In today’s digital world big data almost sounds like an over prescribed medication. A company has revenue struggles? Use big data. Another needs more marketing ROI? Big data. But really, we have only scratched the surface of utilizing big data to its full, practical potential. Big data is a metaphorical iceberg, with the portion above water being structured data and the massive portion below the surface being unstructured data. The challenge for companies today is tackling unstructured data to unlock the full potential of big data computing and analytics.
What is Unstructured Data?
Unstructured data is what it sounds like, data that is uncategorized and lacking organization. Most of computed data by companies currently is structured data. Although this is well and good, it is estimated by industry experts that as much as 90 percent of enterprise data is unstructured and considered inusable. A majority of unstructured data is text-based and originates from human sources, such as emails, reports, and industry records. The list goes on and on. With such a staggering volume of unstructured data, it is understandably daunting for any company to begin classifying it. This apprehension is fueled even more by the lack of analytics tools capable of processing unstructured data. Typically analytics tools operate by keywords, categories and classifications.
The Potential for Unstructured Data
So, what is the hype over unstructured data? Long considered untouchable, unstructured data is a goldmine for relevant business and industry information. With tools available like Apache Spark and the patience to do a little organization, unstructured data can turn into a tremendous asset for any company. For businesses, unstructured data is what they make of it. It can be a valuable secondary resource to glean information from or it can be the bane of the IT manager’s existence. Either way, there isn’t a clear-cut path for a company to proceed.
My suggestion: take small steps to make the transition and utilize some of the unstructured data already available. It won’t happen overnight, but a robust system for harvesting unstructured data will pay dividends in the universal race for data and data processing. Here are four practical ways your company can begin to organize unstructured data. Other useful tips on big data cleanup can be found here.
Tips to Organize Unstructured Data
1) Start With the Basics
Don’t overcomplicate things. It is a moot point to say structuring unstructured data is hard. Instead, call data for what it is and proceed accordingly. Several departments will have to coordinate to organize groups of unstructured data. Make an effort to convince relevant team managers the importance of data projects. Many IT team members are resistant to unstructured data because it isn’t computer generated or originates from outside of the company. Decide early who will be involved and how much.
2) Set Goals
Set reachable goals and objectives. What does the organization hope to get out of organizing data? What benchmarks are reasonable to meet? Goal setting will establish a sense of purpose and create more internal support for data projects.
3) Create Basic Structure
Begin classifying groups of data using criteria established in goal setting. This may be by department, category or by security level. Classify data and label it internally with a relevant ‘tag.’ Labelling information will make it more retrievable for later use and more refined classifications. Go in and remove excess or duplicate data. Basic structure will also allow for irrelevant information to be discarded or flagged. This stage is time consuming, but is necessary to harvest and used previously inusable data.
4) Prioritize Data
This is important for storage and maintenance. Data should be classified by importance to the organization, usually by how frequently the data is accessed. Data with top priority needs to be easily retrievable by personnel authorized to use it. Priority can also be established by relevance of the information and timeliness. Industry records from past decades won’t be as useful as the earnings reports of a competitor from a previous quarter. Take care when prioritizing data and create clear guidelines on what information meets certain priority thresholds.
As analytics tools become more sophisticated companies will use data more effectively. The next big breakthrough in big data is harnessing unstructured data that isn’t as intuitive to track or retrieve.