Unless you’ve been hiding under your desk to avoid being asked to work this weekend (we understand), you’ve heard of big data. If it hasn’t headed for your organization yet, stay under your desk, because it soon will. With big data comes a whole plethora of unstructured data, which doesn’t typically fit within the framework of the traditional relational databases you’re probably used to, like SQL. Here’s what you need to know about it and how you can prepare for its presence in your IT department.
What is Unstructured Data?
Most IT workers are used to structured data. It is written in a format that’s easy for machines to understand, though it baffles most people unless they’re programmers. Structured data is easily searchable by basic algorithms. Examples include spreadsheets and data from machine sensors.
Unstructured data is more like human language. It doesn’t fit nicely into relational databases like SQL, and searching it based on the old algorithms ranges from difficult to completely impossible. Examples include emails, text documents (Word docs, PDFs, etc.), social media posts, videos, audio files, and images.
Why is It Important to Store and Retrieve Unstructured Data?
There is a lot of important, useful information locked up in all that unstructured data. The information in emails and social media, for example, holds important insight that can be used for operational intelligence, marketing intelligence, and more. This kind of information can tell businesses things beyond a customer survey, such as what the public has to say about your latest products or changes in store hours. It also holds information on the production process, various ongoing projects, plans for the future, and much more. Pictures from your last R&D project, for instance, might be helpful in generating better ideas for creative endeavors down the road.
What Tools are Available to Leverage Unstructured Data?
There are a number of ways to begin assembling a database of unstructured data and processing it to yield this valuable insight. Many companies have migrated to an object-oriented database such as NoSQL. Some have also undertaken open source big data analysis initiatives, like Hadoop.
Should your business look into a big data plan? That depends.
- How much does the organization stand to gain from the unstructured data it holds? Big data initiatives aren’t cheap in terms of storing the data (in house or in the cloud), processing the data, and managing the software involved in analysis.
- Is there a variety of unstructured data to contend with? Big data analysis is more useful if it holds a variety of different data as opposed to lots of similar data. AKA, variety is better than quantity.
- Are your competitors taking on big data projects? If this answer is yes, then you should definitely be on board or risk losing your competitive edge.
- Does your business have a qualified person to manage the big data effort? Ideally, a data scientist has strong skills in math, especially statistics, and is also creative enough to see where and how the data can be analyzed and put to good use.
Robust asset management software can be extremely helpful as you add the infrastructure necessary to undertake big data. It helps track the software in play on the system, and can also help monitor your needs for additional hardware and schedule necessary maintenance for the infrastructure.
If you’re ready to take on big data, select your tools carefully, and good luck!
This blog post has been updated from its original publish on 6/23/17.
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