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Autonomous Analytics: Turning Data into Decisions
Data is touted as the “new oil”, but without a strategy for effectively using it, it’s value is limited.
James Hodge, chief technical advisor at Splunk, said that “the fact that businesses are producing vast amounts of data is indisputable, but … senior business leaders, including those at board level, are still struggling to access the data on their own terms, when they really need it to power live, forward looking decisions.”
Businesses are failing in their attempts to effectively use data. Gartner says that only 20 to 30 percent of business intelligence (BI) projects are successful. Gartner categorizes 87 percent of businesses as having low BI and analytics maturity.
A major hurdle for companies is in the data preparation. Some estimate that as much as 80 percent of time is spent in collection, and while this number has been questioned as being high, it remains true that data prep is time intensive.
Increasingly tasks like data collection, cleansing and formatting are being automated. But that’s not all. AI techniques, like Machine Learning, are also being applied to the data in order to improve decision making. These advances in business intelligence and analytics are being called autonomous analytics.
Jen Underwood, founder of Impact Analytix, said that “next generation autonomous analytics capabilities will be able to automatically prepare and cleanse data, perform feature engineering, find key insights and hidden patterns. Automation expedites investigation across millions of variable combinations that would be too time consuming for a human to do manually. Furthermore, artificial intelligence algorithms will interpret results and present unbiased alternatives along with actionable recommendations.”
