Six New Data Science and Machine Learning Trends to Watch in 2017
Sundeep SanghaviCo-founder & CEO, DataRPM
As we embark on 2017, the exciting potential of data science and machine learning is just waiting to be unleashed in ways that will benefit people, businesses and economies across the world. Here are six of the key trends that will shape the development of this incredible technology this year.
1. Automated Machine Learning Will Dominate the Data Science Industry
For those that aren’t au fait with the term, Machine Learning (ML) is a subfield of computer science that sees computers (and other devices) gifted with the ability to learn things without explicit programming.
This year, we will see Automated ML dominate the data science industry. Part of the reason for this is that Data Science and ML are intrinsically linked. After all, machines can only learn based on the data they have to work with, and these two fields cannot really exist without each other. Budding data scientists will need to know how machines can more easily learn things based on the information they are given.
Over the coming year, companies and startups looking to work in Data Science will seek out those candidates with a sound knowledge of ML. Those without this basic building blocks need not apply.
2. Traditional Business Intelligence Will Be Replaced by the Internet of Things
Traditional methods of gathering business intelligence will go the way of the Dodo as the Internet of Things (IoT) makes it easier than ever to gather this sort of information. Essentially, as the rise of sensor-driven devices engulfs all facets of society, about 50 percent of business intelligence (BI) platforms will capitalize on event data streams to find meaningful trends.
Sound like a long shot? It shouldn’t. Analysts from Gartner predicted this two years ago, so it isn’t exactly news. In the same way that ML will perform a not-so-hostile takeover of Data Science, so the IoT will colonize business intelligence. Time to skill up.
3. Money’s No Object
That is to say, spending on Data Science and ML is about to rocket. This is because, as an industry, Big Data and Data Science Analysis are moving out of an ‘emerging’ stage and into an established, more mature one. Currently, just 30 percent of businesses have experienced the Big Data revolution, but all of that is about to change.
To use an insect analogy, prior to 2016, Big Data was a caterpillar, crawling along slowly as businesses struggled to get their heads around what the term meant. Well, 2016 was one Big Data chrysalis, and now 2017 is set to be the year that this technology emerges as a beautiful butterfly—and everyone wants a look.
Analysts also predict that between 2015 and 2019, Big Data spending will increase by more than 50 percent to $187 billion, a net increase of $64bn in just four years. This year, 2017, sits neatly in the middle of that expansion so expect to see a sharp increase in spending across the sector.
4. Validate and Explain
Part of the reason for the Big Spending boom is due to an increasing need to see validation and explanation of the data produced by all these millions of machines and events. After all, businesses investing in all this Data Science collection will, rightly, want to understand it.
As the industry currently sits, the United States is lacking some 200,000 data scientists, the people needed to make sense of all the information gathered and processed by numerous machines and algorithms. Naturally, this talent won’t come cheap and data scientists will be in great demand over the next 12 months.
5. The Industrial IoT
A lot of the time, IoT news focuses on lifestyle fads such as watches that can turn on your TV and other wrist bands that can predict when you might die based on the amount of exercise you do. Ok, that might be a bit of an exaggeration, but you get the point.
You know those annoying processes that are done manually? Things like finances, monitoring sales, checking balance sheets and so on? Well, machines are about to remove those boring burdens from your tired shoulders.
As technology gains the ability to manage tasks and processes at scale, traditional business management will become antiquated. Cloud accounting is a great example of one area where that is already happening. Invoices and payments are automatically processed in a fuss-free manner that frees up time for the more important aspects of running a business.
To the Future
Of course, the future of ML and Data Science is far from limited to this selection of trends. The industry as a whole is only just emerging from its infancy, meaning that there is considerable scope for how and when it will grow. Regardless of how that may develop in coming years, 2017 is all set to be an exciting time for the industry, and Data Scientists everywhere.
Raghav KherManaging Director, Microsoft Accelerator Seattle
This week, we’re proud to announce the latest batch of startups joining our Seattle Accelerator’s Fall cohort. With a combined funding of over $140 million, these 11 startups span AI, cloud, and IoT spaces.