Top Trends in Data and Analytics, 2022
Published 11 March 2022 - ID G00763301 - 46 min read
By Rita Sallam, Ted Friedman, and 37 more
Top trends in data and analytics technology and practices can help anticipate change and transform
uncertainty into opportunity. Data and analytics leaders must factor these trends into key investments that
drive new growth, efficiency, resilience and innovation.
Overview
Opportunities
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Connections between diverse and distributed data and people create truly impactful insight and
innovation. These connections are critical to assisting humans and machines in making quicker,
more accurate, trustworthy and contextualized decisions while taking an increasing number of factors,
stakeholders and data sources into account.
CEOs’ highest priority is to return to and accelerate growth, but they must do so in an extremely
uncertain and volatile environment. Capabilities that enable navigating and responding to accelerated
disruption across all aspects of the geopolitical environment, business, government and society are
foundations of success.
Prioritizing trust and security in these unprecedented times of global chaos is fundamental to the
strategic role of data and analytics to realize new sources of value.
Recommendations
Data and analytics leaders looking for new opportunities for their D&A programs and practices should:
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Improve situational awareness to rapidly adjust to disruption and uncertainty by prioritizing investment
in data and analytics diversity and dynamism, including adaptive AI systems, expanded data sharing
and data fabrics.
Drive new sources of innovation and value for stakeholders by implementing context-driven and
domain-relevant analytics to be composed from modular capabilities by the business. Addressing the
scarcity of skills and hireable D&A talent is a top existential priority.
Institutionalize trust to achieve pervasive adoption and value at scale by managing AI risk and
security, and enacting connected governance across distributed systems, edge environments and
emerging ecosystems.
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What You Need to Know
The past year has seen an accelerated pace of disruption across all vectors of business, government and
society with the potential for even more profound shockwaves to come as a result of the Russian invasion of
Ukraine. The global health crisis has been displaced for the moment by a geopolitical one, and the
combination continues to shift people’s priorities, their values and their roles as family members, customers,
employees and citizens. These conditions are driving extreme uncertainty, but also opportunity. This will only
accelerate.
CEOs’ highest priority is to return to and accelerate growth while faced with uncertain and highly fluid global
political, economic and health realities and their impacts.1 For organizations to thrive in this environment and
realize value at scale, they need to optimize for a new value equation. One which enables them to respond
more quickly than their competitors to shifts in customer and employee values and accelerates new product,
channel and business model innovations, particularly in response to macroeconomic and political disruptions.
At the same time, organizations must factor in new stakeholder demands for a response to geopolitical,
sustainability and social justice issues. These create new market dynamics, heated competition for talent,
acute supply chain challenges, and renewed macroeconomic unknowns, such as inflation, new regulation and
political shifts. Increasingly frequent weather disruptions introduce yet another set of challenges and new risks.
Combined, these issues have made managing radical uncertainty the purview of the D&A leader, and a critical
core competency for success.
It is vital for data and analytics leaders responsible for strategy and innovation to take action along three tracks
set out in our analysis of the Top Trends in Data and Analytics for 2022 (see Figure 1):
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Activate diversity and dynamism by leveraging the rise of adaptive AI systems to drive growth and
innovation while coping with fluctuations in global markets. Innovations in data management for AI,
automated, active metadata-driven approaches and data-sharing competencies, all founded on data
fabrics, unleash the full value of data and analytics.
Augment people and decisions to deliver enriched, context-driven analytics created from modular
components by the business. To make insights relevant to decision makers, organizations must also
prioritize data literacy and put in place strategies to address the scarcity of hireable data and analytics
talent.
Institutionalize trust to achieve value from D&A at scale by managing AI risk and enacting connected
governance across distributed systems, edge environments and emerging ecosystems.
Figure 1. Gartner’s Top Data and Analytics Trends for 2022
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The top D&A trends represent business, market and technology dynamics that you cannot afford to ignore
(see Table 1). They have the potential to transform your enterprise, and will accelerate in their adoption over
the next three years. You should decide whether to proactively monitor, experiment with or aggressively invest
in key trends based on their urgency and alignment to strategic priorities.
The top D&A trends and technologies do not exist in isolation; they build on and reinforce one another. We
have selected our trends for 2022 in part based on their combined effects. Taken together, our top data and
analytics technology trends for 2022 will help you meet your organization’s top strategic priorities to anticipate,
adapt and scale value. This, in turn, will enable you to encourage innovation and put in place success metrics
and incentives that emphasize learning and reward innovation.
Enlarge Table
Table 1: Top Trends in Data and Analytics, 2022
Section
Trends
Activate Dynamism and Diversity
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Adaptive AI Systems
Data-Centric AI
Metadata-Driven Data Fabric
Always Share Data
Augment People and Decisions
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Context-Enriched Analysis
Business-Composed D&A
Decision-Centric D&A
Skills and Literacy Shortfall
Institutionalize Trust
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Connected Governance
AI Risk Management
Vendor and Regional Ecosystems
Expansion to the Edge
Source: Gartner (March 2022)
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Activate Dynamism and Diversity
Adaptive AI Systems
Analysis by: Erick Brethenoux, Soyeb Barot, Ted Friedman
SPA: By 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI
systems will outperform their peers in the number of operationalized AI models by at least 25%.
Description: Adaptive AI systems aim to continuously retrain models and learn within runtime and development
environments based on new data, in order to adapt more quickly to changes in real-world circumstances that
were not foreseen or available during initial development. AI engineering orchestrates and optimizes
applications to adapt to, resist or absorb disruptions, facilitating the management of adaptive systems. AI
engineering provides the foundational components of implementation, operationalization and change
management at the process level in order to enable adaptive AI systems. Adaptive AI systems support a
decision-making framework centered around making faster decisions while remaining flexible to adjust as
issues arise.
Why Trending:
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Flexibility and adaptability are now a fundamental business requirement — many organizations
unfortunately learned this the hard way during the global COVID-19 pandemic. Reengineering
systems has significant impacts on employees, businesses and technology partners. For many
enterprises, these changes demand resilience by design and adaptability by definition.
