4 common Big Data myths businesses need to know about
Big Data promises organizations a plethora of advantages as it takes a leap into the future of a largely data-driven economy. One of the main effects is how market will become revolutionized, owing to highly sophisticated data analytics tools, allowing organizations to harness all types of data, from research data to financial data, mobile data to social media data. It can further allow organizations to discover new revenue streams and formulate new business strategies that can result in exceptional competitive advantages over rivals.
However, Big Data can equally be a cesspool for organizations due to the many assumptions that managers and executives have about harnessing big data tools and applications. These myths are as follows:
Big Data contains Big Data
Big Data sure is ‘big’ in terms of the size of the datasets. Big Data repositories contain exabytes of data, which are enormously large data compared to the ones we are accustomed to in our daily lives. According to Tech Target, “An exabyte of storage could contain 50,000 years’ worth of DVD-quality video.” However, in reality, big data consists of a very large quantity of very small data, and not big chunks of data as many organizations may be misled to believe.
Therefore, there is an incredible complexity to gather meaningful data past a sandstorm of irrelevant data.
Big Data requires instant implementation
Before organizations make the big error in hastily implementing big data for their company’s goals and objectives, it is important that they pay attention to the needs of their business and then decide on how big Data should be implemented. The key is take implement big data in small, incremental stages, and how data can be collected and interpreted as efficiently as possible.
Granular data is better for decision making
There is an assumption that the more granular the data is, the better off the organization is in leveraging data to its utmost potential. This is not true in the slightest. Just as the first page of the book doesn’t tell you how good the entire book is, the same is true for granular data about the entire big data.
Data captured in real-time, for instance, is only a snapshot of a particular reality in a particular time frame. Therefore, if an organization was to implement a business decision based on real-time data, then chances are that the impact of its decision will be short-lived as the data might have changed drastically after a short period.
Hence, it is important that companies first determine their objectives and what specific conclusions they want to draw out of it, and use a combination of both granular and aggregate data.
For big data, you need data analysts
Another trend that has been witnessed among organizations is the rise in data analytics, prompting a dramatic demand increase for data analysts. However, there seems to be a lot of hype around the subject, as there is a greater need for tools and systems that can accommodate the velocity at which data is being stored, analyzed, and interpreted.
Therefore, organizations need to give more importance to the tools through which they can support the voluminous data for storage, analysis, and interpretation purposes.