“Data and analytics leaders should actively monitor, experiment with, or deploy emerging technologies. Don’t just react to trends as they mature. Engage with other leaders about business priorities and where data and analytics can build competitive advantage.”
– Rita Sallam, Vice President Analyst, Gartner
It’s no secret that technologies like machine learning, AI, and predictive analytics have revolutionized how organizations are developing. But within these broad categories, several more specific data trends are particularly relevant. Organizations that pay attention to these now will thank themselves later when they’re able to keep pace with the next set of emerging trends in years to come. Here are the most disruptive data analytics trends of 2020 that will continue to mature over the next five years.
Augmented analytics (AA) allows for optimized decision-making beyond what many professionals are accustomed to. AA uses machine learning, AI algorithms, and a process called natural-language generation, which transforms structured data into our normal, natural language. Rather than pulling up the most relevant insights manually, organizations can automate this process and save loads of time. These insights can be made available to all important players, and the process saves them from having to be (or rely on) data scientists and analysts. AA has a lot of potential to be disruptive because it addresses the collective Achilles heel – that there are not enough data scientists and expertise to manage all the data that organizations are accumulating.
Data fabric is a platform that allows for the convergence and management of data from different sources. Through this framework, data can be transported, combined, designed, managed, and protected across channels. For organizations that don’t want to convert or migrate data, data fabric offers a solution that doesn’t waste as much time. Closely tied to managing augmented data, data fabric allows organizations to support data at scale from diverse silos like cloud, SQL, and more. In the past, organizations aimed to have all their data stored in one warehouse. Today, we’re moving beyond this to a more comprehensive goal.
Explainable AI will be essential for organizations to successfully manage the rise of AI in business. With such complicated models being employed more every day, it’s critical for organizations to learn how to understand and explain their results for internal monitoring. Detecting for bias and privacy issues, and ensuring regulations are observed, is of growing importance and difficulty.
Explainable AI is critical because it addresses the growing “black box” problem in which data experts do not know exactly how and why an AI tool came up with the answer it did. Many AI algorithms can’t be examined after they generate insights, leaving organizations to wonder about the accuracy of the information. With explainable AI, the models and steps involved can be more carefully scrutinized. This way, organizations can repeat these processes and have them be transparent rather than hidden.
Continuous Intelligence is as useful as it sounds, providing the ongoing capacity to make real-time business decisions based on analytics. Organizations with situation-specific data can make informed decisions or receive predictions on what to do next. Central to this process is a focus on outcomes and automation. This may sound like what many organizations are already doing, but it goes a step beyond that. You can analyze data as it is created, visualize aspects of your organization, make predictions faster, learn from unstructured data, and automate actions immediately. In today’s world of developing technology and ongoing change, continuous intelligence is a no brainer for the future of business.
Perhaps the least surprising trend, the mobile intelligence (MI) framework will be built upon to achieve better results. Mobile app development will be geared toward a better work experience, stronger internal communications, and stronger B2C communications. With consumers using mobile devices for more and more tasks each year, it’s clear that mobile is being embraced on all levels. This means organizations need to have MI at the front and center of their strategy. Much like continuous intelligence, MI allows for real-time insights that help organizations act more quickly.
In 2020, we’ll see an even stronger focus on bridging the gap between diverse data sets as organizations grapple with what tools to use and how to combine data in the most efficient way. When data sets become too large, sometimes the simplest solution is to break it up into smaller, more meaningful sets. The key to truly diverse data is to look beyond what’s obvious, not just focusing on the data that would be easiest to collect.
“Non-representative data sets are less likely to yield workable insights than those which cover all facets of the issue under investigation. It’s also true in terms of the variety of data available. With the sheer divergence of datasets available, it’s more critical than ever, as insight can often be found in unexpected places. Thanks to breakthroughs in technology such as image analysis and natural language processing, meaning can be extracted, in an automated way, from video, handwriting, recorded speech and the text of emails and social media posts.” – source
Organizations that dig in and engage with these trends now will be much better prepared for data advancements in the years to come. Meanwhile, those that don’t may have a lot of extra headaches later on.
Being data-driven is no longer a luxury reserved for the biggest and wealthiest organizations. It’s an accessible reality for all. Moving forward, focus will shift toward refining data management processes and accessing deeper layers of expertise for sharper results – better customer service, higher marketing ROI, and more confident decision-making. That’s how data-driven organizations will move onward and upward in 2020.