Key Highlights from the Gartner Data and Analytics Summit 2022

Executive Summary

• Gartner believes we will not return to a “new normal” but rather a “no normal”. Ambiguity and uncertainty are here to stay. This requires innovation and investment in data and analytics.
• Thinking of unleashing innovation in your organizations? Gartner advise you focus on 4 key areas - Data, Analytics, Decisions, and Governance.
• “Causal Inference” is coming into a new renaissance. Causal inference is a new toolbox that goes beyond correlation for data scientists to solve problems in fundamentally different ways. 
• Gartner highlights “conversational analytics” as an emerging application for natural language technology use cases.
• Futurist Lisa Bodell challenged the audience to imagine what work would feel like if you could stop doing mundane work and unnecessary time-sucks, and spend your time on valuable things.

Observations from the Gartner Data and Analytics Summit 2022

Last week WhizAI team members attended the Gartner Data and Analytics Summit 2022  in Orlando to meet with clients and learn the latest from the experts at Gartner on the trends impacting data and analytics. It was a great event held in person for the first time since 2019 with 5,500 data and analytics professionals in attendance. In this short article we wanted to share some of the major highlights.

From the very first keynote on Day 1, Gartner vice presidents and analysts Gareth Herschel and Debra Logan reminded us of their view that enterprises are heading not toward a new normal, but rather a “no normal” and that uncertainty and ambiguity are here to stay and this requires that organizations demonstrate flexibility, continuously innovate and invest in data and analytics. Data and analytics is vital to business strategy which, of course, is reflected in the growing popularity of this event with record attendance this year and by the fact that, per Gartner, this summit is their second largest event behind the Gartner IT/XPO.

The first keynote was titled: Unleash Innovation, Transform Uncertainty and focused on how to drive innovation with data and analytics. Innovation starts with “us” and as data and analytics leaders we need to drive innovation within our teams by driving innovation in what you do and how you do it and across the organization by being agents of change to drive business results. Speakers, Gartner analysts Gareth Herschel and Debra Logan, then presented a framework around 4 key ideas on how to think about innovation.

1. Data - Do I have the right data? Here they explored the need to move from big data to small data. We only want data that makes us smarter. We also need to innovate in sourcing data. Think about data you can create, specifically synthetic data. Gartner predicts by 2030 that synthetic data will completely overshadow real data in AI models.
2. Analytics - Am I finding the right insights? Analytics is both an art and a science. Identify the right questions to ask. Great analysis will prompt us to ask better questions. But analytics is an art and “all art is knowing when to stop” (quote: Toni Morrison).
3. Decisions - Will analysis drive outcomes? The focus here was on how can we influence the way decisions are made? We need to make it easier to make decisions by delivering insights at the right time. We can help accelerate decision making with augmentation and automation and Gartner noted the growing adoption of embedded analytics.
4. Governance - Does governance help or hinder innovation? The Gartner analysts had fun with this topic noting “governance” is the dreaded word. But, they were clear that there is no freedom without governance. Governance enables innovation if done properly.

This past July, WhizAI was recognized several times by Gartner as a vendor for Natural Language Query in various hype cycles related to data and analytics. In a session on Developing an Enterprise Strategy for Natural Language Use Cases, Gartner vice president and analyst Bern Elliot recommended to the audience that organizations define strategic enterprise NLT use cases and data requirements, leverage NLT solutions that are already happening and develop an informal community of interest. 

The book end to Day 1 was the closing keynote by IBM’s David Cox - IBM Director, MIT-IBM Watson AI Lab, IBM Research - in a session titled: What’s Next in Data and Analytics. This was a fascinating presentation focused on the difference between correlation and causation. He noted that the problem is the vast majority of our toolbox in data science today is based on correlation. So, he introduced that there is a discipline in machine learning called “Causal Inference”. 

Causal inference is not new but David believes it’s coming into a new renaissance. Casual inference provides a framework to infer causal structure, design experiments to test causal structure and make better decisions when some causal structure is known. His major takeaway is that we have a new toolbox for data science that we can use to solve problems in fundamentally different ways. If you want to learn more, I encourage you to go to https://cif360-dev.mybluemix.net/ and here you’ll find an open source toolkit intended as a guide to help data scientists quantify causal relationships in data.

On the last day of the summit the final keynote tied the message back to the opening keynote with a session by Lisa Bodell, CEO of FutureThink, titled Unleash Innovation. Lisa is a futurist, helping companies think about their strategy, how to plan for change and innovation. According to Lisa, companies mostly react to change, but the best companies plan for change. You should be asking where will my department be 10 years from now? Where will my company be 10 years from now? The best companies are forming partnerships today that help make the future happen.

Early on she questioned the audience – What holds each of us back when it comes to change? She said it’s three things

1. More vs valuable – we are conditioned to spend time doing mundane and unnecessary work
2. Internal vs external – we often focus on internal stuff at work versus focusing on what is happening in the market and the environment around us
3. Doing vs thinking – we don’t spend enough time thinking because we believe thinking is not rewarded at work. She mandates that her employees spend at least one four hour block thinking each week.

So, she challenged the audience to do better and said there are 4 things we can all do.

1. Ask killer questions – in the future asking the right questions will become more valuable than finding answers
2. Challenge obstacles
3. Kill a stupid rule – if you could kill or change any two rules at work, what would they be?
4. Reflect – what do you want to do? What’s stopping you?

If you’re intrigued by Lisa’s thoughts, you can find many videos of her on YouTube sharing these and other similar ideas.

We hope this short write-up has given you at least a small sense of the Gartner D&A Summit. There were so many sessions across the data and analytics landscape and connecting with colleagues and friends was also fun. The 2023 Summit is planned for March 20-22. WhizAI hopes to see you there. Contact us to learn more.