Executive Summary: ISG Provider Lens™ - Advanced Analytics and AI Services - U.S. 2024 (Specialist)
10 Jan 2025
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Navigating data complexity and AI integration is crucial for enterprises to achieve real-time insights
The U.S. data analytics and AI market is undergoing transformative changes and a dynamic evolution driven by
technological advancements, regulatory changes and enterprise demands. The market is also experiencing an accelerating need for enterprises to integrate advanced technologies, especially AI, into their business strategies.
The increased emphasis on AI, both as a tool for data science services and a key enabler of business transformation, is reshaping the landscape . Enterprises are grappling with the need to harmonize technical, statistical and business-oriented perspectives to drive more effective datadriven decision-making. This challenge is intensifying as companies recognize the necessity of business-led data science initiatives, prioritizing AI investments that are directly aligned with strategic business goals.
This trend is particularly underscored by the rise of generative AI (GenAI), hailed as the next frontier in addressing complex business challenges by automating previously manualintensive tasks such as content generation, report summarization and predictive analytics. GenAI is seen as a critical next step in AI evolution, offering the potential to automate routine processes, assist with predictive modeling and generate advanced insights at scale.
However, U.S. enterprises continue to face a complex array of challenges as they strive to incorporate data-driven decision-making and integrate AI technologies into their business operations. The landscape is shifting rapidly, with data modernization, AI adoption and advanced analytics capabilities at the forefront of transformation efforts. However, these advancements are not without hurdles. The challenges can largely be divided into two major areas — data foundations and strategy challenges and business context-related challenges. A deep dive into these challenges reveals the intricate barriers that enterprises must overcome to capitalize on data and AI-driven opportunities.
Data-related Challenges
• Data availability and quality: One of the fundamental challenges for enterprises is ensuring the availability of high-quality data. Poor data quality can lead to inaccurate analytics, unreliable insights and misinformed decisions. Data must be accurate, complete, timely and free of errors. However, many organizations still struggle with data silos, incomplete datasets and inconsistencies across different data sources. Inconsistent data entry practices, poor data validation processes and manual data handling further exacerbate quality challenges. Enterprises need robust data
quality frameworks and tools to monitor and improve the integrity of their data assets.
• Data volume and variety: Enterprises are generating vast amounts of data across multiple systems and platforms, leading to volume and variety challenges. With structured, semi-structured and unstructured data coming from a wide range of sources, including IoT devices, social media and transactional systems, integrating and processing this diverse range of data is a significant hurdle. The sheer volume of data also strains legacy systems,
making real-time data processing and decision-making difficult.
• Data observability and governance: With increased regulatory scrutiny and growing concerns about data security and privacy, data observability and governance have become critical components of data strategy. Enterprises need real-time visibility into data flows, data usage and potential issues such as data quality or security risks. Ensuring data is handled per evolving regulatory frameworks such as GDPR and CCPA is essential for avoiding compliance pitfalls.
• Inefficient data sharing and inconsistent data definitions: In many organizations, the ability to share data across departments and functions is hindered by poor data integration capabilities, incompatible systems and inconsistent data definitions. This inefficiency limits the scope of insights drawn from data, creating barriers to effective collaboration and cross-functional decision-making.
• Data storage limitations, data silos and inaccessibility: Data storage limitations pose a significant barrier to enterprises’ ability to scale their data analytics capabilities. Data silos restrict the free flow of data across the organization, preventing comprehensive analysis and insights. Accessing the right data at the right time remains a challenge for many enterprises, especially when data is stored in disparate systems or outdated formats.
• Data stewardship: Effective data stewardship is key to ensuring data is used ethically, securely and in accordance with organizational goals. However, many enterprises face challenges in defining and executing data stewardship
practices, leading to issues such as data misuse, uncoordinated data management efforts and inadequate protection of sensitive information.
