‘Today is all about Big Data’; ‘let’s try to use Big Data’; ‘Big Data is the next big thing’; We hear about Big Data everywhere, so much so that it is the most trending information science buzz word right now. But do we really know what ‘Big Data’ means?
Big data can be described as data that is extremely large for conventional databases to process it. The parameters to gauge data as big data would be its size, speed, and range. Big data can be comprised of both structured and unstructured data.
As the name suggests structured data would be the relatively simple data perhaps in numbers and characters that can be classified, recorded and read easily by a machine and hence can be stored by databases without difficulty. However, this kind of data, as most data analysts would agree, is only about 20 percent of the data that’s out there.
While unstructured data is data that is extremely large, incomprehensible and cannot be stored in conventional databases. It’s basically data, heavy in text and human generated. Text from social media, Facebook posts, and tweets essentially are sources of unstructured data. It cannot be easily recorded and classified in database systems.
Now that we know what big data is, let’s understand why companies today are investing in harnessing this indispensable Big Data. Companies are investing a great amount in elaborate dashboards and real-time data streams, but none of these data representing mechanisms are able to help companies or customers gain insights from data. While the data influx has risen exponentially, there has been negligible progress in efficient data interpretation for the last two decades. Sample this, a data analyst analyzes a dashboard and then uses his or her proficiency to derive conclusions and suggest actions. That would be just one dashboard and companies receive inexhaustible data; analyzing every dashboard with same proficiency would not just be time-consuming but would also require enormous manpower. This delay in effective data interpretation can cost a company time and resources.
This brings us to what I believe is, a fundamental flaw in various companies’ approaches to investing in big data. The investment should not be in big data but in efforts to transform big data to simple and easy-to-understand reports. The investment should be in the interpretation rather than raw data. We need systems that not only tell us what the data means but also give us insights to derive conclusions.
While there are various software tools like Hadoop, Augmented Analytics Software, Data Integration tools, Information Management Solutions, etc., which are currently used to address the management of unstructured or raw data, there aren’t many tools to interpret that data. Our artificial intelligence report writing tool, Phrazor, does exactly that. Phrazor analyses big data, derives insights, and then communicates those insights, in words, using natural language generation technology. Interested? Contact us.