David Pidsley

Data and analytics leaders are challenged with how to optimize business value from their data and analytics investments. This Excel-based tool helps leaders build and document data and analytics initiatives’ value stories and assumptions, and then quantify their benefits in an NPV and ROI model.

Setting and justifying budgets for D&A staffing and strategic investments is a top challenge for organizations. D&A leaders can use this research to benchmark their current spending and optimize funding allocation, improve operational efficiency, enhance decision making and drive business success.

D&A leaders must adopt delivery models that are best suited to their business partners’ needs. Project management is suitable for tasks with clear, time-bound deliverables. This research highlights how to adopt a product-centric approach to successfully execute D&A strategy and operating model.

The acceleration of generative AI has heightened the need for chief data and analytics officers to reinvent themselves and their function. To succeed, CDAOs must champion their role in AI, find new AI funding models and accelerate AI-ready data, data governance, and D&A and AI literacy.

Data and analytics leaders often focus on strategy as a deliverable, rather than strategy as a process of continuous improvement. This self-assessment enables you to identify strengths and opportunities to build a comprehensive strategy and planning process.

Data contracts are becoming an increasingly popular way to manage, deliver and govern data products between data producers and data consumers. Before adopting data contracts, data and analytics leaders must understand what data contracts are to implement them effectively.

Benchmarking empowers chief data and analytics officers (CDAOs) to compare their spending, staffing and organizational design with peers. CDAOs should identify performance improvements, justify investments, optimize resources and shape their strategy and operating model against these peer benchmarks.

D&A product management speeds up the agile delivery of trusted, reusable data at scale. This guide for D&A leaders helps launch data products that align with business goals by understanding user needs and feedback, defining ownership and governance practices, and managing the product life cycle.

D&A operating models should be intrinsically linked to the strategies that help deliver quantifiable business value. This enables D&A leaders to exploit the data, analytics and AI ecosystems through their architectures and scope the work needed to achieve business outcomes.

One of the most promising GenAI use cases is task augmentation in the data analysis life cycle, including insight generation and high-level goal fulfillment. This note helps the head of analytics and BI prepare for this technology, which could revolutionize enterprise decision-making capabilities.

AI-powered technology and generative AI have revolutionized the D&A landscape, necessitating robust governance. Inaction risks both D&A and organizational success. This analysis highlights how data and analytics leaders can prioritize and respond to top trends to maximize value and mitigate risks.

Generative AI models have become a commodity, but organizations are still struggling to scale their adoptions for analytics and AI. Data and analytics leaders should adopt emerging AI engineering practices, focusing across data, model and application to amplify the business value.

Designing a data-driven organization requires heads of EA’s enterprisewide view and D&A teams’ data and technology expertise. This research helps heads of EA collaborate with D&A to design a robust future-state architecture that makes the data-driven organization a reality.

The broad adoption of digital workplace applications provides more workstyles data. Digital workplace leaders can use this insight to get grounded in the functionality of prepackaged technology solutions for personal and team effectiveness, application and device proficiency and process performance.

Organizations are struggling to prove the business value of AI and keep pace with its rapid advancement. A product-centric delivery mindset and approach can help AI leaders to better manage AI initiatives, emphasizing outcome over output and quick adaptation to technological changes.

Data, analytics and AI initiatives must create concrete and measurable impact on business outcomes. This research helps data and analytics leaders to create a resilient, business-focused strategy to enable tangible business outcomes.

Heads of analytics and BI struggle to deliver initiatives when they fail to manage relationships across the data and analytics ecosystem. Use this research to understand communities and leverage their existence, influence and value to analytics initiatives while mitigating associated risks.

Facing a plethora of vendor options and selection criteria, buyers of analytics and business intelligence platforms struggle to overcome information overload. The head of analytics and BI can use this curated research as a starting point to examine and compare vendors in the ABI platform market.

