Unstructured data analytics is a relatively new paradigm of extracting actionable information or intelligence from a vast collection of data stored in mediums like emails, images, videos, blogs, tweets, Facebook posts, etc. A lot of businesses have started to tap into this data for assisted business decision making, improving operational costs through new trends and patterns identification and offering services/product features that they were not offering till date.
Executing your first unstructured data analysis project can be a very daunting task. It can very easily run into choppy waters if the means, expectations and outcomes are not aligned from the very beginning. In this five part series, we will list five tips from our experience in building such platforms that can help companies roll out their unstructured data analytics projects easily.
#1 – BI vs Unstructured Data vs. Big Data – Figure Out What is Right For You!
Before you embark on an unstructured data analytics project, it would serve you well to familiarize yourself with three keywords that are used interchangeably by technologists, marketers and product vendors – Business Intelligence (BI), Unstructured Data and Big Data. This will help you clarify your business objectives and let you draw a good, cost effective plan for implementing the right solution.
BI in some shape or form has been around for more than a decade. It involves extracting data from multiple sources into a large container in a performance efficient form and running reports and summaries on top of that data. BI tools have three main components – Data Extraction Engine, Data Warehouse and Reporting front end. BI tools are normally used for structured data analysis. These days most BI tools claim to support unstructured data analytics as well but they are inadequate in providing meaningful results.
You should look for a BI solution when you are looking for correlating transactional, structured data. This data could from operational systems, websites, eCommerce systems, financial systems, CRM, etc.
Unstructured data is not a new science but it has become mainstream in recent times. It deals with making sense of text written in natural languages like English and extracting insights from it. Unstructured data analytics solution has four parts – Data Extraction Engine, Data Storage Component, a collection of algorithms and rules that can be used to distill intelligence and optionally a data visualization engine.
You should consider using unstructured data analysis solution if a bulk of your data comes from mediums like emails, images, blogs, etc. and is written using natural languages. If you plan to analyze tweets and Facebook feeds then you are looking for an unstructured data solution.
Big data is a supporting technology that is used for managing large volume of data that grows at a very rapid pace. It deals with both structured and unstructured data. Depending on your needs, you may need a Big Data enabled BI solution or Big Data enabled Unstructured Data Solution. This will be based purely on the volume and speed of growth of your data. On the other hand, if your data volume is low and the growth rate is managable then a standard BI or Unstructured Data solution without Big Data support will work out fine.
It is important for you to figure out which type of solution suits your need up ahead in the project. Using a larger, more complex solution than you need will lead to high cost of implementation and operation while using a smaller solution may force you to scrap it after a very short period of use thereby leading to a very low ROI.
In our next post, we will talk about aligning the business side to the outcomes of your unstructured data analytics project.