Wired ScienceAs big data has grown, so has the need for big data scientists in the commercial world. The more sensors and algorithms there are that generate data all over the planet, the greater the need has become for professionals who can interpret and explain what that data suggests. In short, data science today is really the discipline of turning raw numbers into business intelligence. Data science as an academic specialty has been around for decades. In the 1970s, as computers shrank from room-sized monsters to desktop companions, data science was born from a combination of computer science, visualizations, advanced math and statistics.
3 Emerging TrendsToday three of the most significant emerging trends in data science involve predictive analysis, machine learning, and data mining. Predictive Analysis Forecasting and more accurate predictions of emerging trends have many applications, from finance to marketing. Intelligent software can model customer behavior and make reasonable conclusions about what they will want to do next, such as the Amazon recommendation engine. It also refers to enterprise software like adaptive ERP systems that make changes to planning in real time and immensely simplify project management. Machine Learning There's a limit to how much programming can do. Machine learning allows computers to take it from there and program the next phase themselves. This technology has gotten a great deal of press as precursors to artificial intelligence, especially IBM's Watson. Platforms that analyze data in new ways are already beginning to have a bigger impact on enterprise operations than the changes following the introduction of SQL databases two decades ago. Look for many aspects of the business landscape to be disrupted suddenly as more businesses gain access to the massive compute powers of these new data analysis platforms. Data Mining Raw data doesn't help anyone. Data mining pulls useful knowledge out of that data, but until recently you had to be a data scientist to manage it. New dashboards for data mining and simple tools like the single letter programming language R brings data mining to non-technical business professionals. This is part of the larger evolution of technology in society, which typically starts with formulas for experts and ends with a push-button interface for everyone. Large companies are now democratizing data mining by embedding visualization tools into more familiar software packages.
Big Data PredictionsCompanies that rely on big data for decision making have been 6 percent more profitable than more traditional firms. Data scientists can testify that even a small competitive advantage like this becomes decisive when compounded over time. Over the next few years, this small change will drive major industry shakeups as IoT sensors proliferate and access to virtual data centers in the cloud becomes more affordable for smaller businesses. The biggest hurdle now will be finding enough professionals with data science skills to analyze that data. This new business landscape will favor the companies that prepare themselves now to learn from and act on that flood of data. These companies will redefine their respective industries. This year, the Harvard Business Review asked C-level execs to predict which industries would see the biggest disruptions due to data science in 2017. The top five were media, telecom, consumer financial, retail/technology (tied) and insurance. Consider how vastly different the business landscape will look as top performers in these fields are upended. That will drive significant societal changes as people adapt to the new reality, just as smartphones and cloud-based apps ushered in a global mobile revolution. Data science is on the cusp of bringing sweeping changes to the way people live and the way businesses operate in the very near future.
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