Business Context-related Challenges
• Lack of customer intelligence and CX: A common challenge for enterprises is the difficulty in building deep and actionable customer insights. A lack of comprehensive customer intelligence — especially from diverse and unstructured data sources such as social media, customer support tickets or IoT devices — limits an organization’s ability to personalize offerings and enhance CX. This makes it difficult for enterprises to differentiate themselves in a
competitive market.
• Limited scalability and difficulty in real-time analytics: As businesses grow, so do their data volumes. The inability to scale analytics to accommodate this growth, especially in real-time, becomes a major bottleneck. Enterprises need to have the ability to derive actionable insights instantly. However, many organizations face infrastructure and expertise challenges to process real-time and large volumes of data.
• Cloud computing costs, legacy systems and talent shortage: While cloud computing offers significant benefits, enterprises often face the challenge of rising costs related to cloud storage and compute power, especially when scaling up their analytics efforts. Legacy systems, particularly those reliant on on-premises infrastructure, further complicate cloud migration, creating friction in efforts to modernize IT environments. Talent shortage, particularly in AI, data science and cloud computing, prevents organizations from fully capitalizing on their data potential.
• Security vulnerabilities, ethical and privacy issues: As data-driven technologies such as AI continue to evolve, the risks associated with data security, privacy and ethics also increase. Data breaches, misuse of data and violations of privacy regulations can have devastating financial and reputational impacts. AI models can inadvertently perpetuate biases or violate ethical principles, further complicating governance and risk management.
Technology Trends Gaining Traction in 2024 and 2025
Service providers are increasingly adopting and developing innovative solutions to meet the growing demands of enterprises in data management, ML and AI deployment. By offering solutions, accelerators and tools that address challenges such as automation, scalability, security and real-time decisionmaking, these service providers play a crucial role in enabling organizations to harness the full potential of their data and AI investments. As new trends such as DataOps, MLOps, IoT, Edge AI and adversarial ML continue to evolve, service providers must stay agile and innovative to support enterprises in navigating these complex and dynamic landscapes.
Below is an in-depth analysis of several key service provider trends and provider offerings, highlighting how they reshape the market.
• DataOps: DataOps seeks to bring the same agility, automation and collaboration to data engineering that DevOps brings to software development. This approach focuses on reducing bottlenecks, improving data quality and enabling faster access to trusted data, thereby ensuring data is always ready for analysis and AI model training.
• MLOps: MLOps, like DataOps, focuses on streamlining the development, deployment and maintenance of ML models at scale. The MLOps trend is driven by the growing need for organizations to manage the complexity of ML lifecycle management and ensure that models can be delivered quickly, accurately and at scale.
• AnalyticsOps: AnalyticsOps is a specialized practice that applies DevOps principles to the management of analytics workflows and focuses on the orchestration of analytics workflows, from data preparation to generating business insights. AnalyticsOps will help streamline the process of analyzing data and delivering insights at speed and scale, ensuring analytics teams can collaborate more effectively and reduce the time it takes to generate actionable insights.
• TinyML: This trend is driven by the proliferation of IoT devices, where running sophisticated ML models on edge devices with limited computational power can drastically improve real-time decision-making without relying on cloud infrastructure.
• Automated Machine Learning (AutoML): AutoML aims to simplify the process of developing m; models by automating tasks such as feature selection, model training and hyperparameter tuning. By making ML more accessible, AutoML enables organizations to deploy models without extensive data science or ML expertise.
• Small data: Small data refers to manageable and structured datasets that are often more domain-specific than the massive volumes associated with big data. Small data analytics focuses on deriving insights from small and high-quality datasets that can still offer significant value for specific business applications.
• IoT and Edge AI: The convergence of IoT and Edge AI is a key trend driven by the need for real-time decision-making in industries such as manufacturing, healthcare, automotive and logistics. IoT devices generate vast amounts of real-time data. By processing this data locally on edge devices using AI models, organizations can make immediate decisions without sending data to the cloud.
