Data Analytics at NBCUniversal. Interview with Matthew Eric Bassett.
“The most valuable thing I’ve learned in this role is that judicious use of a little bit of knowledge can go a long way. I’ve seen colleagues and other companies get caught up in the “Big Data” craze by spend hundreds of thousands of pounds sterling on a Hadoop cluster that sees a few megabytes a month. But the most successful initiatives I’ve seen treat it as another tool and keep an eye out for valuable problems that they can solve.” –Matthew Eric Bassett.
I have interviewed Matthew Eric Bassett, Director of Data Science for NBCUniversal International.
NBCUniversal is one of the world’s leading media and entertainment companies in the development, production, and marketing of entertainment, news, and information to a global audience.
Q1. What is your current activity at Universal?
Bassett: I’m the Director of Data Science for NBCUniversal International. I lead a small but highly effective predictive analytics team. I’m also a “data evangelist”; I spend quite a bit of my time helping other business units realize they can find business value from sharing and analyzing their data sources.
Q2. Do you use Data Analytics at Universal and for what?
Bassett: We predict key metrics for the different businesses – everything from television ratings, to how an audience will respond to marketing campaigns, to the value of a particular opening weekend for the box office. To do this, we use machine learning regression and classification algorithms, semantic analysis, monte-carlo methods, and simulations.
Q3. Do you have Big Data at Universal? Could you pls give us some examples of Big Data Use Cases at Universal?
Bassett: We’re not working with terabyte-scale data sources. “Big data” for us often means messy or incomplete data.
For instance, our cinema distribution company operates in dozens of countries. For each day in each one, we need to know how much money was spent and by whom -and feed this information into our machine-learning simulations for future predictions.
Each country might have dozens more cinema operators, all sending data in different formats and at different qualities. One territory may neglect demographics, another might mis-report gross revenue. In order for us to use it, we have to find missing or incorrect data and set the appropriate flags in our models and reports for later.
Automating this process is the bulk of our Big Data operation.
Q4. What “value” can be derived by analyzing Big Data at Universal?
Bassett: “Big data” helps everything from marketing, to distribution, to planning.
“In marketing, we know we’re wasting half our money. The problem is that we don’t know which half.” Big data is helping us solve that age-old marketing problem.
We’re able to track how the market is responding to our advertising campaigns over time, and compare it to past campaigns and products, and use that information to more precisely reach our audience (a bit how the Obama campaign was able to use big data to optimize its strategy).
In cinema alone, the opening weekend of a film can affect gross revenue by seven figures (or more), so any insight we can provide into the most optimal time can directly generate thousands or millions of dollars in revenue.
Being able to distill “big data” from historical information, audiences responses in social media, data from commercial operators, et cetera, into a useable and interactive simulation completely changes how we plan our strategy for the next 6-15 months.
Q5. What are the main challenges for big data analytics at Universal ?
Bassett: Internationalization, adoption, and speed.
We’re an international operation, so we need to extend our results from one country to another.
Some territories have a high correlation between our data mining operation and the metrics we want to predict. But when we extend to other territories we have several issues.
For instance, 1) it’s not as easy for us to do data mining on unstructured linguistic data (like audience’s comments on a youtube preview) and 2) User-generated and web analytics data is harder to find (and in some cases nonexistent!) in some of our markets, even if we did have a multi-language data mining capability. Less reliable regions, send us incoming data or historicals that are erroneous, incomplete, or simply not there – see my comment about “messy data”.
Reliability with internationalization feeds into another issue – we’re in an industry that historically uses qualitative and not quantitative processes. It takes quite a bit of “evangelicalism” to convince people what is possible with a bit of statistics and programming, and even after we’ve created a tool for a business, it takes some time for all the key players to trust and use it consistently.
A big part of accomplishing that is ensuring that our simulations and predictions happen fast.
Naturally, our systems need to be able to respond to market changes (a competing film studio changes a release date, an event in the news changes television ratings, et cetera) and inform people what happens.
But we need to give researchers and industry analysts feedback instantly – even while the underlying market is static – to keep them engaged. We’re often asking ourselves questions like “how can we make this report faster” or “how can we speed up this script that pulls audience info from a pdf”.
Q6. How do you handle the Big Data Analytics “process” challenges with deriving insight?
