Big Data Challenges-The ever-increasing volumes of data – being generated everyday – are providing businesses with opportunities on the one hand and opening up numerous challenges on the other. While a lot has been written about the various ways big data can help businesses offer personalized services and solve many real-world problems, what about the challenges that big data itself poses?
Digital businesses, today, are faced with unique challenges due to the burgeoning amounts of data they have to deal with. Here’s a look at some of these problems, in no particular order.
Volume: The sheer amount of data being created is mind-boggling. IBM in its report on 10 Key Marketing Trends, 2017, has reported that 2.5 quintillion bytes of data is being created every single day. And, the rate at which big data is growing is equivalent to an enterprise-grade organization being created every day!
Storage: The data being created cannot float around. It needs a place to rest. Enter storage. Inundated with large amounts of data, businesses are facing infrastructure crunch when it comes to accommodating the ever-increasing volumes of data. They are, therefore, required to look for and invest in storage solutions that can scale according to the need.
Security: One of the most pressing challenges that digital businesses, globally, are faced with is the protection of the oceans of data they possess. Cyber criminals are always lurking around looking for opportunities to steal the data and use it for various cyber crimes. This is underscored by the rise in the number of data breaches and hacking into the corporate networks recently. The introduction and enforcement of regulations like the GDPR (General Data Protection Regulation) add to the burden of ensuring data security.
Unstructured: Often, within an organization, data is spread across departments and silos, and is largely unstructured. All the data – structured and unstructured – from disparate sources must be collated and acted upon to make it useful for analysis.
Usefulness: Not all data is useful or can be used for analytics. Businesses must sieve out relevant data and enrich it using appropriate tools before it can be used for analysis. Sieving out useful data is a time-intensive activity, which involves significant human effort and increases operational costs.
Data-bias: In the end, it is humans that feed relevant data into machine learning models. Despite adequate caution, human biases can creep into the data, which can skew the end results to reflect the biases.