Finding the optimal blend of humans and AI in decision making becomes more difficult as analytics capabilities mature but suspicion of AI increases. Data and analytics leaders can use this framework to identify the ideal level of collaboration for human-AI decision making.

Data and analytics strategy is a top priority for D&A leaders who are adapting their operating models to generative AI. This Toolkit provides best-practice, peer-sourced examples of D&A strategy and operating model elements that can be used to validate with the C-suite and stakeholders.

Open and modular analytics architecture makes it easier to build analytics capabilities into applications. Data and analytics leaders should adopt the concept of composable analytics along with generative AI to bridge the gap between insights and actions.

In an important but often overlooked result of prescriptive analytics, users experience the output as a recommendation within a business application or process, while the analytics themselves are invisible. Data and analytics leaders should prepare for data visualizations becoming less important.

Decision intelligence platforms combine explicit decision modeling, AI, analytics and related capabilities to support, augment or automate decision making, driving business outcomes. Data and analytics leaders can use this guide for investing in DIPs to make their organization more decision-centric.

Microsoft Fabric is a unified data and analytics platform that will impact both current and future client deployments on the Azure public cloud infrastructure and platform services. This research provides data and analytics technical professionals with clarity on the Microsoft Fabric offering.

As more decisions become automated or augmented, a proportion of analytics will fade into the background, reducing the emphasis on visual analytics and threatening the focus of ABI platforms. Product managers need to prepare for this inevitability by embracing AI to connect decisions to actions.

Data and analytics leaders should provide optimal analytics experience to multiple personas for collaborative decision making. This research will help guide their choices of vendor solutions in the analytics and business intelligence space.

Executive leaders in global companies struggle to raise AI literacy in their organizations, limiting their ability to identify use cases and build meaningful applications. This research explains how to use AI hackathons to fast-track an organization’s AI literacy and increase its skills.

This Hype Cycle will help data and analytics leaders evaluate the maturity of a wide range of analytics and BI innovations. Key trends include the further improvement of augmented capabilities driven by generative AI, and the need to navigate new levels of technological and process complexity.

Data and analytics leaders use ABI platforms to support the needs of IT, analysts, consumers and data scientists. While integration with cloud ecosystems and business applications is a crucial selection requirement, buyers also need platforms to support governance, interoperability and AI.

Government CIOs are expected to incorporate AI into their capabilities quickly and at scale. This must be accurate and safe. The document explores the trend of AI for decision intelligence and provides recommendations for CIOs to improve decision outcomes and build a data-driven organization.

Metrics stores provide governance of the business metrics to establish a single source of trust. Data and analytics leaders should pilot a metrics store to enable consistent business language across lines of business.

Data and analytics leaders face challenges aligning technology and organizational requirements to drive strategy. Taking action on the top trends listed here can help their organizations anticipate change, make critical decisions and enhance business efficiency.

Developing and evaluating business use cases to drive the adoption of GenAI is a high priority for D&A leaders. This research details approaches to GenAI use case evaluation informed by interviews with 20 organizations and highlights the experiences of two companies that use distinctive approaches.

A supply chain command center design fuses complex data, multistructured analytics and AI to improve decision making, performance and outcomes. Supply chain technology leaders should reference this architectural framework for successful command center initiatives.

Resistance to change in data and analytics is a top-three impediment to success. D&A leaders struggle with addressing the challenges of power politics to address that impediment. These seven practical and provocative hacks can move their initiatives forward and get them adopted enterprisewide.

Many organizations struggle with how to migrate or consolidate their existing analytics dashboards from their legacy analytics and BI platforms to new platforms. Data and analytics leaders should use this research to streamline the migration process with a resilient and value-added approach.

Data and analytics leaders increasingly focus on delivering data products to stakeholders, but overlook the product management discipline as a critical delivery model. Leaders must understand how D&A product management may evolve their operating model to deliver on the promise of data products.

Chief data and analytics officers can use this toolkit to explore and document the implications of business-unit-specific data on key business outcomes by formalizing a discovery process between business, D&A and IT teams.