The value of fully industrialized AI lies in the ability to rapidly develop, deploy, adapt and maintain
AI across different environments in the enterprise. Given the engineering complexity and the demand
for shorter time to market, it is critical to develop less rigid AI engineering pipelines.
Automated, yet resilient, adaptable systems will require composable D&A architectures with
application development to assemble intelligent decision-making solutions.
Decision making is a core capability, and it is becoming more complex. Decisions are becoming more
connected, more contextual and more continuous. Hence, D&A leaders need to reengineer decision
making enabled by adaptive systems in the future.
Implications:
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Adaptive AI systems will require processes to be reengineered for automated decision-making.
Increased automation will, in turn, require business stakeholders to ensure the ethical use of AI for
compliance and regulations.
Adaptive AI systems, while enabling new ways of doing business, and by leveraging generative AI
capabilities, will result in the creation of new business models, products, services and channels.
While adapting to context, real-time changes fostered by adaptive capabilities will promote new
collaboration between organizations; this is how adaptive AI systems will enable cross-organizational
change. It is possible to only change a few internal functions using adaptive AI systems, but that would
amount to local optimization, which defeats the purpose.
The bringing together of the D&A, AI and software engineering practices will be critical in building
adaptive systems. AI engineering is going to play a critical role: building and operationalizing
composable architectures.
Recommendations:
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Data and analytics leaders should:
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Make it easier for business users to adopt AI and contribute to managing adaptive AI systems by
incorporating explicit and measurable business indicators through operationalized systems and
institutionalizing trust within the decisioning framework.
Maximize business value from ongoing AI initiatives by establishing AI engineering practices that
streamline the data, model and implementation pipelines to standardize AI delivery processes.
Place limits on the amount of time a system takes to make a decision in response to disruptions, while
focusing on the critical components of the decision-making framework. The new decision-making
process should encourage the use of flexible initial decisions that can be amended as more
information is gathered about the environment.
Changes Since Last Year
As part of the Top Data and Analytics Trends, 2021, we introduced a trend called XOps (see Top Trends in
Data and Analytics for 2021: XOps). We also expanded on this trend in Gartner’s Top Strategic Trends
research by evolving XOps into AI engineering (see Top Strategic Technology Trends for 2022: AI
Engineering). AI engineering is a discipline that streamlines the AI development and operationalization life
cycle by leveraging DataOps, ModelOps and DevOps, paving the way to build automated adaptive AI systems.
Over the past 10 years, AI-based systems have been built for efficiency and autonomy, but operationalization
has remained brittle, even if AI engineering practices are on the rise.
Data-Centric AI
Analysis by: Svetlana Sicular, Ted Friedman, Mike Fang, Erick Brethenoux
SPA: By 2024, organizations that lack a sustainable data and analytics operationalization framework will have
their initiatives set back by up to two years.
Description: Data-centric AI disrupts traditional data management and prevalent model-centric data science by
addressing AI-specific data considerations, such as data bias, labeling and drift, in order to improve the quality
of models on an ongoing basis.
Why Trending:
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The AI community is facing a bifurcation of “model-centric” and “data-centric” AI, because data quality
and consistency improve AI accuracy more efficiently than tweaking models.
Most commonly delivered AI solutions depend on data availability, quality and understanding, not just
AI model building. However, many enterprises attempt to tackle AI without considering AI-specific data
management issues. The importance of data management in AI is often underestimated.
Traditional data management is ripe for disruption, to support AI efforts. Without the right data,
building AI is risky and possibly dangerous. In most organizations, AI-specific considerations, such as
data bias, diversity and labeling, are addressed haphazardly.
The AI community remains largely oblivious to data management capabilities, practices and tools that
can greatly benefit AI development and deployment.
AI reflects and amplifies bias originating in the choice of algorithms, data interpretation and labeling,
and human bias recorded in the data. Bias mitigation is an acute, AI-specific problem. People interpret
data, curate it and put algorithms and data together. For this reason, AI-centric risk management,
including data governance, is also a related trend.
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Implications:
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Data-centric AI is evolving, and should include relevant data management disciplines, techniques and
skills, such as data quality, data integration and data governance, which are foundational capabilities
for scaling AI.
Data management activities don’t end once the model has been developed. Deployment
considerations and ongoing monitoring of the relevance of the model require dedicated data
management activities and practices.
Organizations that invest in AI at scale will shake up their data management practices and capabilities
to preserve the evergreen classical ideas and extend them to AI by making them AI-centric in two
ways:
o Add capabilities necessary for convenient AI development by an AI-focused audience that is
not familiar with data management.
o Use AI to improve and augment evergreen classics of data governance, persistence,
integration and data quality. For example, by making augmented classical profiling, cleansing,
visualization and entity resolution available to AI teams.
Leading enterprises are building out data fabric and active metadata, while the tooling for data
management is about to be reinvented. They must decide what to build themselves now, and what to
wait for. For example, enterprises are aggressively implementing graphs and extending their
governance to AI.
Recommendations:
Data and analytics leaders should:
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Formalize data-centric AI and AI-centric data as part of your data management strategy. Implement
active metadata and data fabric as a key foundational component of this strategy.
Leverage an ecosystem of AI-centric data management capabilities that combine traditional and new
capabilities to prepare the enterprise for the era of decision intelligence.
Promulgate policies about data fitness for AI. Define and measure minimum standards, such as
formats, tools, metrics, etc., for AI-centric data early on. This will prevent the need to reconcile multiple
data approaches when you take AI to scale.
Seek diversity of data, algorithms and people to ensure AI value and ethics.
Establish roles and responsibilities to manage data in support of AI. Leverage AI engineering and data
management expertise and approaches to support ongoing deployment and production uses of AI.
Include data management requirements when deploying models.
Metadata-Driven Data Fabric
Analysis by: Robert Thanaraj, Melody Chien, Ehtisham Zaidi, Mark Beyer, Mayank Talwar
SPA: By 2025, active metadata-assisted automated functions in the data fabric will reduce human effort by a
third, while improving data utilization fourfold.