For example when:
- -capturing data
- -aligning data from different sources (e.g., resolving when two objects are the same)
- -transforming the data into a form suitable for analysis
- -modeling it, whether mathematically, or through some form of simulation
- -understanding the output
- -visualizing and sharing the results
Bassett: We start with the insight in mind: What blind-spots do our businesses have, what questions are they trying to answer and how should that answer be presented? Our process begins with the key business leaders and figuring out what problems they have – often when they don’t yet know there’s a problem.
Then we start our feature selection, and identify which sources of data will help achieve our end goal – sometimes a different business unit has it sitting in a silo and we need to convince them to share, sometimes we have to build a system to crawl the web to find and collect it.
Once we have some idea of what we want, we start brainstorming about the right methods and algorithms we should use to reveal useful information: Should we cluster across a multi-variate time series of market response per demographic and use that as an input for a regression model? Can we reliably get a quantitative measure of a demographics engagement from sentiment analysis on comments? This is an iterative process, and we spend quite a bit of time in the “capturing data/transforming the data” step.
But it’s where all the fun is, and it’s not as hard as it sounds: typically, the most basic scientific methods are sufficient to capture 90% of the business value, so long as you can figure out when and where to apply it and where the edge cases lie.
Finally, we have an another excited stage: find surprising insight from the results.
You might start by trying to get a metric for risk in cinema, and you might find a metric for how the risk changes for releases that target a specific audience in the process – and this new method might work for a different business.
Q7. What kind of data management technologies do you use? What is your experience in using them? Do you handle un-structured data? If yes, how?
Bassett: For our structured, relational data, we make heavy use of MySQL. Despite collecting and analyzing a great deal of un-structured data, we haven’t invested much in a NoSQL or related infrastructure. Rather, we store and organize such data as raw files on Amazon’s S3 – it might be dirty, but we can easily mount and inspect file systems, use our Bash kung-fu, and pass S3 buckets to Hadoop/Elastic MapReduce.
Q8. Do you use Hadoop? If yes, what is your experience with Hadoop so far?
Bassett: Yes, we sometimes use Hadoop for that “learning step” I described earlier, as well as batch jobs for data mining on collected information. However, our experience is limited to Amazon’s Elastic MapReduce, which makes the whole process quite simple – we literally write our map and reduce procedures (in whatever language we chose), tell Amazon where to find the code and the data, and grab some coffee while we wait for the results.
Q9. Hadoop is a batch processing system. How do you handle Big Data Analytics in real time (if any)?
Bassett: We don’t do any real-time analytics…yet. Thus far, we’ve created a lot of value from simulations that responds to changing marketing information.
Q10 Cloud computing and open source: Do you they play a role at Universal? If yes, how?
Bassett: Yes, cloud computing and open source play a major role in all our projects: our whole operation makes extensive use of Amazon’s EC2 and Elastic MapReduce for simulation and data mining, and S3 for data storage.
Q11 What are the main research challenges ahead? And what are the main business challenges ahead?
Bassett: I alluded to some already previously: collecting and analyzing multi-lingual data, promoting the use of predictive analytics, and making things fast.
Recruiting top talent is frequently a discussion among my colleagues, but we’ve been quite fortunate in this regards. (And we devote a great deal of time in training for machine learning and big data methods.)
Qx Anything else you wish to add?
Bassett: The most valuable thing I’ve learned in this role is that judicious use of a little bit of knowledge can go a long way. I’ve seen colleagues and other companies get caught up in the “Big Data” craze by spend hundreds of thousands of pounds sterling on a Hadoop cluster that sees a few megabytes a month. But the most successful initiatives I’ve seen treat it as another tool and keep an eye out for valuable problems that they can solve.
Matthew Eric Bassett -Director of Data Science, NBCUniversal International
Matthew Eric Bassett is a programmer and mathematician from Colorado and started his career there building web and database applications for public and non-profit clients. He moved to London in 2007 and worked as a consultant for startups and small businesses. In 2011, he joined Universal Pictures to work on a system to quantify risk in the international box office market, which led to his current position leading a predictive analytics “restructuring” of NBCUniversal International.
Matthew holds an MSci in Mathematics and Theoretical Physics from UCL and is currently pursuing a PhD in Noncommutative Geometry from Queen Mary, University of London, where he is discovering interesting, if useless, applications of his field to number theory and machine learning.
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