Analytics and AI continue to collide, with repercussions in multiple domains. To leverage emerging opportunities and mitigate unknown risks, data and analytics leaders must address the impact of artificial intelligence analytics and data science ecosystems, user behavior, roles, and decisions.

Staying ahead in banking involves more than data analytics — it takes understanding and influencing human behavior. To dominate the fast-paced financial market, banking CIOs should facilitate the collaboration of data, analytics and behavioral science in order to create customer-focused solutions.

Securing funding and budgeting for data and analytics programs is imperative for chief data and analytics officers. CDAOs must explain their D&A funding model changes as a value realignment and discuss their plan to tighten their relationship with their organization’s CFO.

It is imperative that chief data and analytics officers secure funding and budgets for data and analytics programs. Use the model definitions, pros and cons, and action plan described herein to partner with sponsors, agree on funding and better D&A operations for strategic value.

The only constant is change. It can take many forms: cultural, technical or economic. Chief data and analytics officers often confront an environment that is volatile, complex and ambiguous. To be successful, they must act as organizational champions, value drivers, change agents and innovators.

This Emerging Tech Impact Radar for data and analytics selects the most important technologies and trends toward which providers should focus their efforts. Product leaders should address four themes — weaving the data fabric, composable D&A, generative decisions and actions, and accelerating D&A.

Successful CDAOs frequently adopt product orientation to create a direct connection between data, insights and business value. To exploit data product value at scale, products must be thoughtfully designed, alongside the necessary product management and execution competencies.

Generative AI has transformed augmented analytics capabilities to make insights more accessible and democratized for users by allowing automated insights, code and data. Data and analytics leaders should use this research to navigate the market and scale analytics adoption.

Data and analytics leaders struggle to leverage generative AI in their analytics and business intelligence platforms and data ecosystems. These slides provide architecture guidance on design patterns to implement generative AI alongside or within augmented ABI platforms.

Facing a plethora of vendor options and selection criteria, buyers of analytics and business intelligence platforms struggle to overcome information overload. Data and analytics leaders should use this curated research as a starting point to examine and compare vendors in the ABI platform market.

Not all D&A work can be delivered as a product, and projects will always exist. CDAOs must approach D&A product management strategically. Use a product-oriented approach when business needs are vague or not well-scoped. Use a project-oriented approach for tasks with clear, timebound deliverables.

Data and analytics leaders must define responsibilities and develop a discipline for D&A product management in their teams. Use this RACI matrix to benchmark current activities, plan for skills gaps, and effectively organize a transition to product management practices for data and analytics teams.

The analytics and decision intelligence (A&DI) platform market is evolving, but it remains highly fragmented. Supply chain technology leaders can use this research to navigate market conditions and build a cohesive technology roadmap to adopt supply-chain-specific A&DI platform capabilities.

With the latest advances in generative AI and ChatGPT, AI is recasting the world landscape and changing how we live and work. Executive leaders must treat AI innovation as a societal trend and not one that impacts solely technology and business. — By Gartner Futures Lab

Citizen data scientists complement expert data scientists in meeting growing demand for machine learning and advanced analytics. Data and analytics leaders must empower citizens across the organization to accelerate time to value, scale decision automation and deliver sustainable business outcomes.

Data and analytics leaders should provide optimal analytics experience to multiple personas for collaborative decision making. This research will help guide their choices of vendor solutions in the analytics and BI space.

Buyers of insight engines are faced with an overwhelming number of vendor options, selection criteria and information overload. Data and analytics leaders should use this curated research to examine and compare vendors in the insight engine market as a starting point.

Data and analytics leaders must use analytics and BI platforms to support the needs of IT, analysts, consumers and data scientists. While integration with cloud ecosystems and business applications is a key selection requirement, buyers also need platforms to support openness and interoperability.

The broad adoption of digital workplace applications provides more workstyles data. Digital workplace leaders can use this insight to get grounded in the functionality of prepackaged technology solutions for personal and team effectiveness, application and device proficiency and process performance.

