In a world of constantly evolving technology and extensive research into the capabilities of artificial intelligence, businesses have been steadily recognizing the importance of data. “Data” seems to be a poorly defined term in the eyes of most consumers, . Look no further than major tech leaders fielding questions from journalists, or Congressmen regarding the exact details of how they absorb and share user data with other companies as well as with other users.
There’s a lot to discuss when it comes to data. The umbrella of what data refers to and where it can be applied keeps growing larger and larger, as does its role in user privacy (or lack thereof).
We recently had the chance to sit down with Nav Kesher, an expert data scientist who carries with him an impressive background of professional experience with the likes of US Airways, Amazon, and, most recently, Facebook’s Analytics department as Head of Marketplace Experience Data Science and Head of Platform Data Science.He’s also a recurring speaker at multiple iterations of Innovation Enterprise’s Big Data conferences, which have attracted industry leaders in the world of data and analytics. On top of all that, he has also been published in INFORMS Magazine, a leading voice in tech and data.
At the top of the conversation, Nav let us know that there are two big categories when it comes to data in the modern world: structured and unstructured. He broke the two down for us, explaining why there has been a significant shift of focus in recent years from one to the other.
“When I started my career in analytics, the world was still driven by structured data: data that is neatly organized, stored in mostly relational databases and readily searchable by simple, straightforward search queries or operations. As businesses evolved and started to collect, store, and manage exponentially more data, the need for predictive and prescriptive analytics started to become all the more important. As a result, the need to extract information from unstructured data (data that is usually not as easily searchable and may not conform neatly into a spreadsheet or database) became all the more important.”
It makes sense: spending all your time looking at hard facts and numbers doesn’t necessarily speak to the spaces in between those figures, which can potentially tell a company much about their users’ patterns and habits.
We asked Nav how he’s able to take such complex subject matter and make it accessible and easy to understand for consumers like us who don’t spend our work weeks studying data.
“I have always been very passionate about telling stories with data, and over the years, I have come to rely on a simple yet powerful framework to effectively convey my thoughts.”
We were surprised by Nav’s ability to simplify the matter for the average person into four major tips on how to use data and gain meaningful messages from said data. “#1: keep it simple yet thought-provoking and relatable. #2: connect data with logic. Data is descriptive, however, it is logic that enables data to become predictive and prescriptive. #3: use pictures and stories. The more memorable the story, the more it will stick with the audience. And #4: engage participation from the audience.” Nav went on to state that using data as a tool is inhibited if audiences feel as if they can’t have differing opinions or ask for clarification.
Public figures like Nav, who excel at distilling intricate concepts about data, are crucial to the development of a smarter, more strategic tech industry. He has personally invested himself in the goal of educating startups and small businesses in the ways of big data.
“Big data is a great source of valuable information, which when properly managed, processed, and analyzed, can generate valuable learnings and actionable insights for businesses. Startups and small businesses generally operate very lean, and as such don’t always have the bandwidth or the resources to invest in data tools for big data analysis. At the same time, some of these startups are solving real ‘people problems,’ and I feel very driven to help these startups fulfill their mission by advising them on data strategy and analytics.”
Nav has tried to embody this potential for connection between technology and humanitarian efforts, especially in regards to water conservation.
“While there are many fields that stand to benefit from the proliferation of big data and artificial intelligence, the one that I am most passionate about is alleviating the water crisis, now and in the future. Water has become our most critical and contested resource and I would absolutely love to see technology help us measure, plan, and more effectively allocate precious water resources where they are needed most, such as fresh drinking water and food production. Deep learning technology [a form of AI] can help analyze satellite imagery and use new-gen technologies to monitor surface water levels and fluctuations around the world and inform strategy on water utilization and conservation.”
Despite his expertise and passion for creating more intelligent approaches to outdated processes, Nav keeps himself humble, especially when it comes to discussing his peers within the industry.
“To be fair, I’m not your quintessential ‘unicorn’ data scientist. I’m someone who is interested in understanding how the world works through the lens of data and presenting my findings in a format that everyone understands. The world of analytics has always had very smart people and more recently has seen an influx of highly talented folks. This has led to a hyper-competitive space. I always try to keep abreast with the latest and the greatest technological advancements in the field of data sciences as well as improving softer skills such as storytelling, data presentation, and communication. I’m a big believer of ‘learn something new every day.’ This could be an incremental development in terms of learning a new language or a machine learning technique or a new visualization format to tell a compelling story.”
And his peers have played a major role in contributing to his own innovative ideas and techniques. Like so many other pursuits, the importance of inspiring leaders and mentors can’t be overstated.
“Throughout my career, I have had the opportunity to work with and be inspired by some amazing leaders, as well as lead and grow large data science teams. These experiences have been very relevant as I go on to advise and inspire the next generation of data scientists. For example, in the tech talks I give at universities, I make sure to tell a compelling story about the ‘people problems’ we are solving using data and how the concepts that students learn in their classrooms are relevant to data science and engineering jobs.”
Nav has led talks at USC and UC Berkeley and is always happy to nurture young talent. Even during our conversation, he was eager to encourage budding data scientists to delve even deeper.
“I would like to leave the readers with a shameless plug that if you are looking to create history and build awesome products by leveraging data science, please get in touch with me; I am always hiring for Analytics at Facebook.”
There are precious few constants in life, but here are a couple: technology will continue to change, finding new ways for humans to connect and improve, and Nav will continue to adapt to these changes, which, in the end, is one of the most human things we can do.