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Big Data: The Art of Data Science
Big Data methodologies and data analytics are increasingly being talked about and applied by organizations for solving problems and assisting with the making of decisions. The main problem at this point ofthe technology cycle is though that while the tools for data processing and analytics abound, the people with to right skills to appropriately find meaning in the results are still quite scarce.
Phil Simon, technology author, told columnist Brian Sommer that “many of us yearn for a simpler time. As Ray Kurzweil notes, we are living in an era of accelerating technological change. Insights today may no longer be accurate in a month. It’s imperative upon professionals to question long-standing standard reports, KPIs, dashboards, and traditional reporting tools. Yes, they’re still relevant, but they no longer can tell the whole story. What’s more, they don’t allow for true data discovery, especially with respect to vast amounts of unstructured data.”
Volker Markl, a professor and chair of the database systems and information management group at the Technische Universität Berlin, said that “Data analysis is becoming more complex. As a discipline, data science is challenging in that it requires both understanding the technologies to handle the data, such as Hadoop and R, as well as the statistics and other forms of mathematics needed to harvest useful information from the data.”
Anthony Goldbloom, founder and CEO of Kaggle, said that “we believe very strongly that data science is an incredibly high-leverage activity. … And because it is high-leverage, I think data scientist recruiting is challenging, and it is very important to do well. The right data scientist can take on a very difficult problem and make a huge positive impact, and I believe a poor data scientist or a less-experienced data scientist on the same problem with the same data can do a lot of damage.”
Guy Cuthbert, managing director at visual analytics firm Atheon Analytics, said that “the gap with machine learning and all the rest of the computer sciences at the moment is that as yet there is no machine inspiration. The inspiration comes from humans understanding how to interpret signals in the data.”
