DataBeat Recap: Data Science Today and Tomorrow
With a topic as broad as “data science,” a conference like DataBeat has to start off by defining what exactly that is. This was done nicely by the first two talks of the day, which covered the current role of data science as well as its possible future applications. Let’s take a closer look:
The day kicked off with Matt Marshall, CEO of VentureBeat, speaking on the “State of the Data Science Movement: The Role of Data Science in Driving our Digital World.” His talk emphasized the sheer ubiquity of data science in today’s world. We have more data, tools, opportunities, visibility, and education available to us than ever before, and each of these factors plays a role in changing the data landscape.
What does that mean? The tools we use today, for example, like Hadoop and MapReduce, were almost non-existent a decade ago. Even if we had the same types and formats of data back then, access to these new tools means that today we can extract meanings heretofore inaccessible to us. A whole new world of data analysis is literally at our fingertips, and all because we have found new ways to explore the same old data.
It’s not just the tools of the trade that are changing. Marshall quoted the infamous McKinsey report which found that by 2018, there will be 4-5 million jobs in the US requiring data analysis skills. That’s 2018, of course, but the job outlook right now certainly isn’t too shabby; in fact, job postings for “Data Science” increased 15 thousand percent last year alone! Citing new tools, the ability to store increasingly large amounts of data, and a job market hungry for new data science talent, Marshall did a good job of convincing us that this is a field that’s here to stay.
Next up was Scott Yara, Senior Vice President of Products & Platform at Pivotal, with his talk on the“Future of Data Science: Relationship Between Data and Apps.” He challenged the audience to consider how we can think about data in a broader context. It’s time to move past simply analytics and data processing: Yara believes that these are just small pieces of a much larger pie, and that bridging worlds is crucial for the future of this discipline. Here, he quoted William Gibson, saying that “the future is here! It’s just not evenly distributed…”
Not evenly distributed yet, to be sure, but we’re slowly getting there. After all, as Yara pointed out, data science was the province of nerds for years, but now “big data” is common parlance. The buzz began to spread to the masses somewhere around 2008, first via specialty news outlets like Wired, then to the business world by way of the Economist in 2010, and nowadays “big data” and “data science” are buzzwords you can find in all mainstream media.
Yara shared his definition of data science, explaining how we’re going to get from where we are to where we need to be. He believes the keys to this transition are:
- A great deal of collaboration around data
Standards in particular are crucial because open source has become such an important part of the process. While this is great news for the field, it also means that we need to be extra vigilant about keeping datasets clean and organized.
Yara believes that what will ultimately allow this movement to succeed are inspired, passionate people. Precisely because people are passionate and data is so mainstream, however, all businesses are under pressure to become software businesses. For the first time, enterprises are finding themselves faced with a new question, asking themselves not just “how do we work with big data?” but rather “how can we be a software company?” As evidence, Yara shared the example of American Express, which is now looking towards creating user-friendly apps for its customers, not just financial services.
It’s taken ten years to get to this point, but data and apps now play a huge role in building a company. New software is being published faster than ever, and their success is becoming more and more crucial to the success of businesses that back them. Companies that have innovated through software and data have usurped first generation behemoths like Borders and Kodak that lagged behind. At this point, Yara quoted Hulya Farinas, a data scientist at Pivotal, saying that “it’s a very tiny jump from developer to data scientist…” It’s a jump, however, that companies will need to commit to if they want to stay relevant.
Want more from DataBeat? Check out our Wednesday recap, or another in-depth post:
- DataBeat Recap: Data Science Tackles Fashion
- DataBeat Recap: Integrating Data Science into Medical Research