Enterprise Trends and Developments in 2024 and 2025
As enterprises increasingly recognize the power of data to drive business transformation, several key trends are shaping the data and AI landscape. These trends reflect the evolving needs of businesses, rapid adoption of advanced technologies, shift toward more sophisticated data management and analytics models and reimagination of how
organizations manage, analyze and derive value from their data.
• Democratization of data: Data democratization makes data accessible to a broad range of users within an organization, empowering non-technical stakeholders to analyze and use data without any specialized skills. Traditionally, data analysis has been confined to data scientists, analysts and IT teams. However, the rise of self-service analytics platforms, low-code/no-code tools and user-friendly dashboards enables data access to business users across all levels. These platforms often incorporate NLP capabilities, allowing business users to interact with data using simple queries, enhancing the accessibility of insights.
• AI-powered insights via augmented analytics: Augmented analytics uses AI and ML to enhance traditional analytics processes, automatically identifying insights, trends and patterns in data without requiring manual intervention. By combining data discovery, predictive analytics and NLP, augmented analytics helps organizations derive actionable insights faster and more effectively.
• Continued shift toward embedded analytics: This trend is gaining traction as organizations seek to make data-driven insights an inherent part of everyday business processes rather than separate, standalone tasks. By embedding analytics into applications, employees can make informed decisions within the context of their workflows without switching between disparate systems or tools.
• Data mesh architecture: Data mesh’s architectural approach to decentralized data management shifts away from
traditional monolithic data lakes and warehouses toward focusing on treating data as a product, with ownership
distributed across various domains within an organization. Each domain is responsible for managing its data pipeline, including data quality, access and governance.
• Operational data warehouse: Operational data warehouse (ODW) integrates operational and analytical data to support real-time decision-making across business operations. As more organizations migrate to the cloud, the cloud-based ODW model is gaining popularity due to its ability to enable real-time analytics, improve operational efficiency and reduce the complexity of maintaining on-premises data infrastructure.
• Real-time data warehousing and automation: Real-time data warehousing enables enterprises to process and
analyze data as it is generated, providing timely insights that can drive immediate decision-making. The combination of real-time data processing and automation allows organizations to streamline data workflows, reduce latency, and react faster to business needs.
Data warehouse as a service: Data warehouse as a service (DWaaS) is an emerging cloud-based model where
organizations can rent a fully managed data warehouse instead of building and maintaining their infrastructure. DWaaS eliminates the need for upfront capital investment in hardware and reduces the operational overhead of managing data storage and processing. This allows businesses to scale their data operations quickly and cost- effectively.
• Metadata-driven architecture: Metadatadriven architecture (MDA) refers to the use of metadata to manage, organize and optimize data processes across an organization. MDA enables better data discovery, lineage tracking and governance by providing a comprehensive view of data flows, transformations and relationships. This trend is increasingly important in the era of big data and AI as it helps organizations understand their data’s context, quality and security.
• Convergence of data lakes and data warehouses: The convergence of data lakes and data warehouses is a growing trend in the data management space. Integrating these two models into a unified platform that can handle both structured and unstructured data enables enterprises to gain a more holistic view of their data assets.
The trends outlined above highlight the increasing sophistication of the data and AI landscape as organizations continue to embrace cloud technologies, AI-powered analytics and more flexible data architectures. These trends point to a future where data is more accessible, actionable and integrated into business operations, enabling organizations to unlock deeper insights and drive significant business value.
To stay ahead of the evolving needs of enterprises, service providers in the data analytics and AI market are enhancing their portfolios with cutting-edge solutions that focus on integrating AI with data science and BI capabilities. Enterprises recognizing the need for a solid data strategy and robust data foundations to fully leverage AI technologies have driven a significant shift toward data modernization. This realization is driving investments in data integration, cloud data platforms and modernization tools that can facilitate the seamless integration of AI-driven
solutions. Service providers will continue to play a crucial role in helping enterprises navigate these trends by offering relevant tools, platforms and the expertise needed to manage, analyze and secure data in this rapidly evolving environment.
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