Description: Metadata is “data in context” — the “what,” “when,” “where,” “who” and “how” aspects of data. The
data fabric listens, learns and acts on the metadata. It applies continuous analytics over existing, discoverable
and inferenced metadata assets. By assembling and enriching the semantics of the underlying data, the data
fabric generates alerts and recommendations that can be actioned by people and systems. It improves trust in,
and use of, data in your organization as a result.
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Why Trending:
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Practitioners are now able to experiment with the data fabric design:
o Advancements in technology, such as graph analytics, graph-enabled machine learning,
automated data content analysis and profiling, have increased the level of automation that can
be introduced to data management overall.
o Cloud capacity has enabled the expansion of data assets in terms of volume and variety,
while at the same time offering significantly more complex resource allocation and utilization
models in an on-demand, elastic environment.
Metadata-driven data fabric has significant business value potential to:
o Reduce by 70% various data management tasks, including design, deployment and
operations. The city of Turku in Finland found its innovation held back by gaps in its data. By
integrating fragmented data assets, it was able to reuse data, reduce time to market by twothirds and create a monetizable data fabric.
o Accelerate adoption through timely and trusted recommendations, enabling business experts
to consume data with confidence. It also enables less-skilled citizen developers to become
more versatile in the integration and modeling process.
o Optimize costs, because data fabric designs are built on the foundations of balancing collectand-connect data management strategies. It does not require you to rip out and replace
existing systems. Data stores and applications participate by providing metadata to the data
fabric. Then, by analyzing the metadata across all participating systems, the data fabric
provides insights on effective data design, delivery and utilization, thereby reducing costs.
Implications:
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Metadata analysis can expose hidden insights into business demand, metadata sharing can speed up
integration and decision making, and metadata can reinvent governance and reduce risk. The existing
data management systems, analytical platforms and systems of record are mere participating systems
in the data fabric design — they feed metadata to the data fabric.
By assembling and enriching the semantics of the underlying data, and by applying continuous
analytics over metadata, the data fabric generates alerts and recommendations that can be actioned
by both humans and systems. Such a high degree of automation drives effective data design, delivery
and use, reduces human efforts and yields a high ROI.
Semantic modeling skills will have a profound impact on several roles in an enterprise:
o Application developers building customer-facing applications increasingly use graph
databases as the storage and execution back-end.
o Data architects designing knowledge-graph-based solutions for content management,
personalization and semantic data interoperability.
o Data scientists performing higher-order exploration into connections and relationships
between data points for better insights through graph visualizations, queries and algorithms.
o Database designers seeking alternative solutions to growing volumes of semistructured data.
Recommendations:
Data and analytics leaders should:
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Start monitoring how data is used, and leverage discovery tools to look for new and unexpected uses
of data. This might imply new opportunity, or an emerging risk that warrants some attention.
Target known opportunities and pain points by investing in experimentation and innovation with
metadata. Assess ways to capture system logs, user logs, transaction logs and current data locations
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from your existing systems. There will be initial pushbacks from your application owners — create a
shared benefit statement.
Initiate a pilot effort to build a “limited” data fabric by identifying an intersection of data used, use
cases, users and systems performing the data management, and the affected business units.
Changes Since Last Year
The data fabric trend progresses toward augmented data management principles by generating
recommendations and alerts to its participating systems and individuals. We have published a few case
studies on initiatives that have already begun to show real benefits, such as that of Montefiore (see Gartner
Recommended Reading).
Always Share Data
Analysis by: Lydia Clougherty Jones, Eric Hunter, Malcolm Hawker, Jason Medd
SPA: By 2026, applying automated trust metrics across internal and external data ecosystems will replace
most outside intermediaries, reducing data sharing risk by 50%.
Description:
Data sharing includes sharing data internally (between or among departments or across commonly owned and
controlled parties such as subsidiaries or sister companies) and externally (between or among parties outside
the ownership and control of your organization). The longstanding calculus that data sharing is not worth the
risk of data misuse is obsolete. We observe that a risk of failure now inures to those organizations that do not
share data automatically, or “always share data,” unless there is a vetted reason not to. Data sharing is a
business-facing key performance indicator that an organization is achieving effective stakeholder engagement
and providing enterprise value.2 D&A leaders at high-performing organizations promote data sharing or
increase access to the right data aligned to the business case.3
Why Trending:
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Organizations need more and more singular types and complex combinations of data to feed their
increasingly voracious use of data and analytics in order to drive business value. This can often be
found in silos within the organization and increasingly, external to it.
D&A leaders responsible for building a data-driven enterprise also require expansive data sharing, yet
they struggle to identify and then locate the right data for their business case. They also face internal
data hoarding, external data hijacking and privacy shaming.
The global COVID-19 pandemic created urgency to share data in order to accelerate independent and
interrelated public and commercial digital business value, as well as improvements to surrounding
agility and resilience.
Global data strategies highlight data sharing as a key priority for increasing government efficiency and
generating public value. They also encourage industry data sharing with the purpose of producing
market growth.
External data has an increased level of relevance for D&A leaders in support of predictive models, as
models trained exclusively with internal or first-party data have seen model drift due to phase shifts in
customer behaviors.
There is a lack of relevant available data for AI training, as well as sustainability and cost pressures for
processing large amounts of AI training data.
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We observe increased demand for more robust predictive analytics generated from more diverse data
sources to drive relevant, unique or otherwise unknowable insights and data-driven innovation.
Implications:
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D&A leaders know that data sharing is a key digital transformation capability, but they lack the “know
how” to share data at scale and with trust.
D&A leaders will change their investment priorities to automate the identification and acquisition of the
most relevant data, whether from big, small, personal, synthetic or yet to be created data, to match
their business case.
Remedying interoperability challenges and adopting a data fabric design will become a priority,
contributing to environmental sustainability through data centralization, reuse and resharing, while
meeting or exceeding stakeholder value and business outcomes, including composable business
objectives.
Automation and open-data programs ease the investment burden; machine-readable metadata
provides automatic discovery of datasets and services, and open standards for metadata lower the
barriers for their discoverability, reuse and resharing.
