Kamal and Siraj Tahir are two brothers who share a passion for data. Each is a data professional in their respective countries; Kamal in Chicago, IL and Siraj in London, England. Both are presenting during Big Data Week in Chicago 2014.
But they’re doing it with a twist. Their two-part presentation is called: “The Big Data Revolution is Expensive. Re-evolving Your Data is Not. Here’s Why.” Kamal is presenting Part 1 from Chicago at 10 am (CST) on May 6, 2013. Siraj will be joined by Kamal at 10 am (CST) on May 8, 2014 to deliver Part 2 from London.
We asked the Tahir brothers a few questions about data, where their love for data came from, and more about their Big Data Week presentations.
Q: Where did your passion for data come from?
Kamal (K): It was from an early age when dad would challenge us with what he called “time and motion study.” He asked us to determine what all can be accomplished in a given time frame. e.g. on a weekend, what can we see in the day–including travel time to different venues, time to get tickets, event or attraction duration, time of events. We would then try to optimize it by evaluating best time to leave, right sequence, mode of transportation for different legs of the itinerary, getting tickets the day before, etc. We also played a lot of Monopoly, Chess, and we played Scrabble in a chess-like mode.
Siraj (S): I guess the childhood games of ‘time and motion study’ had a big part in priming me up for it, but I would say my passion for data and information burgeoned during my higher education. Environmental engineering in general, and flood modeling more specifically in my case, requires dealing with large datasets from various sources to derive models and visualisations. My passion really burgeoned during my doctoral research where I was manipulating and munging gigabytes of data, to create models that generated further gigabytes of data – which of course I had to analyse, simplify and present in an comprehensible way to other engineers without such background.
Q: Where did the idea for data re-evolution come from?
K: Siraj and I often talk about data and how much is enough and what is really useful e.g. focus on stats in US sports vs European. This led to many discussions about utilization of data and how we see it from different angles because the diminishing marginal returns are different based on utility, or the league predictions of our favorite team Arsenal.
The signal to noise factor is critical. Without direction and domain expertise, adding more noise to improve chances of getting the signal are counterintuitive. Many investments in ERP and CRM did not deliver what was hoped because speeding up a weak process does not make it better. Similarly just adding data, is just as counter intuitive. Pragmatically you have to know what you are trying to solve and look for the the right level of data and information to drive results. Its not just a question of does it give you insights, but can you act on those insights. I worked on several products and with partners and clients and each time the focus was not just to get volumes of data, but the right one for the challenge or opportunity at hand. Siraj and I often talk about data and how much is enough and what is really useful e.g. focus on stats in US sports vs European. This led to many discussions about utilization of data and how we see it from different angles because the diminishing marginal returns are different based on utility, or the league predictions of our favorite team Arsenal.
S: Many long hours were spent talking about relative merits of statistics and utility of the information for the end user, the viewers. These discussions fomented into ideas about how data and information that we already collect for different purposes can be combined and utilized for improving our knowledge and understanding of the operation of systems around us, both man made and the environment. An example would be modeling the likelihood of a water mains failure based on information about pipe material, age, ground conditions, water pressure, and weather conditions – and using it to plan infrastructure investment.
Q: What are 2 or 3 things a person will learn from watching your presentations?
K: An appreciation of how just some additional data points can drive real gains, how one plus one can be many if its the right data or 1+1 can be zero or 1.3, if its not the right data. I think a viewer will learn more on how actionability is just as important as the insight. Viewers will come away with an understanding about the balance between domain knowledge, data expertise, and other roles; which is critical to avoid having a lopsided team provide lopsided solutions. I believe the final takeaway will be why marketers are equally disciplined as engineers on the optimal solutions, even though our definition of optimal may be different.
S: First, I think viewers will see that the engineers are equally excited as marketers on how big data principles can be used to provide further insight and intelligence and help us do our jobs better.
Second, they will learn why domain expertise is imperative to set up data collection activities. Lastly they will understand why we need the expertise of a data scientist to show what can be done with the data that already exists.
Big Data Week in Chicago is a partnership between Cook County Government and BLUE 1647. For more information about the events taking place visit: https://bigdataweek.com, https://bigdataweek.com/chicago or follow Big Data Week on Twitter at @CHGbigdataweek, @bigdataweek. Big Data Week uses hash tags #bdw14, and #bdw14chi.