![]() ![]() Ongoing advances in neural networks also continue to aid the ML push, making BI and forecasting more intelligent and concrete. ![]() As some businesses are now discovering, ML lets them produce highly accurate estimates of future behavior-i.e., answers-based on large volumes of historic data. ![]() Swetha Ganeg Basavaraj, Datavisorīeyond demand models that only predict market trends, revenue levels and so on, ML can generate actual answers. For example, anomaly detection in data, outlier identification and triaging, metadata checks and cataloging data better for use by business and analytics users-all these help BI improve data governance standards. While it is common to use AI to predict and automate business decisions, AI can be used by BI teams to improve the way they do data quality checks-both extraction and transformation. Machine learning can automate the process so that BI professionals can focus on higher-level trend analysis and behavior patterns that can bring greater value more quickly to the organization. Guy Yalif, IntellimizeīI processes involve analyzing large sets of data-if done manually, this is time-consuming. ML can find patterns and help BI and marketing leaders activate experiences at a level of granularity that was not possible before. BI professionals and their marketing counterparts can then activate that insight for each unique prospect to tailor their journey through the marketing funnel and deliver more revenue. Machine learning helps BI professionals learn more about each prospect. ![]()
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