Recommendations:
Data and analytics leaders should:
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Collaborate across business and industry lines, promoting data sharing to create individual and
aggregate stakeholder value. This will accelerate buy-in for increased budget authority and investment
in data sharing.
Establish trust in internal and external data, metadata, data sources, data sharing technologies, and
downstream reuse and resharing ecosystems through automated mechanisms and metrics from active
metadata insights, augmented data catalogs and automated data quality metrics
Overcome data sharing hurdles by focusing on, and then quantifying, the risk of not sharing data,
including business failure.
Consider adopting data fabric design to enable a single architecture for data sharing across
heterogeneous internal and external data sources.
Changes Since Last Year
As part of the Top Data and Analytics Trends, 2021, we introduced a trend called “D&A as a Core Business
Function,” which noted that smarter data sharing increasingly plays a key business role. Smarter data sharing
has evolved from “don’t share data unless” to “share data always,” unless there is a well vetted reason not to.
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Augment People and Decisions
Context-Enriched Analysis
Analysis by: Afraz Jaffri, David Pidsley, Sumit Pal
SPA: By 2025, context-driven analytics and AI models will replace 60% of existing models built on traditional
data.
Description: Contextual data originates in multiple sources, including image, text, audio, log, sensor and
associated metadata. It is needed to build a richer knowledge-based-model of business entities and
relationships. Holding this information in a graph structure enables deeper analysis utilizing the relationships
between data points as much as the data points themselves. Graph analytics exploits this structure to identify
and create further context based on similarities, constraints, paths and communities. This forms the basis for
the utilization of active metadata that drive data fabrics, personalization of automated insights and data stories,
powerful feature sets for machine learning models and a wider field of vision for improved decision making.
Why Trending:
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In an ever-changing world full of uncertainty, context is a necessity, not just “nice to have.” Utilizing
contextual data enables building a richer knowledge-based model of business entities and
relationships to meet the challenge of accelerated internal and external business dynamics.
Analytics and data science techniques are applied to data where the properties of each data item are
taken in isolation. Adding contextual information gathered from the relationships between entities
uncovers new patterns of behavior that reveal facts and better insights and generate more predictive
power.
Advances in techniques for audio, image and video recognition, natural language processing (NLP)
and speech-to-text are increasing the accessibility of a wider set of data for use in advanced analytics
and decision intelligence.
Augmented analytics tools capture contextual information about the user, environment, datasets and
analytics outputs to generate automated insights and data stories, as well as personalized news feeds
in social-media-style timelines.
There is an overload of analytics content and insights. Preserving context enables targeted insights to
be provided on demand, enabling consumers to concentrate on core insights that require exploration
and action.
Graph data models naturally accommodate the context of data through relationships and graph
structural properties. Data is represented in a form that is easily understood by business users and
domain experts.
Increasing the usage of data fabrics within data management exposes metadata that captures the
context and usage of data assets.
Implications:
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Capturing, storing and utilizing contextual data demands capabilities and skills in building data
pipelines, X analytics techniques and AI cloud services that can process different data types and
provisioning use cases that include this data for end-user applications.
Automated insight generation using greater contextual insights enables analysts who produce BI
reports and dashboards to focus on the more complex needs of business users. It also enables them
to answer critical business questions and improve insights that are used in decision making. This will
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shift the traditional skills required for analysts from technology-centric to business-focused, and give
rise to the importance of business translators who can identify decision-making points and priorities.
Identifying and accessing contextual data that lies outside an organization requires the enablement of
data sharing services and exchanges and the creation of policies that promote and regulate internal
and external data sharing.
Recommendations:
Data and analytics leaders should:
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Identify multiple data sources and formats that can be used to augment data that is already used for
analytics, data science and AI, and enable the extraction of key entities and relationships.
Utilize the power and flexibility of graph data models to connect data that exists across the
organization into graph structures that can be fed into downstream data management, analytics and AI
processes.
Augment existing machine learning models with features extracted from graph representation using X
analytics, graph analytics and data science techniques.
Identify key personas that can benefit from shorter time to insight and assess analytics and BI
platforms that deliver contextual insights.
Changes Since Last Year
The “Context-Enriched Analytics” trend builds on previous top D&A trends on graph technologies as a key
enabler for data and analytics innovation. This year, the focus is on bridging the gap between structured,
unstructured and metadata with graphs and the utilization of graph analytics for an increasing number of
business use cases, both stand-alone and within packaged applications.
Business-Composed D&A
Analysis by: Julian Sun, Yefim Natis, Shaurya Rana
SPA: By 2025, 50% of embedded analytics content will be developed by business users leveraging a lowcode/no-code modular assembly experience.
Description: Business-composed D&A refers to the active building of D&A capabilities by business
technologists using low-code composition technology along with collaboration tools to assemble the modular
and rich capabilities of organization-level value streams. “Business-composed D&A” shifts application
development power to the business users or business technologists, enabling them to craft business-driven
D&A capabilities collaboratively.
Why Trending:
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Historically, building custom analytics applications or embedding data and analytics in a workflow or
business process required traditional IT-led embedded D&A capabilities, such as APIs and developer
software development kits (SDKs). This is slow and expensive, and requires a high level of technical
skills.
Per a recent Gartner Building Digital Platforms survey, D&A capabilities are the most common and
fastest growing capabilities to be integrated with digital business platforms within an enterprise,
enabling agile decision-making.4
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IT-led embedded D&A is time consuming to deploy and lacks business impact. Business users expect
to close the loop of D&A and build the last mile of it by turning insights into business actions.
Fully decentralized D&A causes siloed insights that achieve local goals at the expense of achieving
organizationwide goals.
Implications:
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D&A leaders will increasingly foster and build collaborative D&A experiences and processes with
cross-functional teams.
The adoption of business-composed D&A, facilitated by fusion teams, will bridge the insights with
actions and decrease business outcome challenges, which wasn’t possible with fragmented
embedded D&A deployed by IT.
