We are bringing you news and highlights from the Gartner Data & Analytics Summit, taking place this week in Sydney, Australia. Below is a collection of the key announcements and insights coming out of the conference.
On Day 1 from the conference, we are highlighting the Gartner opening keynote presentation on innovation amidst uncertainty, as well as sessions on data quality and the top trends in D&A.
Presented by Sally Parker, Senior Director Analyst, Gartner and Peter Krensky, Director Analyst, Gartner
Data and analytics (D&A) have simultaneously been a driver, enabler and response to the uncertainty organizations have dealt with for more than two years. In this session, Sally Parker, Senior Director Analyst at Gartner and Peter Krensky, Director Analyst at Gartner, explored how organizations can consider new perspectives in D&A and design better decisions in a world of perpetual change.
“As D&A professionals we should always be asking ourselves not just whether we are collecting, integrating and storing our data in the best way possible, but more fundamentally, whether we have the right data.”
“Having the right varieties of data is more important than volume.”
D&A leaders can ensure they have the right data by doing four things: 1) stop collecting data just in case; 2) consider substituting big data for small data, 3) swap real data for synthetic data, and 4) ensure your active metadata tells you not only what data you have, but also what it means.
“The most valuable data will be the data we create, not the data we collect.”
Gartner estimates that by 2030, the majority of the data used to build models will be synthetic data.
“Data alone is unlikely to drive decisions. Design better decisions by improving the timing of decisions, accelerating decisions and connecting decisions.”
Presented by Melody Chien, Senior Director Analyst, Gartner
High-quality, trusted data is critical for successful digital business initiatives. If data and analytics leaders are to enable a data-driven organization, they must take pragmatic and targeted actions to improve their enterprise data quality. In this presentation, Melody Chien, Senior Director Analyst at Gartner, reviewed a few key actions to help organizations achieve their data quality goals.
“It’s estimated that poor data quality costs organizations an average of US$12.9 million dollars per year. However, these issues do not have to take a lot of time to fix.”
“Start by identifying specific business outcomes that can be influenced by improving data quality. Not all data is equally important.”
“Then, get alignment from stakeholders on the definition and expectations of data quality, and establish data quality standards for critical and commonly-shared data elements across the enterprise.”
“Assign data quality accountability. Ensure that data quality programs are included within enterprise-wide D&A governance initiatives, and establish ownership of data quality responsibilities.”
“It’s important to perform data profiling as a routine process, measuring quality of targeted data elements and analyzing results to identify potential quality problems.”
“Engage stewards and stakeholders to build an improvement plan to act on these profiling results. Consider both short-term and long-term, preventative plans.”
Presented by Ian Bertram, Managing VP, Gartner and Carlie Idoine, VP Analyst, Gartner
Managing consequent and persistent uncertainty and volatility will be a key focus for data and analytics (D&A) leaders in 2022. In this session, Ian Bertram, Managing VP and Carlie Idoine, VP Analyst at Gartner, highlighted the top D&A trends that must be on leaders’ radars to drive new growth, efficiency, resilience and innovation.
Trend #1: Adaptive AI Systems. As decisions become more connected, more contextual and more continuous, D&A leaders need to reengineer decision-making. Use adaptive AI systems that can offer faster and flexible decisions by adapting more quickly to changes.
Trend #2: Data-Centric AI. Without the right data, building AI is risky and possibly dangerous. Formalizing data-centric AI as part of your data management strategy will ensure AI-specific data considerations, such as data bias, labeling and drift, are addressed.
Trend #3: Context-Enriched Analysis. It helps identify and create further context based on similarities, constraints, paths and communities. By 2025, context-driven analytics and AI models will replace 60% of existing models built on traditional data.
Trend #4: Skills and Literacy Shortfall. Through 2025, the majority of CDOs will have failed to foster the necessary data literacy within the workforce to achieve their stated strategic data-driven business goals. Organizations must foster broader data literacy and digital learning, rather than simply delivering core platforms, datasets and tools.
Trend #5: Connected Governance. Connected governance provides a means to connect disparate governance efforts, including D&A governance, across different organizations, both physical and virtual, as well as geographies.
Trend #6: Expansion to the Edge. Data, analytics and the technologies supporting them increasingly reside in edge computing environments, closer to assets in the physical world and outside IT’s traditional purview.
Gartner, Inc. (NYSE: IT) delivers actionable, objective insight to executives and their teams. Our expert guidance and tools enable faster, smarter decisions and stronger performance on an organization’s mission-critical priorities. To learn more, visit gartner.com.
Susan Moore
Gartner
susan.moore@gartner.com
