The banking and financial services industry is not new to the world of data and analytics.
Data analytics is now a part of every initiative spanning every function – from customer service, new product innovation, risk management, process and workforce management, marketing…the list is extensive
However, in this narrative, we hear a lot about ‘structured data’, data that is stored in well-defined schemas such as databases. But as data becomes the lifeblood of this industry, it is unstructured data that we have to look at to exploit data to its fullest potential.
What is unstructured data?
Until recently, structured and unstructured data have been kept relatively separate from one another. While the former has been used to automated and semi-automated analytics, the latter has not been a part of the decision-making process.
- Unstructured data consists of large volumes of data (typically large volumes of files) that don’t reside in traditional structured databases.
- While it has an internal structure, it does not have a predefined data model.
- It can be human or machine-generated and can come in a textual and non-textual format.
- This data is also not actively managed in a transactional system.
- It can reside in a plethora of sources…word documents, presentations, surveys, transcripts, emails, blogs, social media interactions, and the like.
- This data is usually text-heavy but also contains data such as numbers.
It is because of these ambiguities that make it difficult to leverage the power of unstructured data.
Why should organizations pay attention to unstructured data?
The BFSI industry holds vast arrays of unstructured data that remains largely under-analyzed and hardly put to use. As the volume of unstructured data increases, organizations have to take a deep dive into this data realm and use it in combination with structured data to drive better business insights.
Learning to leverage the power of unstructured data can give organizations a significant advantage.
Gain comprehensive insights
Structured data is data that has been scrubbed, cleaned, and organized into pre-packaged categories, something like a black box. Unstructured data, on the other hand, is more comprehensive and vast and can provide deep insights into all aspects of business operations.
The examination and analysis of unstructured data into decision-making helps organizations make better, smarter, and more informed decisions as it gives a comprehensive understanding of customer preferences, identify market gaps, discover unmet customer needs and process gaps, etc. These insights can then be used to boost productivity, enable new product development, and assist in serving the customer better.
Enhance customer experience
Today channels such as social media are becoming more prominent in the vocabulary of customers. Such channels hold vast volumes of unstructured data that provide deep insights into the habits and behaviors of the customers. By developing the capability to mine and analyze this data, organizations can design the best strategies to target and attract their audience to the relevant product and service offerings. The capability to use this data in predictive analytics helps organizations understand buying behaviors, which significantly help in improving cross-selling efforts and all branding initiatives.
By scanning and analyzing vast datasets of unstructured data from a variety of sources, organizations can find patterns in customer and market behavior. Through these insights, organizations can identify which products and services are most compelling for their designated audience. These insights can be further used to fine-tune product and service development and designing marketing initiatives that deliver the most impact.
Leveraging unstructured data also helps organizations improve response and issue resolution time. Data in comments and suggestions can be utilized to identify customer pain points. This can be used to make quick and timely action, deliver an elevated customer experience, and build customer loyalty.
Navigate the compliance and risk management landscape
Compliance issues can be costly in terms of time, money as well as reputation. Analyzing the unstructured data from sources such as chatbot conversations and emails, for example, can help organizations identify regulatory and compliance issues before they snowball into a negative business impact. This can be achieved using pattern recognition and sentiment analysis of speech-to-text conversations using Machine Learning and AI algorithms.
By connecting unstructured data with in-house risk-analysis data, organizations also gain deep and real-time insights into risk-relevant events. It helps risk managers identify early warnings of risk for any portfolio of assets, customers, and corporate entities.
The value of unstructured data is clear. However, organizations need to ramp up their ability to effectively mine, store, manage, and analyze it. Given the nature of this data, managing and analyzing unstructured data needs customizations. It is also important to knock down silos and create a scalable data hub using technologies such as cloud and Artificial Intelligence. Organizations now need to work on improving their capability to store, analyze, and report data from a wide variety of sources and share the same with key decision-makers to uncover the true value of unstructured data.