Organizations will develop a business-driven data and analytics architecture by composing packaged
business capabilities (PBCs) aligned with value streams.
The D&A catalog will evolve as business technologists create PBCs as reusable D&A assets for
others to easily compose and recompose, beyond traditional datasets, reports or dashboards that
need slow and expensive integration.
The expanded capabilities would bring more complex governance concerns for both D&A and
application development, requiring more Ops practices to manage production. A more comprehensive
Citizen-X program might emerge within the community to share skills, best practices and governance
rules and processes
Recommendations:
Data and analytics leaders should:
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Prepare for growth in the number of citizen developers and business technologists using low-code/nocode technology to broaden their use of self-service analytics by providing training in basic software
development practices.
Evaluate your existing analytics and data science tools and innovative startups offering low-code
enabled composition experience to build rich analytics-infused applications within the business
process.
Foster collaboration and build more PBCs by creating cross-functional fusion teams of both centralized
and decentralized D&A users that are aligned with the corporate-level value streams.
Changes Since Last Year
As part of the Top Data and Analytics Trends, 2021, we introduced a trend called “Composable Data and
Analytics.” Business-composed D&A builds on previous trends in low-code-enabled composition from modular
ABI and DSML capabilities as a key enabler for D&A applications. This year, the focus is on the people side,
which is shifting from IT to business, composing D&A capabilities that are aligned with value stream
management and powered by automation workflow.
Decision-Centric D&A
Analysis by: Gareth Herschel, W. Roy Schulte
SPA: By 2023, more than 33% of large organizations will have analysts practicing decision intelligence,
including decision modeling.
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Description: Careful consideration of how decisions should be made (the discipline of decision intelligence —
see Note 1) is causing organizations to rethink their investments in D&A capabilities. The application of D&A to
organizational decisions requires contextual understanding that this application can take several forms, such
as decision support, augmentation or automation. The initial focus of decision-centric D&A is on the insight that
decision makers need to govern D&A investments, rather than on the available data or the analysis that can be
carried out. This is a sign of increasing maturity of D&A strategies and organizations’ ability to reengineer
decisions to drive business outcomes.
Why Trending:
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Access to a growing variety of data, combined with a continually increasing variety of analytical
techniques, means organizations have an overwhelming permutation of decisions to make about the
analysis they could carry out. Relevance to organizational decisions is an easily identifiable filter for
these options.
Forty-seven percent of organizations believe that the decisions they face will be more complex,
increasing demand for connected, continuous and contextual D&A and explainable
decision processes.5 Decisions are increasingly interconnected and based on continuously changing
data about the business context. Without D&A, the growing complexity of decisions is impossible for
organizations to manage.
Only 58% of organizations report that they consistently and formally define decision ownership.
Decisions are made in several different ways. Personal decision making based on experience, training
or intuition is the most common, but also the most difficult to combine with D&A.
Improving the quality of decisions is not a new goal, but organizations are not good at building the
systems and processes needed for it to be possible. This is the intent behind growing interest in the
discipline of decision intelligence.
Implications:
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Decision-centricity requires a deep understanding of human psychology and behavior. Embedding
D&A into collaborative or individual decisions is most likely to be successful when supported by NLP
or data visualization capabilities.
Decision-centric D&A needs to be aligned with enterprise architecture and technical development
teams. D&A can be embedded into decisions in several different ways. For example, embedding
analytic decision models (e.g., machine learning) into business processes requires technical
integration with business applications, as described in the Business-Composed D&A trend.
Time and complexity are the main criteria for determining which decisions are most suited to
automation with D&A. Simple problems that can be confronted quickly are the most appropriate
candidates for automation, while highly complex problems that need time to solve require decision
support and augmentation, rather than automation.
Recommendations:
Data and analytics leaders should:
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Approach D&A projects by considering which decisions you are seeking to influence, rather than what
data you possess or how to analyze it.
Create new decision-making habits by training decision makers to apply best practices, such as critical
thinking, trade-off analysis, recognizing bias and listening to opposing views.
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Consider creating a role for decision engineers by hiring or upskilling experts who can work with
decision makers to identify opportunities that would benefit from the rigor of decision intelligence
practices.
Changes Since Last Year
The application of D&A to decisions has often been an add-on; we have the data and analysis, then we think
about how to accelerate its deployment to real time, or contextualize it for a collaborative decision. We are
seeing a switch to a decision-first approach: using decision intelligence disciplines to design the best decision,
and then delivering the required inputs (possibly including D&A). This flip from D&A-driven decisions to
decision-driven D&A is subtle, but fundamental.
Skills and Literacy Shortfall
Analysis by: Alan D. Duncan, Eric Hunter, Jorgen Heizenberg, Peter Krensky, Joe Maguire, Sally Parker
SPA: 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.
Description: The 2021 Gartner CDO Survey shows that organizations that deal with the human elements of
D&A are more successful than organizations that only consider technology. With a human focus, the mission
of D&A is to foster broader data literacy and digital learning, rather than simply delivering core platforms,
datasets and tools. Organizations (and society) will need to learn how to learn, and they will need a lot of help
to do so.
Why Trending:
•
•
•
•
Virtual workplaces and the heightened competition for D&A talent have exposed weaknesses in
organizations lacking content-neutral and enterprise-specific data literacy strategies
Increasing talent acquisition efforts require D&A leaders to take an increasingly agile approach to data
literacy and upskilling investments. New hires are increasingly dependent on this investment to
accelerate their contribution of critical delivery and support roles across the organization.
Cultural aversion to change is a prevailing and recurring roadblock to the success of D&A programs.
Expecting data literacy in the workforce as a default is a false presumption.
Momentum is being driven by vendors addressing customer enablement, advocacy and adoption as
part of their go-to-market (e.g., Tableau, Qlik, Alteryx, Collibra). More and more providers are offering
learning solutions for D&A. These solutions include not just technical learning, but also the “soft” skills
for curiosity, critical thinking and communication (e.g., Pluralsight, Skillsoft, The Center for Applied
Data Science, Udacity).
