These data and analytics technology trends will have significant disruptive potential over the next three to five years. Data and analytics leaders must examine their business impacts and adjust their operating, business and strategy models accordingly.

Key Findings:

The expanded and strategic role of data and analytics in digital transformation is increasing the complexity of data, the number of variables to analyze, and the types of analyses required for success. This is pushing the limits of current capabilities and approaches.

Virtually every aspect of data management, analytics content, application development and sharing of insights is using machine learning (ML) and artificial intelligence (AI) techniques to automate or augment manual tasks, analytic processes and human insight to action.

Intelligent capabilities that enable emergent and agile data fabrics and explainable, transparent insights and AI at scale are necessary to meet the new demands and expand adoption.

Recommendations

Educate, ideate and engage with business leaders about your strategic priorities and where data and analytics can automate or augment human activities.

Put in place formal mechanisms to identify technology trends and prioritize those that can be incorporated into your strategy and roadmap with the biggest potential impact on the business.

Take action over the next three to five years to proactively monitor, experiment with or exploit key trends. Don’t just react to trends as they mature.

Identify the gaps in your data, analytics and organizational capabilities that are preventing you from exploiting the disruptive trends.

Use success metrics and incentives that emphasize learning and reward innovation.

Invest in nontechnology trends — such as data literacy, AI governance, data engineering, data storytelling and privacy and ethics — as these are critical key success enablers.