Data science life cycle

How Data Scientists Can Adapt to the World of Business

In brief:  DS is no longer reserved to the backroom of IT departments, tech companies, and university research labs. It has become an integrated part of a wide variety of businesses, but it’s common for the motivation found in data scientists and the motivation needed to drive a company to success to differ widely. For example, a data scientist working in a research facility may spend quite some time investigating a particular theory, only to find it is unviable or that the results lack application in the real world. Businesses, however, don’t have this luxury. They are driven by the bottom line and need to see quick results that will have a measurable impact on their companies. It is therefore imperative that data scientists learn how to adapt their skills to suit these needs. This article looks at ways that data scientists can change to suit the modern workplace.

Why this is important: Theoretical knowledge without practical application won't get you far.  But this isn’t a one-way street; as data scientists struggle to adapt to corporate culture, so too do businesses struggle to understand the discipline and mindset of DS. By opening conversations, the two areas can learn from each other and thrive.

 The 7 Habits of Good Data Scientists

In brief:  Data science is not necessarily one single thing, skillset, or methodology. This is why data science is always said to be an “interdisciplinary branch” of science that combines mathematics, human behavioral and workflow studies, flexible use of logic systems, and a core employment of algorithms. This makes being a data scientist pretty hard work, as if algorithmic logic wasn’t already pretty tough. More than just data analytics, more than just big data insight, more than just the ability to handle new streams of raw unstructured data and more than just knowing how to drive a database while blindfolded, data scientists have to understand business and be flexible super-performers. So, what core attributes make a good data scientist? This article by Forbes seeks to answer just that, with Simon Asplen-Taylor, interim chief data officer (CDO) and founder at data analytics advisory company Datatick outlining seven defining characteristics. 

Why this is important: By looking at what industry experts consider to be key to being a good data scientist, we can see how we measure up and take action to improve our skills!

  Data Scientists will be the CEOs of the Future

In brief:  With companies becoming increasingly aware of the power of data, they are frequently employing data scientists in more senior roles. This article cites a report by Telsyte which flags the importance of big data analytics in decision making and claims that Australian organisations will have at least one senior team member specialising in big data in the near future. It also looks at examples of data scientists who have made the leap to becoming successful CEOs such as Sebastian Thrun, founder of Google X and Udacity; Brad Peters, founder of Birst; Jim Goodnight, CEO of SAS, the world’s leading business analytics software vendor; and Thomas Thurston founder and CEO of Growth Science. CEOs of the future will need to have a greater awareness of data, than ever before. 

Why this is important: As data scientists, we have the ability to apply logical reasoning and analytics to complicated scenarios, free of corporate politics. This cool-headed approach means that data scientists will generally make good CEOs and company founders. The future is likely to see data-driven CEOs in increased numbers. 

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Data Science Can Tell Us Why Things Break Down 

In brief:  The University of Twente’s professor in Dynamics based Maintenance at the faculty of Engineering Technology, Tiedo Tinga, has sought to discover why do things break down – and when. This article highlights how he believes data science has the answers to that question. His research uses the Physics of Failure in order to focus on the detection and prediction of failures in systems. Tinga states that data science and material science are able to work side-by-side in order to recognise patterns and analyse them; “we can achieve added value by combining different data sources. If you can combine your specific failure data with variable additional data, then you use the power of data science. It’s much more than a few homogeneous primary datasets.” Tinga acknowledges reliability is not a major concern for all industries, but stresses its importance to some, such as aviation, where data science is able to make a huge contribution.  

Why this is important: DS and material science are sometimes seen as being in competition with each other to produce the best results. As data scientists, it’s important for us to realise that our work doesn’t exist in a vacuum and this article looks at how academics are using the benefits from both in order to produce better outcomes.