2017 Big Data Resolutions
2017 Big Data Resolutions
Machine learning augments the shortage of data scientists.
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Adam Wilson, CEO of Trifacta shares his vision for big data in the coming year.
Data is fueling an incredible pace of innovation and in turn, those new innovations are creating more diverse types of data, including everything from machine generated metadata to drone telemetry data. For data scientists and analysts, this means a new wealth of data for finding insights and creating business value, but it also means more challenges in understanding and working with these new types of data. In 2017, the tools that support data preparation and visualization in analytics will grow in importance.
As traditional industries (e.g. oil and gas) continue to digitize aspects of its business, they’ll eventually catch up to industries on the forefront of digital transformation. Healthcare, marketing and finance were industries leading the trend in 2016; and in 2017 manufacturing will tap into the data being generated from IoT to become a leader in digital transformation.
Given the shortage of data scientists and the continuing growth of data, many are pointing to machine learning and automation as means to scale analytics efforts. Machine learning and automation will become more effective in 2017, but will not yet equal the hype. Enterprises will realize that machine learning is best used to augment humans and make them more effective, not replace them.
Data visualization has exploded and become the centerpiece of many enterprises analytics strategies. But up to this point, no matter how illustrative those visualizations are, they tend to be static. In 2017, we’re going to begin to see greater adoption of interactive visualizations that support much more dynamic analytics. With big data, there is rarely one question and one answer. A single question can have dozens of answers, and spawn more questions, depending on the ultimate goal of the analysis. Supported by a more iterative data analytics pipeline, interactive visualizations will begin to become a reality in 2017.
What are your thoughts regarding the direction of big data in 2017?
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