8 Ways Artificial Intelligence Can Empower Business Intelligence | MicroStrategy
BI Trends

8 Ways Artificial Intelligence Can Empower Business Intelligence

According to IDC’s 2019 Data Integration and Integrity End User survey, knowledge workers and business analysts spend less time on analytics than they did in 2017.

In 2017, respondents spent 19% of their time each week on analytics, compared to 37% on data preparation, 20% searching, and 24% on governance. In 2019, respondents note that they now spend just 12% of their time on analytics each week, compared to 42% on data prep, 25% searching, and 21% on governance. 

IDC Research Director Chandana Gopal says that the time spent on production versus the time spent on creation of value is a common frustration point for many BI and analytics professionals. But that’s where artificial intelligence (AI) can help business intelligence (BI) in empowering humans to focus on the more meaningful parts of their work.

In the on-demand webcast Analytics in the Era of AI, which features Gopal and IDC Group Vice President Dan Vesset, Gopal notes that the talk around AI taking away large numbers of data-skilled jobs or doing away with the need for data scientists is just that—talk. Rather, AI will empower users to spend more of their time creating business value by:

  1. Providing more situational awareness

  2. Evaluating alternatives

  3. Identifying drivers

  4. Predicting potential outcomes

  5. Understanding risks and constraints

  6. Delivering recommendations and prescriptions

  7. Capturing institutional knowledge

  8. Aiding in-process collaboration via crowdsourcing or expert identification

Gopal says the biggest hindrance to taking advantage of AI for most organizations today is data silos—and that applies to both technology and people. Over the last decade, brands have made technology investments in BI, AI, big data, machine learning, data warehouse systems, data lakes, predictive analytics…the list goes on. But the problem is that most have been investing in these technologies in silos to solve a particular business problem—which is not conducive to better overall decision making or automation. Three related issues include: 

  1. Brands have not been accounting for the diversity of decisions and decision makers within their organization. They’ve been focused on providing executives with reports and dashboards while operational employees and customer-facing employees can’t access the insights they need because they don’t know how to get them. (Watch the webcast for IDC’s recommendations on rethinking investments to empower more employees.)

  2. Organizations have not been viewing decision making as a process. Instead, disparate groups of people have been working on different steps of decision making, including tracking, analyzing, making decisions, and taking action. In the webcast, IDC presents how AI, viewed not as a future goal but as a part of current tasks, can help in both automation and connection. 

  3. Brands have not been preparing for the future explosion of data. Currently, IDC notes that 49.8 zettabytes of data are being created worldwide, but by 2023 that number will more than double to 102.6 zettabytes. This is why the effective incorporation of AI will become vital in handling data volume and complexity. 

Top Takeaways

Gopal advises organizations to focus on these key starting points as they move forward with analytics in the era of AI:

  • Don't think of AI as a destination, but rather a part of each process

  • Recognize the range of decisions and decision makers, and identify patterns that can be automated

  • Remove FUD about AI by training and educating users

  • Expect complexity to increase exponentially—prepare by empowering users with data

  • Use technology to address skills gap challenges

Want to hear IDC’s recommendations for using AI with analytics, see their AI framework, discover the vital role that analytics and insights play in IDC’s digital transformation platform, and hear the latest forecasts around AI spending? Watch Analytics in the Era of AI on demand.


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