At its annual Data & Analytics Summit in February, Gartner released its list of top data trends for the year. Not surprisingly, a number of the themes were underpinned by digital transformation, which the analysts noted presents both a challenge and an unprecedented opportunity for companies today.

What’s the difference between these two scenarios (challenge versus opportunity), and how can organizations ensure they land in the latter? The answer, in short, is data. According to Rita Sallam, research vice president at Gartner, “The story of data and analytics keeps evolving, from supporting internal decision making to continuous intelligence, information products, and appointing chief data officers. It’s critical to gain a deeper understanding of the technology trends fueling that evolving story and prioritize them based on business value.”

With that in mind, the following are the top data and analytics trends for the year as Gartner sees them:

Augmented Analytics: By next year, Gartner expects that augmented analytics will be a key driver of business intelligence (BI) and analytics purchasing decisions. Drawing on machine learning (ML) and artificial intelligence (AI) techniques to transform how analytics content is developed, consumed, and shared, the firm sees the trend as the next wave of industry disruption and encourages analytics leaders to take note.

Augmented Data Management: The second trend on Gartner’s list also uses ML and AI capabilities, focusing on augmenting data quality, master data management, and other information management categories to be more self-configuring. By automating many of the manual tasks associated with these functions, this approach gives less technical users more data autonomy.

Continuous Intelligence: Continuous intelligence uses real-time data to improve decision making, and Gartner predicts that more than half of major new business systems will incorporate this trend by 2022.

Explainable AI: As AI becomes more prevalent in a variety of enterprise applications and functions, it follows that people will begin to question how AI systems arrive at their decisions. Enter explainable AI, a tool that, as the Wall Street Journal’s Angus Loten defines it, “auto-generates explanation for how an advanced AI model reached a specific recommendation.”

Graph Analytics: Graph analytics, which facilitates the exploration of relationships between key data sets, will grow in the coming years as the need to ask complex questions across complex data increases.

More on these and Gartner’s other key 2019 data and analytics trends can be found here. As distinguished analyst Donald Feinberg said, “The survival of any business will depend upon agile, data-centric architecture that responds to the constant rate of change.” Analytics leaders would do well to heed this warning, and begin researching and investing around these new industry trends today.