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The Analytics of Digital Transformation, according to TCS


Lipika Dey, in this interview before her talk at next month's LT-Accelerate conference, discusses consumer and market analytics and techniques to extract actionable information from text and social media data.



By Seth Grimes (AltaPlana), Nov 2014.

Next month’s LT-Accelerate conference will be the third occasion I’ve invited Lipika Dey to speak at a conference I’ve organized. She’s that interesting a speaker. One talk was on Goal-driven Sentiment Analysis, a second on Fusing Sentiment and BI to Obtain Customer/Retail Insight. (You’ll find video of the latter talk embedded at the end of this article.) Next month, at LT-Accelerate in Brussels, she’ll be speaking on a particular topic that’s actually of quite broad concern, E-mail Analytics for Customer Support Centres.

As part of the conference lead-up, I interviewed Lipika regarding consumer and market analytics, and — given her research and consulting background — techniques that best extract practical, usable insights from text and social data. What follows are a brief bio and then the full text of our exchange.

Dr. Lipika Dey, Tata Consultancy Services Dr. Lipika Dey, senior consultant and principal scientist at Tata Consultancy Services

Dr. Lipika Dey is a senior consultant and principal scientist at Tata Consultancy Services (TCS), India with over 20 years of experience in academic and industrial R&D. Her research interests are in content analytics from social media and news, social network analytics, predictive modeling, sentiment analysis and opinion mining, and semantic search of enterprise content. She is keenly interested in developing analytical frameworks for integrated analysis of unstructured and structured data.

Lipika was formerly a faculty member in the Department of Mathematics at the Indian Institute of Technology, Delhi, from 1995 to 2006. She has published in international journals and refereed conference proceedings. Lipika has a Ph.D. in Computer Science and Engineering, M.Tech in Computer Science and Data Processing, and 5 Year Integrated M.Sc in Mathematics from IIT Kharagpur.

Our interview with Lipika Dey –

Q1: The topic of this Q&A is consumer and market insight. What’s your  personal background and your current work role, as they relate to these domains?

Lipika Dey: I head the research sub-area of Web Intelligence and Text Mining at Innovation Labs, Delhi of Tata Consultancy Services. Throughout my academic and a research career, I have worked in the areas of data mining, text mining and information retrieval. My current interests are focused towards seamless integration of business intelligence and multi-structured predictive analytics that can reliably and gainfully use information from multitude of sources for business insights and strategic planning.


Q2: What roles do you see for text and social analyses, as part of comprehensive insight analytics, in understanding and aggregating market voices?

Lipika Dey: The role of text in insight analytics can be hardly over-emphasized.

Digital transformation has shifted control of the consumer world to consumers from providers. Consumers — both actual and potential — are demanding, buying, reviewing, criticising, influencing others, and thereby controlling the market. The decreasing cost of smart gadgets is ensuring that all this is not just for the elite and tech-savvy. Ease of communicating in local languages on these gadgets is also a contributing factor to the increased user base and increased content generation.

News channels and other traditional information sources have also adopted social media for information dissemination, thereby paving the way for study of people’s reactions to policies and regulations.

With so much expressed and exchanged all over the world, it is hard to ignore content and interaction data to gather insights.


Q3: Are there particular tools or methods you favor? How do you ensure business-outcome alignment?

Lipika Dey: My personal favourites for text analytics are statistical methods and imprecise reasoning techniques used in conjunction with domain and business ontologies for interpretation and insight generation. Statistical methods are language agnostic and ideal for handling noisy text. Text inherently is not amenable to be used within a crisp reasoning framework. Hence use of imprecise representation and reasoning methodologies based on fuzzy sets or rough sets is ideal for reasoning with text inputs.

The most crucial aspect for text analytics based applications is interpretation of results and insight generation. I strongly believe in interactive analytics platforms that can aid a human analyst comprehend and validate the results. Ability to create and modify business ontology with ease and view the content or results from different perspectives is also crucial for successful adoption of a text analytics based application. Business intelligence is far too entrenched in dashboard-driven analytics at the moment. It is difficult to switch the mind-set of a whole group at once. Thus text analytics at this moment is simply used as a way to structure the content to generate numbers for some pre-defined parameters. A large volume of information which could be potentially used is therefore ignored. One possible way to practically enrich business intelligence with information gathered from text is to choose “analytics as a service” rather than look for a tool.

As a researcher I find this the most exciting phase in the history of text analytics. I see a lot of potential in the yet unused aspects of text for insight generation. We are at the confluence where surface level analytics has seen a fair degree of success. The challenge now is to dive below the surface and understand intentions, attitudes, influences, etc. from stand-alone or communications text. Dealing with ever-evolving language patterns that are also in turn influenced by the underlying gadgets through which content is generated just adds to the complexity.



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