There are many different levels to analytics, starting with descriptive analytics (reports, dashboards) and diagnostic analytics (drill-downs, ad-hoc queries), which focus on understanding the past. The next three levels turn their attention to the future and are increasingly augmented by AI technology. While the potential of predictive, prescriptive or cognitive/autonomous analytics is exciting, there’s some essential groundwork that’s needed at the lower levels. If your company hasn’t mastered the basics of analytics yet, it’s fallacious to believe you skip ahead with AI and without a solid analytics foundation.
Without the transparency that analytics provides, it will be difficult to judge the results of any artificial intelligence system. We’re already beginning to see examples of poor decisions being made by algorithms and data models with little insight into their rationale. Analytics holds AI accountable and can help optimize the effectiveness of AI systems over time. In addition, many of the problems that can derail your success with analytics will also sink your AI efforts. Therefore, it’s important that your organization learns how to crawl and walk with analytics before running with artificial intelligence. Here are some areas where analytics helps to clear a path for AI success:
- AI is powered by the same fuel as analytics—data.If you’re not collecting the right data or lack faith in the quality of your data, it won’t be magically corrected with AI. Just as your analytics systems rely on accurate, complete data so will any AI technology. Increasingly, artificial intelligence may be used to improve data quality, but it won’t compensate for data that is fundamentally corrupt or unreliable.
- AI is also dependent on many of the same business processes that impact analytics.For example, data privacy is a sensitive subject with consumers, especially as companies collect more and more data on their customers. Without sound data privacy best practices that are a byproduct of a mature analytics program, AI technology could inadvertently misuse customer data in various ways that could damage goodwill and brand perceptions. Analytics essentially highlights the upstream and downstream processes that could also be impacted by AI.
- Artificial intelligence will also depend on having skilled people with both domain and data expertise.Their insights into the business, key processes and data will be essential to implementing the AI technology effectively as well as maintaining and improving the AI systems over time.
- AI relies on having a data-driven culture in place so that it can be fully embraced.If your managers and employees frequently question or ignore insights from your analytics tools when making decisions, AI won’t alter this resistance. However, if they become accustomed to relying on and trusting your analytics data, they will welcome opportunities to lean on artificial intelligence to further augment their analytical capabilities.
- AI will face many of the same organizational roadblocks that can impede success with analytics.If certain managers and teams already feel threatened by the greater transparency created by analytics, good luck trying to introduce anything like AI that further threatens their perceived power and influence. Breaking down internal political barriers with analytics can clear an easier path for future AI adoption.
Before you pursue AI technology in 2017, evaluate how far your analytics capabilities have progressed to date. Just like crawling is a key developmental milestone for infants, analytics is equally important to aspiring data-driven organizations that look to embrace artificial intelligence. A lack of analytics or data maturity will delay your organization’s ability to benefit from emerging AI technologies. With artificial intelligence potentially ushering in a new industrial revolution, it’s imperative that each organization learns to crawl and walk with analytics in earnest before seeking to run with AI. The data-driven companies that do so will be able to quickly outpace their rivals that failed to adequately prepare for the coming AI revolution.
**This article was originally posted on Forbes.com on January 11, 2017.