The Impact of Artificial Intelligence on Data Analytics

Posted on Updated on

The Impact of Artificial Intelligence on Data Analytics

By: Brad Starks

We are in the midst of an artificial intelligence boom which is looking to transform many areas of life, business, and technology. According to the Second Annual AI Index Report released in late 2018, which seeks to track AI “volumetric and technical progress on an ongoing basis”, we are in the midst of a sharp uptick in a variety of areas related to AI. Papers being published, startups, VC funding, academics, and overall adoption of AI are all on a sharp rise compared to the 2017 AI index Report (Shoham et al., 2018). Given how large the impact artificial intelligence is poised to have it stands to reason that we will also see AI playing a large part in data analytics from now into the future.

According to SAS, an industry leader in all things data analytics, artificial inelligence can be seen as adding intelligence and value to existing analytics products, particularly analytics (SAS, 2018). Furthermore the growth of AI allows for big data to be better and more efficiently leveraged. We are seeing data gathering taking place at such a rate that it’s no longer possible for humans to process it quickly enough for the data to be of use, as people simply cannot work fast enough on their own. Jeff Rajeck, who is a researcher, trainer, and consultant for Econsultancy, notes that organizations are turning to AI in order to bolster their analytics platforms in such a way that the “systems use machine learning and other AI techniques to help analysts find patterns in customer data, elicit recommendations for optimizing performance, and allow non-professionals to access complicated analytics using simple language” (Rajeck, 2018). Through the integration of AI with analytics people can move away from specifically analyzing data directly, to examining the output from AI and machine learning assisted analytics in order to quickly tell a big picture story based on massive amounts of raw data.

Starting in the third quarter of 2018 Nokia looked to harness the power of artificial intelligence and apply it to their own analytics platform when they released their own AI powered analytics software. Through the integration of AI, the Nokia analytics software will leverage advanced machine learning and deep learning algorithms to provide new levels of prediction and automation capabilities with the ultimate goal of drastically improving the user’s experience with the software through improved performance and output (Nokia, 2018). As a direct result of the integration of AI into the software Nokia expects that users will be able to make predictions six times faster given that the software will be delivering analytics and its own insights much more quickly compared to a more traditional analytics platform that relies on people to do much of the work and analysis of the data.

Given that we are seeing artificial intelligence take over work within analytics that people used to be responsible for, what does that mean for the IS professionals who will be working with the emerging AI integrated analytics platforms? According to ACM inroads, AI is taking over for human interpretation of analytics. This means that “IS professionals need to be ready to contribute to the decisions regarding the boundaries between human and computer-based decision making” as analysts’ job responsibilities shift in the wake of the AI revolution (Topi, 2018). It’s less likely that we will see a reduction in the number of analysts and IS professionals, rather these people will shift from slowly analyzing massive amounts of data to more quickly reacting and interpreting readily available analysis and insights instantly provided by an analytics platform supercharged with AI.

References

Nokia. (2018, March 27). Nokia’s new AI-powered analytics software dramatically improves customer experience and satisfaction. Retrieved December 7, 2018, from https://globenewswire.com/news-release/2018/03/27/1453329/0/en/Nokia-s-new-AI-powered-analytics-software-dramatically-improves-customer-experience-and-satisfaction.html

Rajeck, J. (2018, September 03). The three trends driving marketing analytics in 2018. Retrieved December 07, 2018, from https://econsultancy.com/the-three-trends-driving-marketing-analytics-in-2018/

SAS. (2018). Artificial Intelligence – What it is and why it matters. Retrieved December 7, 2018, from https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html

Topi, H. (2018, September). IS EDUCATION: New IS competency: Integrating analytics and AI capabilities into information systems. ACM Inroads, 9(3), 36-37. Retrieved December 7, 2018, from https://dl.acm.org/citation.cfm?id=3239258

Yoav Shoham, Raymond Perrault, Erik Brynjolfsson, Jack Clark, James Manyika, Juan Carlos Niebles, Terah Lyons, John Etchemendy, Barbara Grosz and Zoe Bauer. (2018, December) “The AI Index 2018 Annual Report”, AI Index Steering Committee, Human-Centered AI Initiative, Stanford University, Stanford, CA. Retrieved December 7, 2018, from http://cdn.aiindex.org/2018/AI%20Index%202018%20Annual%20Report.pdf