We are bringing you news and highlights from the Gartner Data & Analytics Summit, taking place this week in Orlando, Florida. Below is a collection of the key announcements and insights coming out of the conference. You can read the highlights from Day 1 here.
On Day 2 from the conference, we are highlighting how to deliver business value using AI, best practices for trusted data sharing and actions to improve D&A risk culture. Be sure to check this page throughout the day for updates.
Presented by Peter Krensky, Director Analyst at Gartner
Catalyzed by digital transformation, the need for democratization and the urgency of industrialization, data science and machine learning (DSML) platforms continue to evolve rapidly. In this session, Peter Krensky, Director Analyst at Gartner, examined the major trends, data science talent personas and an overview of leading technologies in the DSML space.
“The dawn of AI is apparently going to take close to a decade.”
“You’re too late to be early in data science and machine learning and 1-2 years away from being late.”
“In-house data science teams are both worth it and a significant expense/hassle.”
“Citizen data science and expert data science are distinct but obviously related disciplines with heavy interplay.”
“There are more personas than ever involved in data science, plus they’re evolving!”
“There are plenty of beginner and intermediate opportunities to deploy data science and machine learning.”
“The data science platform market bifurcated in 2021-2022 and the entire space is in transition.”
Presented by Lydia Clougherty Jones, Sr. Director Analyst at Gartner
Sharing data is a must for revenue growth, cost optimization, improved risk mitigation and accelerating digital business. In this session, Lydia Clougherty Jones, Sr. Director Analyst at Gartner explained the business imperative of data sharing to help D&A leaders modernize and align data sharing with stakeholder priorities, enterprise goals and organization benefit.
“Mandated enterprise data sharing is closer than you think.”
“Global data strategies highlight data sharing as a key priority to generating public and private value.”
“Data sharing is a business-facing key performance indicator (KPI) of achieving effective stakeholder engagement and providing enterprise value.”
“Embed data sharing in every relationship.”
“Embrace the chaos within augmented data ecosystems outside of your organization’s control to find known and unknown relationships in combinations of diverse data.”
“Organizations often unnecessarily require too much trust, or not enough, across data ecosystems, disrupting the risk/reward calculus of data sharing for business value.”
“Trusted data sharing means the optimal, not perfect, level of trust across data sharing ecosystems. Apply “situational trust,” not perfect trust, to achieve maximum value and benefit from data sharing.”
“While occasionally the right amount of trust could also be perfect levels of trust, business leaders must resist the emotional pull toward over-investing in perfect trust, which ironically can create enhanced risk given emerging D&A liability theories.”
“The journey of eschewing perfect trust, and instead establishing the right trust to match the situation at hand, enables new business opportunities for data reuse and resharing, accelerating data and analytics value while mitigating risk.”
Presented by Saul Judah, VP Analyst at Gartner
A data-driven culture is a key to the success of data and analytics teams. But if your culture is not risk-aware, your investments in data, analytics and AI will be exposed to greater risk. In this session, Saul Judah, VP Analyst at Gartner, explained why a risk-aware culture will help data and analytics leaders deliver better business value, and five actions they can take to improve their data and analytics risk culture.
“Good business decisions cannot be made unless you understand risk.”
Gartner identified five actions to take to improve risk culture in organizations.
1: Assess your culture with observation, metrics, interviews and surveys.
2: Analyze how culture impacts your data analytics strategy and operating model. “Even if you have the best strategy on the planet, your culture could be a limiting factor.”
3: Develop risk-aware principles for data and analytics. “A principal is a clear statement. It is universal and applies as an anchor for behavior.”
4: Apply “culture hacks” necessary for awareness. “Culture hacking is a method that you can use to make a series of immediate small changes in support of a larger transformation.”
5: Be prepared to explain the business impact for each risk, otherwise you will fail. “If you don’t explain the business impact, then the funding you need to address the risk will be difficult to get and if you do get the funding, all the work that you do to address the risks will appear to be on the cost-side.
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Meghan Rimol
Gartner
Meghan.Rimol@Gartner.com
