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Gartner Data & Analytics Summit: A SearchCIO photo roundup

In this photo roundup, SearchCIO chronicles our time at the 2017 Gartner Data & Analytics Summit and explores its major themes, including data centricity and the rise of AI.

Given the vast amount of information available at their fingertips, business leaders' ultimate goal should be to apply data and analytics insights to every process and every decision made in every profession, according to organizers of this year's Gartner Data & Analytics Summit in Grapevine, Texas.

At the summit, an annual gathering of analysts and IT and data professionals, the emphasis was on the future. Analytics is evolving fast and transforming the way businesses operate, but there's still a long way to go -- and it's IT executives' responsibility to get us there.

But fret not: The analytics boom is on the horizon, fueled by the internet of things, artificial intelligence (AI) and new innovations. With strong leadership, we will be on our way to data analytics abundance, Gartner analysts said.

Sessions at the summit explored everything from organizational restructuring, AI and machine learning to virtualization, workplace diversity and digital twins. I was there for two busy days, reporting on everything through the SearchCIO lens. In this Instagram roundup, I highlight some of the most pertinent and interesting moments from the summit.

Day 1: Making data connections

Monday, March 6, 9:15 a.m.: "Gartner Opening Keynote: Lead in the Age of Infinite Possibilities"

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Debra Logan, a Gartner vice president and fellow, and Gartner analyst Kurt Schlegel kicked off the Gartner Data & Analytics Summit with an opening keynote stressing that although we have an abundance of data and opportunities, there is still scarcity when it comes to budgeting, skill development and establishing the right data analytics culture.

They explained the way to overcome that scarcity is through organizational restructuring -- including the addition of a chief data officer (CDO) -- that may require changes to decision-making processes and how modern technology and applications are used for data analytics.

Another point made in the keynote: Companies need to get stronger at data governance, a key component of data analytics success. As data is more widely distributed and in ways that often make its origins and its semantics unclear, governance continues to get harder -- making it vital for organizations to put decisions and responsibility for data governance closer to the point of action.

Monday, March 6, 10:45 a.m.: "Key Trends in Artificial Intelligence and Machine Learning"

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Gartner analyst Alexander Linden started his session by defining machine learning as a "technical discipline that solves business problems through the extraction of knowledge from data." Machine learning, according to Linden, represents a paradigm shift in classical engineering and problem-solving.

From there, Linden got into the nitty-gritty of machine learning and deep learning, extrapolating on the power and potential of the technology. He said, by 2019, deep learning will provide best in-class performance for demand, fraud and failure protection. But Linden was clear that our tools in the field of machine learning are currently insufficient -- too juvenile, too old, too expensive and/or too isolated. The work must start now.

Monday, March 6, 2:00 p.m.: "How Diversity Can Solve The Skills Gap"

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Gartner's Logan took the stage again in the afternoon, this time talking about the often-overlooked topic of diversity in the IT and analytics realm. As she put it: "We have an image problem." And that problem is a leadership issue, not a management one, she said.

Over the course of her session, Logan made the business case for diversity on the IT team -- and not just visual diversity, but also experiential and thought diversity. She cited a McKinsey & Company study that found increased diversity led to noticeably better business performance. More diverse companies and companies with diversity programs are better able to win top talent and improve their overall productivity and decision-making, she added.

Monday, March 6, 4:00 p.m.: "Case Study: Food Inspection Forecasting at the City of Chicago"

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Gene Leynes, data scientist for the city of Chicago, was on hand to explain how data analytics led to successful food-inspection forecasting in his city. How did his team do it? They started by hiring a CDO and creating an open data portal that provided near-real-time updates on everything from crimes to water quality. The portal became their main data warehouse that they used in the creation of their model. The model linked historic food-inspection data to several other sets of relevant data -- temperature, time of day, inspectors on hand -- to find patterns and gain insights into the food-inspection process.

Monday, March 6: End of Day 1

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Following a day filled with interesting sessions, I took a breather with fellow attendees on a balcony overlooking the massive Gaylord Texan Resort and Convention Center.

Day 2: Data-fueled innovation

Tuesday, March 7, 8:30 a.m.: "Guest Keynote: An Interview with 'Mr. Robot' Creator Sam Esmail"

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In a brush with Hollywood, Sam Esmail, creator, executive producer, writer and director of the popular USA Network show Mr. Robot, dropped by the Gartner summit to talk about his inspiration for the show and its driving themes.

He discussed positive and negative aspects of the hacking culture, how social media is making us lonelier and more fragmented, why cybersecurity investment needs to be a priority and the importance of getting data in the right hands at the right time.

"Data is power," Esmail said. "Power in the wrong hands can corrupt. That's not an excuse to pull back."

Tuesday, March 7, 9:45 a.m.: "What to Do and Not to Do with Smart Machine Technology, AI and Cognitive Computing"

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After Tuesday's keynote, Gartner analyst Tom Austin sought to cut through the hype and reset expectations about AI and smart machines. They are not magic, he said. In most cases, they don't learn -- they're just force-fed data and trained to respond in certain ways. Along with that, they can make mistakes and are too narrowly focused, because they are usually designed to only address a specific subject. Truly advanced smart machines, he said, are a distant reality.

Austin urged attendees to be practical, invest in outcome-focused, AI-rich applications and build internal experience. By shooting for more immediate returns, there is a better chance of success.

Tuesday, March 7, afternoon: Waffle sundae break

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In between afternoon sessions, waffle ice cream sundaes were offered to attendees on the expo floor. Fact: No reporter can resist waffle ice cream sundaes.

Tuesday, March 7, 3:00 p.m.: "To the Point: Digital Twins -- The Future of IoT and Analytics"

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Riding my sugar high, I made a beeline for Gartner analyst Al Velosa's session on the elusive topic of digital twins. What is a digital twin? Velosa defined it as a digital representation of an object that is implemented in an encapsulated software object that mirrors it. It is characterized by having a functional data structure, being driven by current and contextual data, having one-to-one correspondence and having the ability to be monitored in real time. The potential benefits are significant, to say the least.

It's still very early in the game, Velosa said. Digital twins are just starting to be adopted by organizations -- mainly in the industrial space. No standard has been set, and no vendor ecosystem has been established, meaning it's up to IT leaders to help shape the evolution of digital twins, he said.

And that closed out my time at the Gartner Data & Analytics Summit.

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