Business & data analytics community have been going big on unstructured data over the last few years. It is now an established fact that with over 80% of enterprise data being unstructured, businesses will either have to catch up to this elephant in the room or get annihilated by their competitors. Be it social media, online reviews, agent chats, emails and calls, blogs or industry literature, ignoring unstructured data is a dangerous stand – one that businesses will have to take at their own peril.
Even so, when one sneaks a quick glance at how businesses across various industries have been faring in gleaning actionable insights from unstructured and semi-structured data, it is hard not to wince in alarm.
Take a look at the following figure, for example, which has been sourced from a TCS 2013 Global Trend Study:
As the figure shows, despite increasing awareness about the critical role played by unstructured data in big data initiatives, most industries are still largely gorging on structured databases to fuel their decisions. The result? They are veering further and further away from what consumers want, and only a handful of players who are truly investing in the right resources to unravel unstructured and semi-structured data are latching on to the cliff.
Take the BFSI sector, for example. According to a few recent studies, 94% of banking firms are unable to deliver on personalization promise to their customers and only 30% of banks have effectively matched their analytics efforts with their business goals. The insurance segment is no paradise either: merely 29% of customers are satisfied with their current insurance provider. In fact, an Accenture study values global insurance customer churn at as much as USD 470 billion, not to mention the shocking policy lapse rate of 25% within the first 3 years.
What’s Holding Back Enterprises and Leaders?
Let’s quickly take stock of the key barriers in traditional analytics tools and solutions that prevent businesses and decision makers from making unstructured data analysis a key component of their big data initiatives:
1. Most traditional analytics tools have little or no capability to analyze unstructured data: To give a healthcare analogy here – it is like using a hammer to perform a surgery, whereas what you really need is a scalpel – that’s how ineffective traditional analytics is in analyzing unstructured and poly-structured data. While structured data such as customer & product databases, is ‘structured’ or inherently designed to fit traditional analytics methods and tools, unstructured data is not. The result? Your enterprise data remains unexplored, unanalyzed, sitting in a corner and gathering virtual dust. Over time, this massive pool of untapped data becomes ‘dark data’ or data which cannot be accessed by enterprises, thereby severely affecting their decision-making capabilities.
2. Even if unstructured data is analyzed to an extent, traditional analytics cannot contextualize it: Contextualization is a key component in the analysis of unstructured data. Let us take an example: if a page of unstructured text data is given to your analytics platform on the topic ‘Taj Mahal’ – your analytics platform has to be able to mimic human reasoning in order to identify whether that page of content is talking about ‘Taj Mahal’ – the historical monument or ‘Taj Mahal’ – the premium brand of tea. While this is a very basic example, without deep contextualization any analytics performed on unstructured data is meaningless.
3. Traditional analytics platforms pigeon-hole unstructured data into silos: In the recent past, businesses have tried to overcome the above shortcomings of traditional analytics by deploying additional tools and solutions to analyze unstructured data independently. The catch? They had no idea what to do after the analysis. Although both structured and unstructured data were being analyzed separately, there was no way to marry the two. In short, there was no unified view of enterprise data, just scattered bits and pieces of staggeringly futile attempts at analysis. Now, what can you do with that? That’s right – nothing much, unfortunately.
4. Traditional analytics cannot preserve lineage and relationships between structured data, unstructured data, contexts and user inputs: It would not be completely fair to say that no advancements whatsoever have taken place to curb the three challenges above. But still, there is something seriously wrong with the way enterprises and data analytics tools are handling unstructured enterprise data – they are unable to map, preserve and update the interrelationships that exist and evolve among the different kinds of data, the inherent contexts and the new insights/updates that pour in constantly from the background in real-time. The result? Faulty decision-making, delayed insights and distorted data views – effectively taking us back to square one.
What’s the Way Out?: Introducing MECBot Ink for Unstructured Data
About MECBot: Our flagship product MECBot is the #1 Augmented Data Management Platform for Real-Time Analytics at Scale. MECBot puts your business first by adopting the Business Domain Entity-Model approach without any dependency on the underlying databases or the structure of the data. It comes bundled with a self-service, intuitive interface and takes care of all your data management and analytics requirement in a centralized manner, including scalable deployment.
With MECBot, a business model can be directly created by CXOs or Data Scientists or Data Analysts or all of them collaboratively. MECBot directly pulls the data from the configured sources and maps it to the specified Business Domain-Entity Model. Data engineers can configure MECBot with available sources and provide the details on interlinkages. It provides advanced analytics modules that work out-of-the-box and allows you to build models like loyalty, churn and segmentation without dumping the file or moving the data around. It also keeps your analytics outcomes up-to-date by keeping the underlying view hydrated with the incoming data. MECBot allows you to create a flattened data view for the chosen entities without writing any complex SQL joins. MECBot can reuse existing views as well to get you started on the same day instead of waiting for months to get it up-and-running.
The following image summarizes how MECBot fosters augmented data management for your enterprise:
About MECBot Ink: Ink is a unique feature of MECBot geared to clean, analyze, contextualize and unify unstructured data with structured data in a seamless manner in real-time. Powered by state-of-the-art Knowledge Base that is based on the concepts of Linked Data, MECBot Ink can understand various domains and semantics across industries by enhancing the existing Universal Knowledge Base with Domain and Tribal Knowledge. Its real-time Annotation Engine detects contextual keywords/phrases in unstructured content at blazing speed and fosters deep knowledge of annotated keywords and phrases, backed by Deep Context hierarchy for on-the-fly disambiguation. With its easy-to-use and intuitive interface, you can explore annotations at scale and structure them as per your business model anytime, anywhere.
Key Highlights: With MECBot Ink, You Can –
- Structure all Unstructured data, contextualize across specific business domains, with the option to extend it across tribal knowledge bases
- Use Smart Annotation Engine and Natural Language Narration to derive on-demand insights at the pace of data generation
- Unify Structured and Unstructured data to create a semantically enriched Knowledge Graph, & deploy purpose-built models to generate insights at scale
- Make intelligent decisions and derive RoI from your data analytics in days, not months or years – augment your business intelligence quickly.
MECBot Ink in Action: Synopsis of BFSI Use Case
MECBot Ink was recently validated with one of the leading online insurance policy comparison platforms in India.
- MECBot builds insights from text data derived from voice data that is generated when customer service agents handle customer queries. Identification of policies is also done from chat data when agents response online to customer chats.
- The top policies mentioned in the overall conversation data are then clustered by time, top trends and ngrams (phrase variations) are detected over time excluding the noise, Intent Analysis is carried out based on the data collected and processed, and other features are loaded on top of it, such as chat statistics like total chats, average chat segments, conversation ratios, along with a fully functional keyword search around the policies – i.e. to identify the respective chats in which a searched policy has occurred.
This application is highly useful for Business Executives, Agent Leads and Business Managers to understand the various analysis around the customer queries, agent service, response time, product performance, policy analysis, and overall effectiveness of agent response.
This is just a glimpse of what MECBot Ink can do in BFSI – we recommend that you explore the full range of MECBot’s capabilities in Banking and Insurance.
Unstructured data is here to stay – the sooner your business gets ahead of the curve, the better it is. Are you struggling with unstructured enterprise data? Explore MECBot Ink in depth here: https://www.mecbot.ai/unstructured-data/
Wish to take a deep dive into what MECBot can do for your business? Please request a demo here: https://www.mecbot.ai/contact-us/