Austin Kronz

Data Mesh Learning (DML) is a community of 8,000+ data leaders on their
data mesh journey. In September 2023, we surveyed the community for
insights to one of the most consistent questions we are asked: How do I get
buy-in for my data mesh implementation?
There is no one “right” way to implement data mesh. Much depends on
your organization’s culture and adaptability. What you will find, however, is
that regardless of whether everyone had confidence in data mesh before
the… Show more

Data Mesh Learning (DML) is a community of 8,000+ data leaders on their
data mesh journey. In September 2023, we surveyed the community for
insights to one of the most consistent questions we are asked: How do I get
buy-in for my data mesh implementation?
There is no one “right” way to implement data mesh. Much depends on
your organization’s culture and adaptability. What you will find, however, is
that regardless of whether everyone had confidence in data mesh before
the implementation began—they certainly did after.
To ensure the data was useful, we only accepted responses from those who
indicated they had started their data mesh journey (not those who were
evaluating data mesh). We paired the results with successful real world case
studies from members of the DML community to provide you with recommendations for your data mesh journey.
The resulting whitepaper was produced by a DML working group composed
of community members. We thank participants for their contributions. Show less

In this article, we explore how data leaders can more proactively recognize when you need data governance, build solid foundations for a data governance strategy, and get buy-in for your initiative.

Today’s analytics and BI platforms are augmented throughout and enable users to compose low/no-code workflows and applications. Cloud ecosystems and alignment with digital workplace tools are key selection factors. This research helps data and analytics leaders plan for and select these platforms.

The optimal organizational model for data and analytics requires balancing centralized and decentralized teams that collaborate within lines of business. To enable a model that effectively uses data and analytics, executive leaders should leverage one of the three approaches explained.

Twenty-three Gartner analysts explore the future through science fiction stories. Executive leaders should use the 23 stories in the downloadable e-book to prepare their organizations for the metaverse, artificial intelligence, sustainability, cloud, security, privacy and other emerging trends.

This Hype Cycle will help data and analytics leaders evaluate the maturity of innovations across the ABI space. Key trends include consumer-focused augmented analytics, composability of D&A ecosystems, and the governance and education required to execute a variety of analytics at scale.

The D&A trends covered in this research can help organizations respond to change, uncertainty and the opportunities they generate over the next three years. Data and analytics leaders must turn these trends into key investments to accelerate their capabilities to anticipate, shift and respond.

This Hype Cycle will help data and analytics leaders evaluate the maturity of innovations across the analytics and BI space. Key trends include human augmentation, consumerization of analytics and BI platforms, and a focus on enabling organizations to make appropriate use of data and analytics.

Data and analytics leaders are under pressure to deliver data-driven insights by introducing new ABI platforms and technologies. This document helps them introduce and expand ABI platforms focused on solving business problems by conducting structured POCs.

Human-made dashboards and reports are helpful, but alone do not allow for data-driven decisions at scale. Data and analytics leaders must take advantage of multiple consumption experiences to deliver highly consumerized and contextualized insights to more users in the post-COVID-19 era.

hese data and analytics technology trends will help to accelerate renewal, drive innovation and rebuild society over the next three to five years. Data and analytics leaders must examine how to leverage these trends into “must have” investments that enable recovery and reinvention after the reset.

This Toolkit includes a customizable request for proposal template and a questionnaire. It will help data and analytics leaders define and prioritize required capabilities before selecting a modern analytics and BI platform vendor.

This tool contains a simple six-question survey that data and analytics leaders can administer to analysts and decision makers to determine how well their analytics and BI strategies meet their needs, and to track the progress of these strategies.

The analytics and business intelligence platform market has transitioned from the visual data discovery era to the augmented era. Data and analytics leaders should begin piloting capabilities that enable the “augmented consumer.”

Augmented capabilities are becoming key differentiators for analytics and BI platforms, at a time when cloud ecosystems are also influencing selection decisions. This Magic Quadrant will help data and analytics leaders evolve their analytics and BI technology portfolios in light of these changes.

The optimal organizational model for data and analytics requires balancing centralized and decentralized teams that collaborate within lines of business. Data and analytics leaders must take one of several possible approaches to strike the ideal balance.

This Toolkit provides an Analytics Atlas that will help data and analytics leaders to understand and navigate the complexity of business analytics and data science technologies. This can further help synergize capabilities for different analytics initiatives within an organization.

Over the next five years, the way analysis is produced, managed and delivered will change. Data and analytics leaders must ensure that an integrated governance program covers self-service, Internet of Things and decentralized analytics programs.

Although new platforms for self-service analytics and data science offer many benefits, they add to an already siloed landscape. Data and analytics leaders should migrate their legacy data warehouses onto a shared and consistent platform, and rationalize existing business intelligence solutions.

Augmented analytic capabilities are disrupting analytics and BI and data science and machine learning markets. Tools leverage ML/AI to transform how analytics content is developed, consumed and shared. Data and analytics leaders should plan to adopt augmented analytics as capabilities mature.

Data and analytics leaders should expect the next era of analytics to shift focus from content authors to content consumers. Next-generation analytics platforms will emphasize lower prices, augmented analytics, natural language interfaces, and a user experience embedded in business applications.

This Toolkit includes a customizable request-for-proposal template and a questionnaire. It will help data and analytics leaders define and prioritize required capabilities before selecting a modern analytics and BI platform vendor.

When selecting modern analytics and business intelligence platforms, data and analytics leaders should evaluate strategic factors such as customer experience, quality of support and vendor ethics as aspects beyond functionality.

A&BI platforms are transitioning from delivering simple, manual self-service to supporting more advanced, automated analytic use cases via growing, augmented, ML-driven capabilities. Data and analytics leaders should enable broader use cases to increase their investments’ business impact.

Self-service analytics has become a reality as modern analytics and BI tools are now widely adopted, but organizations struggle to deliver in a way that satisfies both IT and the business. Data and analytics leaders can avoid chaos and increase efficiency by balancing control and agility.

Modern analytics and BI platforms are now mainstream purchases for which key differentiators are augmented analytics and support for Mode 1 reporting in a single platform. This Magic Quadrant will help data and analytics leaders complement their existing solutions or move to an entirely new vendor.

This comprehensive analysis of the PaaS market will help organizations navigate a complex terrain of cloud platform services. It is designed for application leaders guiding their organizations to realize the full business value of cloud computing.

Analytics is at a crucial juncture, with new waves of analytic capability promising to spur even more radical transformation. Data and analytic leaders must balance pragmatic delivery of immediate benefits with a long-term vision for the role of analytics within their organization.

This Toolkit contains a simple five-question survey that data and analytics leaders can administer to analysts and decision makers to determine how well their analytics and BI strategies meet their needs, and to track the progress of these strategies over time.

Citizen data science fills the gap between mainstream self-service analytics by business users and the advanced analytics techniques of data scientists. Data and analytics leaders should use CDS to explore new data sources, apply new analytics capabilities and access a larger user audience.

Key innovations in data science and machine learning are about rigor and discipline, as well as transformational insights. Data and analytics leaders should engage with vendors innovating in areas such as data management, unstructured data analysis and model operationalization.

Organizations have relied on spreadsheets for a range of business analytics purposes for many years. Data and analytics leaders must evaluate their continued reliance on such tools for use cases such as dashboard creation, reporting and modeling.

Data and analytics leaders should evaluate customer experience, quality of support, ease of use and achievement of business benefits as aspects beyond functionality when selecting modern analytics and business intelligence platforms.