Despite all the talk of AI and ML, there are very real hurdles to the adoption of advanced analytics technologies, especially at organizations with sizable data investments and tricky business challenges.

Simply put, many organizations lack the data governance controls for many AI and ML projects to make it past the experimental stage, or the tools to give everyday users the ability to collaborate with more advanced users.
That should not stop data leaders from the pursuit. However, the key is to attack the challenge from two angles. First, prioritize data governance and consider analytics platforms that can integrate data from across the enterprise (which many claim to do, but few can actually do). Second, consider gradually integrating advanced analytics into traditional analytics workflow.

Smart Analytics—technologies designed to augment traditional analytics tasks with the help of AI—can give data leaders and practitioners alike powerful tools to answer business questions and gain unexpected insights.

Yet data stewards must simultaneously manage the underlying risks to data access. Smart Analytics initiatives must be anchored to holistic data and analytics applications that are designed to protect the business. Otherwise, new-fangled ""smart"" tools only exacerbate the problems and create ‘ability’ siloes.

As in all things, your analytics tools should be able to unequivocally answer the question being asked by business users hungry for data: ""What's in it for me?"" Give business users the tools and the data they need and a culture of data-driven problem-solving emerges. Smart analytics can breed confidence in the user base, allowing ingenuity to flourish, safely."