Extract from Apoorv Agarwal’s article “Structured vs Unstructured Data”
Data is the raw material that fuels the information age. Today’s most successful enterprises have comprehensive strategies for collecting, storing, and utilizing that data, and this gives them a huge advantage. However, in order to extract that value from our data, we need to turn it into knowledge.
Artificial intelligence is the tool for the job.
Essentially, machine learning algorithms can crawl through massive data sets to uncover insights that are either hidden to the human eye or that we couldn’t practically discover within any reasonable time frame. By detecting these subtle patterns, businesses transform their raw data into powerful analytics.
Data, however, takes many forms, and our approach to analyzing it likewise needs to adapt. Although data scientists categorize data in many ways, we’re going to narrow in on two general buckets: structured and unstructured data. On the whole, structured data is easier to process than the unstructured variety, and so most big data analytics tools work on it alone.
The problem is that this leaves a huge amount of value on the table. Unstructured data makes up over 80% of enterprise data and is growing at a rate of 55 to 65% per year. This is a big incentive. Not only do we already have these resources at our disposal, but, as we’ll see, unstructured data sets provide unique opportunities.