Processing Unstructured Data in 5 Easy Ways

Enterprises constantly deal with increasing amounts of data coming from various sources and often end up processing unstructured data on a regular basis, which constitute 80% of their data. All of this information which keeps on increasing at an exponential rate, can be really useful to enhance enterprises' value proposition and increase customer satisfaction after gaining insights. But if not recorded and organized properly, it leads to a lot of time and effort wastage.

Let us understand the challenges and cover the best practices for handling and processing  unstructured data to get the most out of it.

But First, How to Define Structured and Unstructured Data?

Structured data is the sequential data which can be stored in database SQL with rows and columns. Whereas, unstructured is the one with no metadata, and cannot be represented in rows, columns or annotations eg: ASCII text, scanned documents, images, etc. 

Though both are valuable, unstructured data remains unusable until processed. Easier said than done, after all analyzing the piles of data can be daunting. However with the right strategy and tools it the process can be made simpler and worth transforming this data which has the potential of ultimately shaping decisions within the company.

Let’s dive into the differences between the structured and unstructured data:

Features

Structured

Unstructured

Technology

Resides in relational database table where patterns can easily be identified

Cannot reside in relational database (based on character and binary data)

Scalability

Less scalable

More scalable

Robustness

Very robust

Less robust

Example

Data kept in relational databases and spreadsheets

Data such as emails, social media, blogs, documents, images and videos

 

It is becoming a major problem to tackle unstructured data getting accumulating and growing exponentially. 

Understand Unstructured Data and its Effects

The data you’re handling is unstructured if the majority of your time is spent on manipulating and analysing it. Examples of unstructured data include emails, customer surveys, documents, call center notes, customer forms and letters, blogs, social media, online forums, articles, reports, etc.

The only disadvantage of possessing unstructured data is you have to process it to use it. It is nothing without a structure and remains unusable until it is processed. Being large and cumbersome, this raw and unorganized data remains an inefficient precursor to structured data. It’s a necessary evil which proves to be highly advantageous if leveraged to gain insights. 

For example, for a social media post, it contains information such as the time of posting, the audience with whom it is shared, etc. However, the content of the post cannot be easily categorized and may cause compatibility issues with the structure of a relational database system.

How To Structure or Process Unstructured Data

Unstructured data can be detrimental if it takes up too much space on your businesses’ storage. It is a good practice to remove unnecessary data to reduce further confusion and save your time only on the structured data that is beneficial. Also, it is necessary to maintain and update the data backup and recovery service which should come handy in times of crisis. 

Here’s a list of actions that our experts have curated which can help process the unstructured data set.

  1. Clearing the unstructured information: Follow the strict rule of clearing the data into useful relational database format on a daily basis. Clean the entire set of data and ensure the practice is followed by each member of the team. Ensure you collect data from reliable sources and avoid any random source to prevent corrupting the entire data set. 
  2. Evaluate whether to keep it or delete it: There will come a time when you would realise it is unnecessary to keep the information which might remain unusable at all times. Gathering information for a purpose is costly, thus should only be collated for future purposes if it is really important. Hire data experts if you feel it is getting difficult and feeling overwhelmed with the time consuming process. 
  3. Entity Extraction: You can process the unstructured data by pulling out names of people, organization, location etc. from it. This process will help you take out the necessary information from the cluttered, raw data, so as to fit the relational table syntax.
  4. Devise a pattern: It is important to follow a reference guide for yourself which should include one or more of the following:
    • Classification: This process helps you show the relationship between the source of information and the data extracted. It is important to keep a record in order to recognize patterns and keep consistent with the process. Categorize the data as per the context in which it is being used will help you in classifying the chunk of text. With more than one word may be used to refer to a particular entity, possessing  knowledge of the larger context and about the domain under consideration will help simplify processing unstructured data. Analyse the grammar of the data as it acts as a meta data for the text and helps us understand some of the meaning being conveyed.
    • Sentence chunking: While scanning, if you run across certain words which fall under noun category, the data can be structured with the type of relationship it bears with other words.
  5. Analyse the data:  After all the raw data is structured, now it's time to analyze and make decisions beneficial for the business. AWS S3 is a great solution which can be queried using Athena. Also, ElasticSearch is great for unstructured text and human communication, and AWS Neptune can effortlessly find relationships in unstructured data and allow you to discover how such data is connected. By mining unstructured data for actionable insights, organizations will be able to have a much more fulfilling experience in providing better products, services, and customer experience, aligning with their business goals.

Wrapping up

Srijan’s data management solutions work sophisticatedly on your unstructured data and our experts can help you reach your data goal. Here’s a quick runthrough of what Srijan has done in the past:

  • Intelligent Image Captioning system using Deep Neural Networks for generating captions to make image descriptive and further use the generated caption for querying tasks.
  • Auto Tagger system to automatically generate key tags for articles and textual data to make it more accessible and organisable.
  • Smart KYC automation system toexpedite the KYC process by automatically extracting relevant details from the passports as well as other IDs/Docs and instantaneously validating it saving tons of time and manual interventions. 
  • PDF data extraction system to extract images and their metadata descriptions from pdf manuals with unprecedented accuracy and perfection for one of our clients. By leveraging current state of the art OCR tools and custom designed rule based image segmentation techniques, we created an intelligent system to extract the relevant details from the given pdf manuals.

 

Want a similar or a tailored solution for your data management problem? Contact us to get the conversation started.

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