Implications:
•
•
CDOs must distinguish transferrable/domain-neutral skills from domain-specific knowledge and
experience of the organization’s own processes. Those who are carrying out succession planning are
not referring to it as such.
Data literacy and technical upskilling strategies will increasingly embrace the concept of “just-in-time”
channels and options for the provision of key skills and concepts as talent becomes increasingly fluid
(new hires, contingent workers, etc.).
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•
•
•
•
D&A leaders will reduce their selectivity criteria for new hires as competition for talent intensifies, but
will compensate for this through diverse upskilling strategies to complement new hires’ potential.
The cost of investing in data literacy and employee upskilling will lead employers to insert “claw-back”
or “payback” clauses into contracts with new hires in order to recover costs in the event that an
employee departs the organization.
Traditional succession planning discussions will incorporate data literacy performance.
Platform and technology selections will increasingly prioritize capabilities for prebuilt content and
customer literacy training and assessment as a part of key workflows and platform usage
Recommendations:
Data and analytics leaders should:
•
•
•
•
•
•
•
•
Take the lead in creating a narrative that sets a strong vision for the desired end state and business
outcomes, particularly with respect to innovation opportunities and use cases that have not been
identified by others.
Work with line-of-business leaders to trace measurable business outcomes back to supporting
analytics output and underlying data.
Monitor the effects of improved data literacy among the workforce by using data literacy assessments
and measuring associated improvements to data-driven business outcomes.
Distinguish between competition for people who already have D&A skills and educating/training those
who currently do not. Develop a multispeed approach: different approaches for the most enthusiastic,
the slower on the uptake and the outright resisters.
Collaborate with HR and business leaders to run data literacy pilot projects in business areas where
there is a high likelihood of achieving measurable business outcomes. Use quick wins to build
momentum and incentivize staff to use data in their interactions. Using workers’ stated pain points as
stimuli can get them to identify the changes necessary to address those pain points.
Go beyond vendor product training to focus on people’s roles. Use a mix of training delivery methods
by considering the times, locations, roles and skills differences to improve overall learning
effectiveness and experiences for new analytics capabilities.
Formulate a succession plan for key D&A roles both inside and outside IT. Budget for 25% turnover.
Incentivize teaching colleagues what you know, rather than engaging in self-interest-based
protectionism.
Coordinate data literacy initiatives with overall data governance programs.
Changes Since Last Year
The limited pool of D&A talent has become acute, with competition for skilled staff escalating amid the “great
resignation.” There is an increasing recognition that this cannot be solved by reorganization, but by
development of the workforce.
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Institutionalizing Trust
Connected Governance
Analysis by: Andrew White, Saul Judah, Ted Friedman, Guido De Simoni
SPA: By 2026, 20% of high-performing organizations will use connected governance to scale and execute on
their digital ambitions.
Description: Connected governance is a framework for establishing a virtual D&A governance layer across
organizations, business functions and geographies, to achieve cross-enterprise governance outcomes.
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. This approach is similar to
virtualized governance efforts, but with at least one difference: virtualized governance organizations are new
and discrete teams with an independent objective. Connected governance does not exactly create a new
team, but helps connect those that are already established. It also provides a means to align and link efforts so
that complex outcomes shared by the organizations and geographies can be achieved.
Why Trending:
•
•
•
As the anticipated transformation across most industries materializes, organizations that are unable to
address cross-organizational, cross-geographical challenges in a flexible and agile way will be left
vulnerable to competition or fail to meet political pressures in the public sector.
The accelerating pace and complexity of digitalization is putting pressure on senior leaders across
multiple business functions to respond to business and mission demands. Similarly, governments are
consolidating organizations that are often driven by a need to provide more effective and
comprehensive services or regulatory regimes.
Organizations need effective governance at all levels that not only addresses their existing operational
challenges, but is also flexible, scalable and highly responsive to changing market dynamics and
strategic organizational challenges.
Implications:
•
•
New demands for cross-enterprise responses and initiatives will require accountability from multiple
executive leaders. This will require knowledge of local governance accountability and how decision
rights are implemented.
Connected governance will be a mechanism to bring together and coordinate diverse business areas
to avoid time-consuming, watered down or even conflicted decisions and mediocre action and
performance.
Recommendations:
Data and analytics leaders should:
•
•
•
Identify the business scenarios and outcomes that are most difficult to govern in the ecosystem
because of, for example, geographic and organizational diversity, complexity and autonomy.
Build trust in data-driven decision making by setting up connected governance of all data assets in the
enterprise.
Consider how connected governance can help to address these challenges more effectively.
16
AI Risk Management
Analysis by: Avivah Litan, Bart Willemsen, Svetlana Sicular, Sumit Agarwal, Farhan Choudhary
SPA: By 2026 organizations that develop trustworthy purpose-driven AI will see over 75% of AI innovations
succeed, compared to 40% among those that don’t.
Description: The speed of AI innovation is increasing pressure to keep pace while keeping businesses
operating, tempting organizations to cut corners on AI trust, risk and security management (TRiSM). This will
lead to potentially harmful outcomes. Organizations must spend time and resources now on supporting
AI TRiSM. Those that do will see improved AI outcomes in terms of adoption, achieved business goals and
both internal and external user acceptance.
Why Trending:
•
•
•
•
•
•
•
Almost half of AI models developed by experienced organizations do not make it into production, and
users cite security and privacy as a primary reason for this negative outcome (see Survey Analysis:
Moving AI Projects From Prototype to Production).
AI is becoming more pervasive, yet most organizations cannot interpret or explain what their models
are doing. They struggle to ensure trust and transparency in their models.
Most organizations developing AI:
o Are not precise on what they want to achieve when developing models; and often expand the
scope when they see what is possible, making operationalization almost impossible.
o Have no processes, tools or measurements to govern and manage model trust, risk and
security.
o Tend to gather AI training data without deliberate data selection goals. Data is often biased
and inadequate for training models.
o Are driven mainly by regulatory compliance when it comes to model governance. However,
compliance does not necessarily lead to trustworthy models.
The increasing dependence on, and scale of, AI escalates the impact of misperforming AI models with
severely negative consequences.
