2017 Data Year in Review, and Data Realities You Can’t Ignore in 2018

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The past year was a busy one for enterprises around the world working to meet new regulatory requirements and stay relevant in a quickly changing business environment. Here are the biggest trends in data in 2017 and how they impacted not only those companies, but their customers and the world.

Offline Met Online

In August, Amazon bought Whole Foods for a cool $13.7 billion – and trust us, it was all about the data. This marked a turning point in the world of retail, and for other types of businesses too, signifying the beginning of an era where consumer data can be used to optimize everywhere, not just online.

Opportunities Were Ripe for the Taking for Industries Willing to Invest in Data Strategies. Some Still Didn’t

2017 seemed to be the year that airlines in particular, with rising prices and publicly embarrassing service events, finally took a hit. Some, like RyanAir, proved that they have done shockingly little to keep up with the data revolution, with no clear strategy for change. Others, after several industry-wide, disastrous public relations incidents following over-bookings, have vowed to change (like United Airlines). Some think that AI is poised to disrupt the airline industry soon, but so far, no major airline has stepped up to the plate.

Data Took Root in Finance

If there’s one industry that started to take steps toward major change with data in 2017, it was finance and banking. This is especially interesting as banks are notorious for holding on to some old school processes and not being quick to change with the times. And with all of the regulation involved when dealing with people’s personal data and their money, it’s hard to blame them. But we did see several major financial institutions start to get serious about a modern, cutting-edge data strategy.

Simply Collecting Data No Longer Cut It

Again and again at conferences across the world in 2017 the main message was the same: now that you’re collecting data, here are the steps to actually use that data. The year 2017 was one where enterprises started facing the reality of creating value from data or being left behind by agile, innovative competitors. Data storage companies like Cloudera and Hortonworks have worked to rebrand themselves as AI-first, pivoting from the storage/compute first approach they have followed since inception.

Data Governance Took Center Stage

Unfortunately, data breaches are becoming more common than most of us are comfortable with. But the mid-2017 Equifax disaster really hit consumers hard and brought data governance to the forefront. And then there was also Uber. And Imgur. And the list – sadly – goes on. GDPR began to take center-stage as the first wave of data science privacy regulations (link to articles or video?)

Machine Learning, and Even Deep Learning/AI, Went Mainstream

Basic analytics are out; machine learning (and beyond) are in. Practical applications of machine learning, deep learning, and AI are everywhere and out in the open these days (the new ads in Piccadilly Circus in London are just one example). But also, employees whose roles traditionally involved small data or limited advanced analytics capabilities are preparing themselves for the shift to come with machine learning.

People Started Talking About Predictive Analytics and Real Time

Not too long ago, using data meant simply conducting analysis on past data sets to create static insights. Not anymore. Now, it’s all about real time (and thinking about the technologies that might be needed in the future to fully support truly real time initiatives). The past year, enterprises around the world from all industries were focused on using up-to-the-minute data to inform decisions rather than data from months or even years in the past.

What’s ahead?

As 2017 comes to a close, it’s time to look forward at what’s ahead in world of data. Of course, there are lots of hot new trends to watch, but more importantly, there are essential facts about the developing data landscape that businesses simply need to realize to excel (or even to move forward) in 2018. Here are the four data realities you need to realize in 2018 to keep your business at the cutting edge in a competitive market:

  • Machine Learning Is No Longer Optional
  • Production Matters (A Lot), but It Can Also Be Hard
  • Building Analytics Products that grow the top line
  • Business People and Data Scientists Need Each Other
  • Data Governance Can’t Be Ignored

In 2018, we will certainly see new technologies throughout the different parts of the big data ecosystem. There will also continue to be agile startups that infiltrate industries with agile, data-at-the-core approaches, forcing incumbents to step up to the plate.

The combination of these two factors, along with the fact that real-life application innovation will accelerate in 2018, means that many industries will fundamentally change as big players start actually harnessing their massive amounts of data for business gain in the new year. Those that stay ahead of the trends as leaders and trailblazers rather than followers reacting to the shift will come out on top.

About the Author

Dr. Ken Sanford is the US lead Analytics Architect for Dataiku. He is a reformed academic economist who likes to empower customers to solve problems with data. In addition, Dr. Sanford teaches courses in Applied Forecasting, Stress testing and Big Data Tools for Economists at Boston College. He has a Ph.D. in Economics from the University of Kentucky in Lexington and his work on price optimization has been published in peer-reviewed journals.


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