| |FEBRUARY 20239To fully realize the potential of unstructured data, organizations must combine and assimilate data silos and create a scalable data lakeand ML has offered us new ways in which this data can be extracted, cleaned, and simplified, and visually displayed in ways organizations can comprehend. With the help of this data along with the structured data that's already there, organizations can create `truly' customized offerings that will enable better conversion.For instance, a bank can sieve through social media feed of their customers and identify their purchase patterns, spending behaviour, travel patterns etc. to create a customized loan or card offering that might attract the customer for their future trip or a purchase.To fully realize the potential of unstructured data, organizations must combine and assimilate data silos and create a scalable data lake. With systems to store and analyze data from a variety of sources and share it with decision-makers to act on, organizations can finally leverage it and derive enormous business value.Three Steps to achieve Total Business Intelligence using Unstructured DataIdentify Sources of DataIdentify data points around the customer that are a must for product development and marketing. Gather only relevant data and filter out the rest. Try to optimize and reduce the data to save data storage costs. Data sources can include information from online reviews, customer feedback forms, as well as information from the web and devices such as smartphones, apps, browser, etc.Design a Concrete End-objectiveThe amount of data out there is colossal. Starting without knowing what to do with the data will only lead to more confusion. Data extraction has a cost and hence must be considered only after devising a clear road map on why, what, and how? Objectives can be as easy as understanding public reaction post a marketing campaign or how a brand is perceived among the target audience or to curate tailor-made offerings for the customer. Knowing an end-objective will significantly help in carrying out data extraction and analytics.Create Data Models that Complement the End-ObjectiveOnce the data is gathered, it must be cleaned and simplified so that business intelligence tools can structure it and create reports that can facilitate business decisions. Analytics teams must create a data extraction architecture and data consumption process that works like clockwork. This streamlining can help them to optimize time and create results faster. Use of AI & ML will add to an upfront investment but will generate far superior returns with its faster data modelling and pattern recognition abilities.Final ThoughtsSince 95 percent of data generated today is unstructured data, it's important that organizations find ways to extract, analyze and make full use of it to facilitate smarter business decisions and faster conversions. It will help organizations to thrive and differentiate itself in a highly competitive environment. With futuristic tools at their disposal, organizations can understand their customer better and create truly personalized experiences. Unstructured data is the fodder that organizations of today need to create immersive digital experiences and enhance customer lifetime value.
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