7 Takeaways From Gartner’s Data & Analytics Summit 2022

Data and analytics are the lifeblood of modern companies and, like so many other things tech-based, they are evolving.

ORLANDO, Fla. — After spending three days at the Gartner Data & Analytics Summit in Orlando, Fla., it’s time to share the highlights of the conference. It was a packed house, with 5,400 attendees and this journalist’s first return to a live event post-COVID. It felt good to get out and connect with my peers.

The spotlight on day one was on innovation amidst uncertainty, digital ethics and the top trends in data and analytics.

Day 1: Transformation and Innovation

The opening keynote, presented by Gareth Herschel, VP analyst at Gartner, and Debra Logan, distinguished VP analyst at Gartner, discussed how brands can consider new perspectives in D&A and design better decisions in a world of perpetual change.

Interestingly, Gartner estimated increased usage of synthetic data in the coming years, claiming it should reduce liability. Synthetic data is data that isn’t based on real-world phenomena or events but rather is generated by a computer program, and reflects real-world data, mathematically or statistically. Gartner estimated that by 2025, businesses could avoid 70% of privacy violation sanctions through the use of this type of data.

Digital Ethics Is a Mainstream Topic

Later in the day, Frank Buytendijk, distinguished VP analyst at Gartner, emphasized that digital ethics are essential for all technology applications but are particularly important when evaluating complex or emerging technologies such as artificial intelligence (AI), blockchain and concepts like the metaverse, and that different technologies have different moral footprints.

For instance, databases and development tools may have a lower moral footprint than AI or the metaverse, both of which require new ways of thinking when it comes to potential ethical implications.

Gartner Calls Out Top Trends in Data and Analytics for 2022

Carlie Idoine, VP analyst at Gartner, and Ted Friedman, distinguished VP analyst at Gartner, shared their thoughts on the key focus for D&A leaders in 2022 — managing consequent and persistent uncertainty and volatility.

Key trends discussed included:

  • Adaptive AI Systems: Adaptive AI is key because it can change its own code to incorporate what it has learned.
  • Data-Centric AI: Formalizing data-centric AI as part of a brand’s data management strategy will ensure that AI-specific data considerations, such as data bias, labeling and drift, are properly dealt with.
  • Context-Enriched Analysis: Context-enriched analysis assists with the identification and creation of further context that is based on similarities, constraints, paths and communities.
  • Skills and Literacy Shortfalls: Brands must encourage broader data literacy and digital learning rather than simply providing core platforms, datasets and technology.
  • Connected Governance: Connected governance facilitates the connection of the wide assortment of governance efforts across different organizations, both physical and virtual, as well as geographical.
  • Expansion to the Edge: Data, analytics and associated technologies increasingly reside in edge computing environments, closer to the physical sources of data and outside the normal realm of IT.

Related Article: What Analytics Trends Should Marketers Expect in 2022?

Day 2: AI Leading to Business Value

On day two of the Gartner Data & Analytics Summit, the focus was on how brands can deliver business value using AI, best practices for trusted data sharing and the actions brands can take to improve data and analytics risk culture.

Building a Foundation of Data Science and Machine Learning to Enable AI

In this session, Peter Krensky, director analyst at Gartner, examined the major trends and data science talent personas and provided an overview of leading technologies in the data science and machine learning (DSML) space.

The biggest challenge, according to Krensky, in getting these AI initiatives off the ground is having the right skills. As more AI and machine learning get implemented, more skilled workers will be needed to create, maintain and analyze systems, processes and projects. “The skills and talent gap is going to create a situation of ‘haves’ and ‘have-nots,’” he warned.

Krensky claimed the dawn of AI is going to take close to a decade. For those who wanted to be early in data science and machine learning, that date has already passed, and within one to two years, if you’re not already there, you’re late. However, there are still plenty of beginner and intermediate opportunities to deploy data science and machine learning.

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