We live in the age of analytics. Business decisions are increasingly based on data of such complexity that a human mind struggles to comprehend. Even business units such as HR that have long made subjective people-related decisions can now rely on the assistance of algorithms and data.
“Data and analytics leaders must look into a more automated approach for how analysis is done and the more ways in which it can be used,” explains Rita Sallam, research vice president at Gartner.
By 2023, AI and deep-learning techniques will be the most common approaches for new applications of data science
“Analytics technology has evolved to a point where it adapts to the needs of employees and customers. Users are no longer forced to adopt traditional approaches,” adds Alexander Linden, research vice president at Gartner.
To solve complex business problems, organizations must adopt more-sophisticated analysis. Gartner has identified two key trends that will shape analytics in the years to come.
Trend No. 1: The rise of AI and deep learning
Gartner expects that by 2023, artificial intelligence (AI) and deep-learning techniques will be the most common approaches for new applications of data science. The challenge: People are suspicious of analysis they don’t understand.
“Historically, analytic techniques were always comprehensible, at least in conceptual terms,” explains Linden. “Even if users did not understand how the specific model was built, they understood the logic behind a multivariate regression or decision tree.”
Deep learning is different from traditional techniques. Although it often delivers more-accurate results, it lacks transparency. Users are unable to comprehend how the solution was achieved, so tend to distrust the technology.
“Found in products such as virtual assistants and self-driving cars, deep learning is mostly used when traditional machine learning techniques are not suitable,” says Linden. “People will eventually get used to it, as AI becomes a part of everyday life.”
A more concerning restriction on the corporate adoption of deep-learning techniques is the threat of civil liability and lawsuits over difficult-to-explain algorithms that make a mistake. Data and analytics leaders should involve the legal department and other decision makers before deploying a new technique.
Trend No. 2: Intelligent and augmented insights
Relevancy is a key criterion for analytics. Users should not have to look for information — the system needs to recognize that a piece of information is relevant to the user and deliver the insights preemptively.
“Many analytical platforms already integrate augmented analytical techniques,” explains Sallam. “They detect trends and correlations in the data, and suggest ways to interpret the results in natural language and the best format to display them. Gartner believes that by 2021, 75% of prebuilt reports will be replaced or augmented with automated insights.”
As users will increasingly expect intuitive interface mechanisms, organizations should invest in analytics technology that can deliver on that expectation. AI techniques can be deployed to focus the user on issues that require their attention. Integration in conversational interfaces expands access and improves the consistency of interpretation.
“In a few years, a decision maker with access to augmented analytics capabilities might just ask their personal analytics assistant to analyze the latest sales results and provide recommendations,” adds Sallam. “The system learns the specific relevancy triggers and is eventually able to initiate interactions, such as providing actionable sales insights on a regular basis.”