The emergence of relationship analytics highlights the growing use of a graph, location, and social analytical techniques.
"As intelligence is at the core of all digital businesses, IT and business leaders continue to make analytics and BI their top innovation investment priority," said Jim Hare, research vice president at Gartner. "This Hype Cycle helps data and analytics leaders make the transition to augmented analytics, to build a digital culture and operationalizing and scaling analytics initiatives."
The five key trends are:
1. Augmented Analytics
As it matures, augmented analytics will become a key feature of modern analytics platforms. It will deliver analysis to everyone in an organization in less time, with less of a requirement for skilled users, and with less interpretative bias than current manual approaches. As technology develops, there will be more citizen data scientists. Gartner predicts that, by 2020, citizen data scientists will surpass data scientists in the amount of advanced analysis they produce, largely due to the automation of data science tasks.
2. Digital Culture
Developing an effective digital culture may be the first and most important step an organization takes in its digital transformation journey. "Data literacy, digital ethics, privacy, enterprise, and vendor data-for-good initiatives encompass digital culture," said Hare.
3. Relationship Analytics
The emergence of relationship analytics highlights the growing use of a graph, location and social analytical techniques to understand how different entities of interest - people, places and things - are connected. Analyzing unstructured, constantly changing data can provide users with information and context about associations in a network and deeper insights that improve the accuracy of predictions and decision-making.
4. Decision Intelligence
D&A leaders draw on a wealth of data from ecosystems that are in constant motion. This requires them to use a multitude of techniques to manage data effectively. The unpredictability of the outcomes of today's decision models often stems from an inability properly to capture and account for the uncertainty factors linked to these models' "behavior" in a business context. Decision intelligence provides a framework that brings together traditional and advanced techniques to design, model, align, execute, monitor and tune decision models.
5. Operationalizing and Scaling
The number of use cases at the core of a business, on its edges and beyond, is exploding. More people want to engage with data, and more interactions and processes need analytics in order to automate and scale. Analytics services and algorithms are increasingly activated whenever and wherever they are needed. Whether to justify the next big strategic move or to optimize millions of transactions and interactions gradually, analytics tools and the data that powers them are showing up in places where they rarely existed before. This is adding a whole new dimension to the concept of "analytics everywhere."