AI regulations are proliferating across the globe, mandating certain auditable practices that ensure
trust, transparency and consumer protection.
Organizations are not prepared to manage the risks of fast-moving AI innovation and are inclined to
cut corners around model governance.
Organizations that properly and continuously govern their models have much improved AI model
outcomes.
Implications:
•
•
Increased focus on AI TRiSM will lead to:
o Controlled and stable implementation and operationalization of AI models.
o Far fewer AI failures, including incomplete AI projects, and a reduction in unintended or
negative outcomes.
o Positive societal impact if AI is trustworthy and has a legitimate purpose, leading to reduced
discrimination, more fairness and protection of human autonomy and privacy.
In contrast, AI models that are not trustworthy or transparent and do not have a legitimate purpose can
eventually lead to severely negative consequences.
Recommendations:
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Data and analytics leaders should:
•
•
•
•
•
•
Engage all internal and external stakeholders from the start to foster transparency and trust.
Define in full the primary purpose of AI, and use AI engineering and governance to develop metrics to
continuously assess the intended impacts of the models.
Account for the parameters of the entire ecosystem in which change is to be made, in addition to
focusing on the primary purpose of the AI model itself.
Make sure the right amount of the right data is available to train the model to achieve balance,
improve accuracy and mitigate bias.
Consider tools such as synthetic data to generate more useful training data when trusted sources do
not suffice.
Reach beyond compliance by involving the ethics board or other interested parties in creating
purposeful unbiased models.
Changes Since Last Year
The rising interest in privacy-enhancing computation (PEC) techniques (see Top Strategic Technology Trends
for 2022: Privacy-Enhancing Computation for an introduction) is a promising support factor for this trend.
Particularly, the expedient adoption of generative AI-based synthetic data (see Hype Cycle for Privacy, 2021)
will aid in providing non-identifiable, privacy risk-free data to train AI models on.
Vendor and Region Ecosystems
Analysis by: Rita Sallam, Julian Sun, Adam Ronthal, Carlie Idoine, Sumit Agarwal
SPA: By 2024, 50% of new system deployments in the cloud will be based on a cohesive cloud data
ecosystem, rather than on manually integrated point solutions.
Description: D&A ecosystems consisting of increasingly comprehensive D&A capabilities are growing in
availability and capabilities. Both CSPs and ISVs are offering less costly, less complex architecture requiring
little or no integration of products and cloud services. At the same time, many global organizations are
assessing the implications of building parallel regional D&A ecosystems to comply with local regulation. Data
sovereignty, financial governance and orchestration across components are key ecosystem considerations.
Why Trending:
•
•
•
CSPs increasingly view compute- and data-intensive D&A workloads as an attractive vehicle for
overall cloud services revenue growth.
Most CSPs and ISVs are using varying degrees of workflow integration with the promise of
composability, AI-driven automation and capabilities convergence across D&A categories. The aim is
to deliver lower cost, shorter time to deployment and agility as a key competitive strategy to respond to
buyer needs.
Cloud data ecosystems redefine the best-of-breed and best-fit engineering versus suite debate. By
resolving disparate and increasingly siloed D&A, they can alleviate some of the challenges of
multicloud, intercloud and hybrid deployments. Unlike with the on-premises stacks of the past, with
cloud ecosystems, the vendor lock-in challenges of using a single vendor may be outweighed by the
potential benefits.
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•
Regional ecosystem pull is accelerated by regional data security laws, which intensify data gravity and
require better regional composability between vendors. This pushes global organizations to consider
migrating and duplicating some or all parts of their D&A stack within specific regions.
Implications:
•
•
•
•
•
Over the past several years, many once-separate D&A software markets have been converging,
driven by increased adoption in the cloud and buyer demands for faster deployments and lower cost.
Greater workflow integration between previously separate products has enabled this trend.
Although there is a need for third-party tools to fill in elements of the D&A ecosystem, this should
diminish over time via innovation in the D&A ecosystem and through acquisitions and cross-category
organic development.
Unlike stack-centric market swings in the past, innovation in the market will thrive, but not just to the
benefit of small, innovative vendors. CSPs might not be the main drivers of innovation; because of
cloud agility and composability, they are fast followers and acquirers of innovation introduced into the
market by smaller, nimble vendors.
Most large organizations will, by design or by default, have to manage a multicloud and multivendor
strategy. However, because it requires additional skills, time and cost to build and manage
applications built from multiple vendor product components, multicloud and intercloud tooling will
evolve to make it far easier to deal with.
Regions not able to create or sustain their own D&A ecosystems will have no choice but to leverage
capabilities created in other regions (hyperscalers) and resort to legislation and regulation to maintain
some level of control and sovereignty.
Recommendations:
Data and analytics leaders should:
•
•
•
•
•
Begin by evaluating the extensibility and broader ecosystem offerings for vendor solutions already in
use and consider aligning with them.
Lower costs and improve organizational resilience by evaluating D&A vendors on the availability,
strength and — importantly — the integration of their broader ecosystem of D&A capabilities.
Evaluate not only extensibility, cost, agility and speed, but also coherence and consistent ease of use
and workflow across the CSP-based D&A ecosystem.
Reevaluate policies favoring a best-of-breed or best-fit strategy for end-to-end D&A capabilities in the
new cloud world by weighing the benefits of a single vendor ecosystem in terms of cost, agility and
speed.
Consider regional composability while assessing the global deployment of D&A solutions by
understanding their technology partnerships and integration with regional D&A ecosystems.
Expansion to the Edge
Analysis by: Ted Friedman, Pieter den Hamer, W. Roy Schulte, Paul DeBeasi
SPA: By 2025, more than 50% of enterprise-critical data will be created and processed outside the data center
or cloud.
Description: D&A activities are increasingly executed in distributed devices, servers or gateways located
outside data centers and public cloud infrastructure, closer to where the data and decisions of interest are
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created and executed. For example, the memory, storage and compute capacities of hardware built into
various types of endpoint devices continue to expand, making larger, more sophisticated workloads
possible. Despite these advances, edge environments bring substantial constraints on resources and flexibility,
and the functionality they offer for D&A workloads is different from that offered by data centers or cloud
environments. This means D&A leaders and their teams must enhance their skills and rebalance their
architectures.
