For years the

Strata Data Conference

has been a mecca for technology and business leaders, the place for sharing ideas and experiences and for measuring progress of the digital economy through a “big data” lens.

After a three-year break, I attended the recent New York edition of the event. Strata used to be held at a midtown NYC hotel but it now warrants the massive Javits Center to meet the demand for its learnings and insights. Here are some of the themes and takeaways from this year’s conference.

Beyond big data

Strata is no longer focused on mastering the technical underpinnings of big data’s defining dimensions (volume, variety and velocity). Instead, the conference is an exposition of real-world use cases and a forum for spreading the knowledge of innovators. Data scientists and business leaders are discussing how they use machine learning and predictive analytics to tackle some very big problems, from connected health systems and smart cities to disease prevention for the world’s most vulnerable. With its focus on data, digital disruptions and digital transformations Strata gives us a pretty good look into the future.

Real-time data analytics

More and more applications come embedded with real-time analytic capabilities. For instance, real-time analysis of customer interactions is used to deliver a more personalized and recommended experience. Or, consider the content consumption analysis that provides feedback in real-time to BuzzFeed’s writers and editors. Data streaming technologies empowering real-time analytics are advancing rapidly and are already firmly in place in manufacturing and energy companies. These capabilities are also finding their way into retail, media and education sectors.

The age of machine learning

Machine learning certainly stood out more than any other technology shaping our future. It delivers predictions and insights, it’s the algorithm behind self-driving cars and it’s used to recognize faces. The great thing about machine learning is it just gets better and better with more data. Every big startup, innovation or new business model in the next five years is projected to incorporate machine learning.

The misuse of machine learning and AI algorithms, particularly on Facebook, Twitter and YouTube, has been a big topic in Congress and the popular media recently, but it wasn’t a focus at the Stratus conference. It would be great if some data industry conference in the near future embraced head-on the intentional and unintentional downsides of AI and machine-learning when it comes to targeting individuals or groups with messages.

Connecting the physical world to the internet

The Internet of things (IoT) is bridging the physical and digital worlds and, in the coming years, will become a primary driver in digital transformation. By 2020, a projected 50 billion devices will be connected to the internet. The data tsunami has only just begun. Early adopters of IoT data analytics in transportation and energy have wiped out hundreds of millions of dollars in costs, while in retail and event settings sensors are being used to track the movements of customers to help understand what it is that draws their attention.

The cost savings in transportation come from more efficient processes, mainly through more efficient movement of goods through internal and external supply chains, less waste, spoilage and theft. Oil and gas producers use sensors to determine flow rates, to detect fluctuations, outages, etc. Think about autonomous vehicles: Sensors continuously monitor hundreds/thousands of data points in real time to operate the vehicle.

Data visualization and context of use

Data visualization is one area of data analytics that was not given enough runway at Strata. Leading designers and visualization experts contend we are stuck in the status quo of uploading data, picking charts and coming up with something that is usually pretty flat and unexciting. They advocate adding tools, animations and graphical devices that reflect a clear understanding of the purpose for a marketing journey, or use case, and target audience. Ideally, visualizations should answer the primary use-case question and help in the follow-up of what the next question will be.

Business models, platforms and the future

Looking back at all of the conference keynotes, presentations and vendor demos one is struck by the remarkable optimism of participants on all sides. No one is talking or thinking about how automation and technology is going to displace people but rather how we can all use AI and technology to cognitively augment workers, as Lyft and Uber have done with Google Maps and the other apps in their ecosystems.

Looking to the future we can expect disruption cycles to come much faster and value propositions to become more complex and rapidly mutable. The great technology platforms of today (Amazon, Uber, eBay, etc.) can help us understand the future of business and the economy. These platforms are great because they work for all participants and not just for users or platform owners. They are enmeshed in the real world and every AI-infused algorithmic system has an objective function.

At the end of the day Strata is a window to the future—in use case after use case we hear from innovators and business executives spearheading projects designed to make the future a better place for everyone.