“Today, business analysts spend 80% of their time searching, cleaning, validating, and governing data, and only 20% of time on analyzing data.” Can your organization relate to this startling statistic from IDC Research Manager Chandana Gopal?
With the volume and complexity of data only set to rise (IDC predicts the global datasphere will grow from 33 zettabytes in 2018 to 175 zettabytes by 2025), Gopal says enterprise organizations need a defined strategy now to empower all users with the data and analytics access they need to make fast, informed decisions. What should that strategy include? A solid investment in embedded analytics, says Gopal.
“Most large enterprises have deployed business intelligence tools, data warehouses, data lakes, predictive analytics, and many other solutions,” notes the IDC research manager in 10 Enterprise Analytics Trends to Watch in 2019, “but have lost the essence of the reason for investing in these tools.
“Often these have been deployed in silos or have been made available to a select group of individuals or have been so complex that they were not accessible to the line of business worker. As a result, even after decades of investment, few enterprises can claim to be truly intelligent.”
But with digital disruption directly related to new or evolved insights-driven business models and customer experiences, this is no time for any or many employees to be operating blind.
By embedding intelligence into applications, business users will be able to focus on new ideas and innovate much faster than before. —Chandana Gopal, IDC
Gopal advises a tiered approach for those organizations that need to come from behind to empower everyone with data and insights. “By evaluating business use cases to determine which ones are most likely to achieve success with the infusion of data and analytics, enterprises must prioritize investment in those that can show a short-term return on investment to get business buy in—and at the same time plan on embedding analytics into more long-term and complicated end-to-end processes,” notes Gopal.
“Enterprises should treat the analytics process as an iterative lifecycle, where each step learns and adapts from the previous one. As the enterprise matures with its data strategy, it can progress from being able to leverage retroactive descriptive analytics (that looks at historical performance), towards becoming predictive and eventually prescriptive in nature.”
To get the IDC research manager’s advice on the next steps for organizations, including leveraging machine learning and artificial intelligence, download a complimentary copy the eBook 10 Enterprise Analytics Trends to Watch in 2019, featuring insights from IDC, Constellation Research, Forrester, Ventana Research, and more.