The problem with big data

While the term has been around since the 1990s, big data was rarely spoken of beyond the IT industry until the dawn of the 21st century. Since then, however, it’s become one of the most popular buzzwords in modern culture – and many don’t even understand what it is.

Understanding big data

According to the definition, big data involves advanced datasets that are beyond the use of your ordinary, day-to-day computing. While it’s capable of working with multiple types of data, including unstructured, semi-structured, and structured data, big data centers around capturing, processing, and managing unstructured data.

Given the technical definition, it isn’t really necessary to attach a tangible size to big data. Although some insist that big data has to deal with extreme amounts of data, ranging from several terabytes to a few zettabytes, it’s actually less about the amount of data and more about the usage.

Overcoming the obstacles of big data

While the exact definition of big data varies from organization to organization and even from individual to individual, the same challenges and obstacles persist.

• Handling such large amounts of data: Either way you look at it, the datasets that comprise big data can take up a large amount of hard drive space. Accommodating this need for additional storage, either through in-house drives or cloud-based servers, and creating a plan for data and disaster recovery, is the sole responsibility of each individual organization.
• Separating stagnant data from actionable insight: Let’s face it: big data collection tends to result in a lot of irrelevant information. Also known as stagnant data, this information actually makes it more difficult to utilize big data in a positive and efficient manner.
• Choosing the right platform: Although it’s still relatively new, the current IT market is flooded with options for managing and processing big data. Not only does this cause confusion amongst those who are new to the concept of big data, but it often results organizations that try to use conflicting tools and software. To overcome this challenge, try to find a centralized solution that offers integrated automation, 24/7 accessibility, and a skilled technical support team.
• Maintaining data security: Big data introduces some brand new challenges to data security. With so much data being collected, created, processed, and stored on a day-to-day basis, there are just too many potential access points for would-be hackers. Couple this with the fact that this data is often coming from multiple sources and it’s easy to see how big data could actually compromise your organization’s overall data security.
• Training current staff members: Finally, there is also the challenge of training your current staff members for proficiency with big data. While some of them might have a strong grasp of big data to begin with, most will be in the dark when it comes to standard operating procedures, regulatory compliance, and general data management. Making sure your team has the proper knowledge to work with big data is the key to long-term success when dealing with this new medium.