Why Trending:
•
•
•
•
•
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.
A diversity of use cases is driving the interest in edge capabilities for D&A, ranging from supporting
real-time event analytics to enabling autonomous behavior of “things.”
Edge-generated data is growing dramatically in terms of volume and diversity. Since it often won’t be
desirable or possible to collect and process this data centrally, organizations need to support
distributed data processing and persistence models.
Digital business solutions increasingly demand faster data distribution and reduced latency, requiring a
shift of data and data processing away from the cloud and traditional data center environments.
Data sovereignty and solution reliability concerns are generating interest for data to be stored “locally”
in edge environments.
Implications:
•
•
•
•
By placing data, analytic workloads and AI capabilities at optimal points ranging out to endpoint
devices, interesting new applications of analytics and AI are coming into focus for D&A leaders and
their teams, including more real-time use cases.
By using distributed computing resources and spreading the load across the ecosystem, D&A teams
can more broadly scale their capabilities and extend their impact into more areas of the business.
Pushing D&A capabilities toward edge environments can also bring benefits in the form of greater fault
tolerance, remote monitoring, remote operations and autonomous behavior.
With the distribution and complexity of edge environments comes a greater challenge from a D&A
governance perspective — ensuring quality, security, privacy and consistency definitions/models are
all more difficult.
Recommendations:
Data and analytics leaders should:
•
•
•
•
Evolve their strategies and practices to de-emphasize centralized approaches and architectures, while
developing new capabilities that can be deployed, executed and administered in various locations
along the cloud-to-device continuum.
Develop skills and experience in edge environments, including complex solutions and governance.
Communicate and collaborate with stakeholders and teams that have historically been outside the
sphere of influence of traditional D&A, e.g., operational technology (OT) teams.
Provide support for data persistence in edge environments by including edge-resident IT-oriented
technologies (relational and nonrelational database management systems), as well as small-footprint
embedded databases for the storage and processing of data closer to the device edge.
Optimize distributed data architectures for their use cases by balancing the latency requirements
against the need for data consistency (between cloud/data center and edge, as well as across edge
environments).
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•
Use edge topology to address data sovereignty and availability requirements while minimizing risk by
extending D&A governance capabilities to edge environments and providing visibility through active
metadata.
Changes Since Last Year
The main change in this trend compared to last year is the degree of acceleration. Deployment of edge D&A
solutions has continued to grow due to two main forces: the demand for automation and control of remote
environments to mitigate health, safety and resourcing constraints, and the increasingly complex regulatory
landscape emphasizing data sovereignty.
Evidence
1
2021 Gartner CEO Survey: The Year of Rebuilding
The 2020 Gartner Chief Data Officer Survey was conducted to explore the business impact of the CDO role
and/or the office of the CDO. It was conducted online from September through November 2020 among 469
respondents from across the world. Respondents were required to be the highest-level D&A leader, the CDO,
the chief digital officer, or the leader with D&A responsibilities in IT or in a business unit. The survey sample
was gathered from a variety of sources (including LinkedIn), with the greatest number coming from a Gartnercurated list of over 3,450 CDOs and other high-level D&A leaders.
2
Disclaimer: The results of this survey do not represent global findings or the market as a whole. They reflect
the views of the respondents and companies surveyed.
The 2022 Gartner Chief Data Officer Study was conducted to explore and track the business impact of the
CDO role and/or the office of the CDO and the best practices to create a data-driven organization. The
research was conducted online from September through November 2021 among 496 respondents from across
the world. Respondents were required to be the highest level D&A leader in the organization: Chief Data
Officer, Chief Analytics Officer, Chief Digital Officer, or the most senior leader in IT with D&A responsibilities.
The survey sample was gleaned from a variety of sources (including LinkedIn), with the greatest number
coming from a Gartner-curated list of over 4,519 CDOs and other high-level D&A leaders.
3
Disclaimer: The results of this study do not represent global findings or the market as a whole, but reflect the
sentiments of the respondents and companies surveyed.
Gartner’s 2020 Building Digital Platforms Study was conducted to provide guidance on how to build a digital
initiative.
4
The research was conducted online during May and June 2020 among 206 respondents working for
organizations in North America and Western Europe with at least $1 billion in annual revenue. Organizations
were from the manufacturing and natural resources, communications, media, services, retail, banking and
financial services, insurance, healthcare providers, transportation and utilities industries.
Organizations also had to work on digital business initiatives or have plans to do so. “Digital business
initiatives” are defined as efforts involving the Internet of Things, delivery of public APIs, private/B2B APIs or a
combination thereof. Quotas were set to ensure a majority of respondents have a fully implemented digital
business initiative.
21
Respondents were required to have a job title of Director or above, and to be involved in either digital
business, data analytics, IoT or API-based platforms for partners. In respect to digital business initiatives, they
were also required to have a role in either defining technology requirements, investigating or evaluating service
providers, or making final decisions.
Disclaimer: The results of this study do not represent global findings or the market as a whole, but reflect the
sentiment of the respondents and companies surveyed.
The 2021 Gartner’s Reengineering the Decision Survey was conducted online from 24 June to 9 July 2021 to
understand the role of data and analytics in organizational decision making. In total, 132 IT and business
leaders participated, representing a range of regions. Respondents were based in North America (45%),
EMEA (38%) Asia/Pacific (9%) and Latin America (8%). The survey was developed collaboratively by a team
of Gartner analysts and was reviewed, tested and administered by Gartner’s Research Data, Analytics and
Tools team.
5
Disclaimer: The results of this study do not represent global findings or the market as a whole, but reflect the
sentiment of the respondents and companies surveyed.
Note 1: Decision Intelligence
Decision intelligence (DI) is a practical discipline used to improve decision making by explicitly understanding
and engineering how decisions are made and outcomes evaluated, managed and improved by feedback.
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