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Data Wonk: A Q&A With Wyndham Analytics Director Srinidhi Melkote

Mr. Melkote on the Misconceptions of Big Data, Upcoming Data-Related Services and More

By Published on .

Each week DataWorks will highlight the work and ideas of the people who make data come to life for marketers.

Lots of companies are just starting to think about hiring people to help manage and analyze customer data. In the hospitality industry, however, data crunchers are relatively common. Srinidhi Melkote heads up a robust team of 14 analytics people for Wyndham's Exchange and Rentals, a division of hospitality company Wyndham Worldwide that offers time-share exchanges and vacation rentals.

It's his job to help key decision makers derive insights for things such as dynamic pricing and inventory management from data through the use of forecasting and mathematical optimization models, software design and development, and business-intelligence applications. While his work is often associated with left-brain thinking, right-brain creativity helps set apart people such as Mr. Melkote, director of analytics in the Wyndham Exchange and Rentals Analytics group.

Srinidhi Melkote, director of analytics, Wyndham Exchange and Rentals
Srinidhi Melkote, director of analytics, Wyndham Exchange and Rentals

For instance, when he was asked to help determine whether the London 2012 Olympics would have an impact on domestic bookings there, Mr. Melkote turned to data gathered by the company during other international sporting events such as the Beijing 2008 Olympics or the 2010 FIFA World Cup in South Africa. He even uses UFO sightings data to evaluate statistical programming tools. "You need a mix of technical skills and a bit of creativity to think outside the box," said Mr. Melkote.

With Wyndham since 2005, Mr. Melkote has a bachelor's degree in engineering and a master's degree in computational finance.

Adverstising Age: What's a common misconception about big data you'd like to dispel?

Srinidhi Melkote: Unfortunately, the term "big data" is not well defined. This leads to all kinds of misconceptions. One common definition places emphasis on the adjective "big," referring to the size of the data to be collected and analyzed. This leads to a common misconception, which is that one needs "big" data to get big value. This is not always the case. The practice of statistics is all about deriving good insights for decision making from relatively small samples of representative data. There is a lot of value to be had from "small" data, provided it's the right kind of data, the right questions are asked, and people with the right skills are hired to interpret this data. This does not mean one shouldn't collect more data; it's just that most of the value, in the form of information and insights, comes from a small portion of "big" data.

Ad Age: What do you wish marketers would understand about what data scientists do?

Mr. Melkote: There is a general assumption among marketers and business people, in general, that all they need to do is provide data scientists access to their data and deep insights will follow with minimal effort from their end. This is rarely the case. Data scientists have the most success if they are in constant touch with business units, ask relevant questions and let answers to those questions guide their analyses. It's hard to get relevant business context just by looking at data.

Ad Age: What's the biggest problem with data-science people as they navigate the world of marketing?

Mr. Melkote: Marketing is a complex field and has multiple short and long-term objectives with overlapping effects and trade-offs. It can be hard to quantify these objectives and define the right metrics to track. For instance, one common objective of pricing and promotions is to set optimal product prices or run promotions to increase overall revenue. It's fairly straightforward to build an algorithm that analyzes historical data to gauge impact of transactions to various price changes, and based on that, set a price. But this does not even begin to capture the complexity that is present in the real world. In the short term, it may turn out that giving healthy product discounts is the best approach but constantly running promotions may lead to customers expecting deeper discounts every time resulting in revenue losses in the longer term.

While this may seem obvious to a marketer or business person, it is usually not apparent that such effects are at play by just looking at the data in a cursory fashion. If a data scientist doesn't know to explicitly model for these, the price recommendations their algorithms provide will not be accurate. This is just one example of the sort of business knowledge and intuition that takes time to develop. Data scientists should expect to face a significant learning curve to understand these nuances in order to build such business beliefs into their models.

Ad Age: What's the coolest or strangest type of data set you've ever worked with and why?

Mr. Melkote: While I get to work with a lot of interesting data in my day job, my favorite examples of cool or strange data come from publicly available information. One such is a dataset of about 60,000 UFO sightings. It has data like the location of the sighting, date of the sighting, the shape of the UFO, along with some text describing their experience. I first came across this data set when I was reading a programming book last year and now use it from time to time when I am evaluating new statistical programming tools. If I recall correctly, most people who sight UFOs tend to describe their sightings as just a flash of light; quite a few people claim to have discerned the shape to be a circle or triangle. White is the most popular color for UFO lights while red and green are not far behind.

Ad Age: What educational fields of study and professional backgrounds help develop the best data people?

Mr. Melkote: The best data people tend to come from fields which have a good tradition of combining theory with applications, which are computationally intensive. Examples include applied mathematics, operations research, statistics and computer science. There is increasing interest in creating data science and analytics as a separate professional discipline as evidenced by the number of universities now offering master's degrees in these areas. As for relevant professional backgrounds, working on data analysis in industries with a good history of collecting and analyzing data helps a lot. Examples of such industries include financial services, retail, travel and e-commerce.

Ad Age: What types of data-related services do you expect more of in the coming year?

Mr. Melkote: A lot of consumer facing services are actually very data intensive but the key has always been to make the user experience so seamless that people actually don't realize these are data related at all (e.g. the Google search bar). All of the complexity is on the back end and not exposed to end users. As someone who's involved in designing and developing these back end data driven processes, I feel that the process of discovering insights from the data is still a fairly manual and cumbersome process requiring specialized skills. If you view the data scientist as a "user" of the data and analysis tools, the user experience is not very pleasant today. A data scientist spends a lot of time extracting data from different sources, "cleaning" the data and formatting it into something usable. This is then followed by visualizing the processed data, leaving less time to frame and test hypotheses and build algorithms or models. One needs to have expertise in a variety of technologies just to get the data in a usable format before even the true value added work can be done.

I would like to see some better tools which help ease this burden and streamline the data scientist's workflow. There has been progress in this area both in the open source world of R [a statistics language], Python [a programming language], etc., and also from established commercial vendors such as SAS. Also, several startups such as BigML [machine learning] and Precog [analytics platform] are addressing this problem from different angles. This is not an easy problem and it's hard to make predictions as technology is changing quite rapidly.

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