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.

The introduction of ChatGPT (and competitors as they emerge) is a market inflection point for data and analytics. Data and analytics leaders should use this FAQ to assess the short-term to midterm implications for D&A capabilities; talent and skills; risks, trust and governance; and the D&A market.

Data and analytics leaders need to add value to business units while navigating inflation pressures and recession threats. D&A leaders must support cost optimization initiatives to drive business recovery, renewing D&A’s role in business restructuring and innovation.

The only long-term prediction that currently makes sense is that there really is no predictability. CIOs should assume disruption at a higher or equal frequency and scale. CIOs should introduce chaos engineering, appreciate short-term solutions and build a futurist function. By Gartner Futures Lab.

The purpose of data and analytics is first and foremost creating a concrete and measurable impact on business outcomes. D&A leaders should create a modern D&A strategy consisting of parallel workstreams to ensure maximum business value.

Data and analytics operating models must be intrinsically linked with the strategies that drive them. Data and analytics leaders must therefore upgrade their strategy and operating model to ensure it can enable dynamic business outcomes amid disruptive market conditions.

Data and analytics has a broader enterprise purpose and is more widely integrated into the ways we work than ever before. Data and analytics leaders must therefore recalibrate their data and analytics strategy to be more deeply connected to business strategy, value and outcomes.

Data and analytics has a broader enterprise purpose and is more widely integrated into the ways we work than ever before. This presentation will help data and analytics leaders build an operating model that defines the enterprise actions needed to execute their strategy and generate business value.

“How do I create a data and analytics strategy?” and “What does a good operating model look like?” are popular questions from Gartner clients. Chief data and analytics officers can learn from best-practice examples that work, to move their own organizations’ strategies from “good” to “great.”

Analytics, BI, data science and ML continue to collide, driving ABI and DSML platform consolidation. To foster personalized consumption of insights and data stories, data and analytics leaders must pivot to a composable platform featuring autonomous analytics, metadata, governance and data quality.

Insight engines deliver information in support of digital workplaces and data in support of analytics and automation. This Critical Capabilities report will help IT leaders choose the most suitable offering from a range of vendors.

Insight engines combine search with composite AI to enable context-enriched analysis, as well as the delivery of actionable information, derived from all types of content and data within and without an organization. This Magic Quadrant profiles 15 vendors to help IT leaders make the best choice.

A lack of context undermines decision making, especially for the most complex of decisions. Data and analytics leaders should harness multistructured analytics and a wider variety of data sources, structures and formats to bridge this gap in decision support.

The ability to represent a full analytics process in code helps organizations scale their management of analytics applications to derive more business value. ABI product managers must augment analytics content life cycle management to code-based representations that can be managed in an agile fashion.

The headless software pattern has reached ABI platforms. Buyers demand back-end-only components decoupled from a front-end presentation layer so they can develop once and deploy many times to downstream systems. ABI product managers must prioritize capabilities for scalable and composable analytics.

Capabilities that automate data preparation, insight generation and multipersona collaboration put analytics and business intelligence and data science and machine learning platforms on a collision course. Data and analytics leaders must democratize decision making by adopting augmented analytics.

A lack of engaged audiences stalls growth in analytics maturity and capabilities. Data and analytics leaders must build communities where influencers, common in social networking platforms, can be cultivated as a critical step to drive data and analytics adoption.

Metrics stores provide governance of the business metrics to establish a single source of trust. Data and analytics leaders should pilot a metrics store to enable consistent business language across lines of business.

Collecting different perspectives on data to establish a cohesive understanding is critical for complex decision making. Data and analytics leaders must introduce collaboration capabilities to achieve analytics democratization.

Data and analytics leaders must scope work and delegate responsibilities for self-service analytics (SSA). Use this RACI (responsible, accountable, consulted, informed) matrix to maximize the adoption of analytics and business intelligence, benchmark current SSA and define a target state.

