Key Takeaways

• “We know from our chief data officer research and surveys that data and analytics leaders that are involved in strategy development are able to deliver far more business value than those who aren’t.”

• “Data and analytics leaders struggle to optimize business value, and there’s often a lack of alignment to mission-critical priorities.”

• “You as data and analytics leaders need to function as your organization’s ‘chief value officers.’”

• “The ROAR (Risk, Opportunity, Appetite, and Return) Model gives you a systematic way to score both benefits and risks for each initiative and then think about the combination of those initiatives in terms of an optimal portfolio.”

• “While we have models that can help you think through and prioritize, it’s what goes behind those models that’s important. Thinking trumps doing.”

• “Optimizing value from data and analytics is a continuous process. It’s not just a once a year activity, it’s not just a quarterly activity — you need to think about it as central to everything else that you manage.”

Learn how to use data & analytics to re-engineer decision making in the free Gartner e-book “The Future of Decisions.”

Key Takeaways

• “Small data looks at analytical techniques that require less data,but offers useful insights.”

• “Large data gives us the context. It allows organizations to use more of their data to enrich their existing data.”

• “Both approaches reduce an organisation’s dependency on big data and enable a richer, more complete situational awareness or 360-degree view.”

• “Small and wide data give D&A leaders more explainability than what they get from big data approaches. They make AI more resilient, focused and less data hungry.”

Read more in the Gartner press release here.

Key Takeaways

• “What we’ve seen in the last two years is that the conversation has shifted from the ‘why’ to the ‘how.’ That is a great sign of progress, because it shows that we’re moving towards a more operational and more practical use of ethics.”

• “The use of artificial intelligence, and the use of technology in general, should be human-centric and socially beneficial, fair, explainable and transparent, secure and safe, and accountable.”

• “Ethics is not a topic that lends itself to exhaustive discussion; there are always more questions than there are answers.”

• “Operationalize your principles by addressing their underlying dilemmas. If you look at AI that needs to be human-centric and socially beneficial, who gets to decide what is socially beneficial?”

• “If you can create an advisory board with a lot of diversity, the chances that you can circle a dilemma from all kinds of perspectives and come up with perspectives that satisfy multiple needs are much higher.”