations of data. It becomes even more complicated if, say, there are various sources as well such as the difference of data gathered from the desktop version of the social networking site from its mobile app version. Integrating data from different sources and in various formats can certainly be challenging and can slow down the process of analyzing big data.
To remedy this, specific filter systems to automate data organization as well as data integration tools to help bridge adjacent data across various sources. For instance, all visual data such as images and videos can be gathered by one software while text-related ones can be collected by another. This way, you can at least consolidate the same formats together so that it’s easier to review them.
4. Lack of expertise and workforce
Big data experts who can effectively advise companies on how to handle their data are often scarce, as not many are pursuing the field just yet. One possible solution for this is to encourage upskilling for your own tech engineers, supporting them with any training needed so you can have in-house data science talent. Likewise, you can just partner up with tech firms to see if they’ve got experts on board who can offer to help you out on big data management.
5. Security concerns
Data breaches are a persistent threat not only for big data but for any form of information available online as well. In fact, IBM explained in a 2023 report that the global average cost of data breaches that year was up to $4.45 million. While big data can be extremely valuable for your business, possessing it also means taking on the responsibility of ensuring said data remains private and secure.
After all, they consist of sensitive data gathered from your users, so being careless with it is to betray their trust and possibly lose their patronage. To prevent data breaches, investing in strong and comprehensive security solutions for all your company data is highly needed.
Big data can offer tons of possibilities and insights so long as it’s effectively gathered and analyzed. While its implementation comes with various challenges, such as the ones discussed in this article, it remains to be an invaluable pool of knowledge that can help any business or sector grow.
Thumbnail image is AI-generated.