Data and analytics leaders can use the RACI (responsible, accountable, consulted, and informed) matrix to build their D&A governance office, benchmark their current governance activities and plan for a target state. This matrix can be customized per need and discipline size.

Data and analytics (D&A) leaders must effectively scope work and delegate responsibilities for efficiently running projects, teams or disciplines. This RACI (responsible, accountable, consulted and informed) chart can be used to define team roles, activities and responsibilities for critical goals.

Data and analytics leaders can use this Tool to compare vendors in the Magic Quadrant for Analytics and Business Intelligence Platforms. Vendors can be compared head-to-head in terms of the critical capabilities for various analytics and business intelligence use cases.

This Toolkit can be used to support a number of efforts in developing your D&A strategy, as well as operationalizing your D&A governance and MDM programs. Use this Toolkit to help facilitate a discovery process between business, D&A and IT teams to learn how data in specific business systems has material and diagnostic impacts on key business outcomes prioritized by CDAOs.

Data and analytics leaders should begin provisioning capabilities that support analytic consumers as well as authors. This research will help guide their choices of vendor solutions in the analytics and BI space.

Organizations are looking for more collaborative, governed and easily embedded augmented analytics in their business processes. Data and analytics leaders should consider the Cool Vendors in this research to close the gap between analytics insights and business actions.

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.

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.

Data and analytics leaders can use this NPV and ROI tool as part of their broader data and analytics value management approach to optimizing the business value of their data and analytics portfolio of initiatives and value streams.

Analytics, BI, data science and machine learning market convergence has accelerated, resulting in new ecosystems with expanded capabilities. Data and analytics leaders must factor the convergence into their strategy or face further fracturing of tools, people and processes.

Citizen data scientists complement expert data scientists in meeting growing demand for machine learning and advanced analytics. Data and analytics leaders must empower citizens across the organization to scale decision automation, accelerate time to market and deliver sustainable business outcomes.

Data and analytics leaders responsible for enabling product orientation should use this research to accelerate their transition from projects and services to D&A product management.

Self-service analytics tools have been pervasive for years, but organizations struggle to achieve effective analytics at scale. To scale analytics, data and analytics leaders should balance self-service analytics and composable analytics applications.

The addition of audiovisual capabilities to automated data stories will increase the reach, transmission speed and comprehension of data. Data and analytics leaders must adopt these capabilities to gain the attention of decision makers and to improve how their organizations make data-driven decisions.

Innovation management techniques are always evolving and maturing year over year. Executive leaders should study this Hype Cycle to understand the trends in techniques and approaches that can help maximize the value and impact of their innovation programs across the organization.

Augmented capabilities are differentiating features in platforms across D&A. They’re also a key factor accelerating the convergence between analytics and data science. Data and analytics leaders must enable analysts and consumers with these capabilities to enhance data-driven decision making.

Organizations can achieve broader analytics capabilities in a self-service manner. To achieve more productive self-service analytics and data science, data and analytics leaders should assess these Cool Vendors to build synergies among automation, augmentation, contextualization and composability.

The emergence of xP&A heralds a transformation of financial and operational planning by providing a single platform with the integration and support needed for finance and business teams to collaborate. Application leaders must assess the merits of xP&A for integrated planning across organizations.

The rising role of context and situational awareness for delivering insights, and AI’s growing role in evaluating implied and explicit data connections have pulled graph technologies to the spotlight. This report showcases different graph technologies and approaches with representative Cool Vendors.

Using popular topics that are fun and entertaining like sports is an effective way for data and analytics leaders to increase data literacy, communicate the value of analytics and drive adoption. Widespread data literacy is becoming even more important as we enter the consumer era of analytics.

Data and analytics leaders should respond to COVID-19-related budgetary challenges by demonstrating the value of D&A to business units. They must support collaborative cost optimization initiatives to drive business recovery, and renew their role in business restructuring and innovation.