Executive Summary: ISG Provider Lens™ Analytics - Services - U.S. 2021
To download the report for all quadrants, click the PDF on the right
The individual quadrant reports are available at:
- ISG Provider Lens™ Analytics - Services - Data Engineering Services - U.S. 2021
- ISG Provider Lens™ Analytics - Services - Data Lifecycle Management Services - U.S. 2021
- ISG Provider Lens™ Analytics - Services - Data Science Services - U.S. 2021
Data as Blood Line
Despite the ongoing effects of the COVID-19 pandemic, there has been some restitution for organizations in the form of investments for digital transformation while keeping theeconomy and employees upbeat. Under the broader umbrella of digital transformation, data analytics has been gaining significant traction across the board, including at the department and line-of-business levels.
One of the key changes in the mindsets of enterprises and services providers is that they combinedly agree on the need for data becoming more available across departments to create and share value. Taking reference from Clive Humby’s “Data is the new oil” to Ed Parkes’ “Data as blood,” this change in how organizations perceive the value of data is gaining paramount importance. Organizations are understanding how data should flow throughout the organization instead of staying static, giving rise to new terms such as orchestration, federation and monetization as well as designations such as citizen data analysts. Enterprises are realizing the need for non-technical employees to contribute to analytics initiatives as data arrives from a variety of sources. These functional and domain experts can make more sense of data than technical analysts. Service providers are responding with innovations and functionalities to address these requirements withhyper-automation, cloud, AI and machine learning.
There are multiple factors driving investments in the analytics market, and innovations are being dictated by personas. These play at two different levels, technical innovations and operational innovations, and with two different reasons. The first reason being the lack of skilled resources and the need to ease workloads, and the second is the need for every resource to utilize data and derive insights.
Technical innovations are aimed at skilled data analysts. These innovations include highly intelligent platforms, convergence of DataOps, MLOps and DevOps, multi-cloud data integration, powerful data fabrics and others.
Operational innovations are for non-technical users. Some examples are low-code/no-code environments, drag-and-drop, point-and-click and self-service functionality,conversational AI and natural language programming/querying.
At the same time, many enterprises are rushing into the data-centric fray without a sound strategy or roadmap. While some of them have a strategy in place, they still facechallenges on where to start, which data to consider or how will it impact business outcomes. The pandemic has not only accelerated the analytics transformation but has allowed enterprises and service providers to prioritize investments and innovations to support this journey.
ISG has identified specific categories that can generate a significant market demand. Two platform categories, self-service BI and data preparation and integration platforms, have started gaining attention. Enterprises are actively investing in these platforms to actively scale their analytics projects with a focus on data quality, data democratization and monetization.
The pandemic has accelerated the need for more investments and moving AI projects from the pilot phase to scaling them across the organization. Although scaling remains a challenge for many business leaders, service providers are addressing this need through the usage of accelerators, tools, workflows and structured frameworks. They are relying on playbooks, functional expertise and domain experience to reduce complexity and cost while improving the accuracy of AI models. The market witnessed several trends based on the changing buying behavior of enterprises and the response of service providers to address these changes. Some of the key trends that ISG identified are presented below.
New verticals investing in analytics: Healthcare and life sciences, retail, consumer goods and manufacturing firms have increased their investments in analytics solutions and services. Several service providers reported increases in the number of projects across these verticals, especially for data science and data engineering services. While there were some large-scale projects such as genome sequencing in light of the pandemic, most projects were focused on supply chains, sales, forecasting for goods, commodity pricing, predictive maintenance, production yield maximization, sales recommendation engine, segmentation engine and others. These investments are driving both the topline and bottom-line impact, making these enterprises operationally effective with data-driven insights.
Solutioning-led approach: Several service providers have started to invest in a solutioning approach in developing platforms and tools to support their clients effectively. They are offering pre-built accelerators, in-a-box solutions or service-as-a-software (as in the case of LTI). Providers such as Cognizant with BigDecisions® and Capgemini with AI Glassbox are leveraging these solutions and platforms as a value addition to enhance the customer journey, and LTI Mosaic (data integration) and Leni (self-service BI) are competing directly with platform vendors. While these tools do not have extensive functionalities compared to pure-play vendors, they are strategized as consumer-grade solutions for existing clients to ensure that all personas are part of the data-centric journey.
AI and data democratization: As enterprises are aiming to become data centric, they also realize the challenges associated with the lack of relevant technical skills tosuccessfully leverage AI systems, pushing the need for citizen scientists to pursue these opportunities. While industry pundits describe this trend as a double-edged sword, the possibilities of accessibility and affordability seem to overweigh concerns about bias and other serious errors. Data democratization is also a challenge, requiring strongergovernance models to protect privacy and against cybersecurity threats.
Data science trends:
- Responsible AI and trusted AI are becoming increasingly prevalent for data science and advanced analytics projects. The ability of an AI/machine learning (ML) model to accommodate risks, bias and discrimination is paramount, as several pilot projects are being scaled across an enterprise. At the same time, potential issues that might arise during deployment should be considered. Providers are investing to create trustworthy frameworks, relying on their years of experience while showcasing thought leadership with these models. These frameworks are expected to become standardized offerings from leading providers and help position them better in the competition.
- Service providers are increasingly industrializing AI while offering data science platforms, accelerators and data science-as-a-service models instead of traditional services-led projects. This is prevalent among small and midsized firms with business objectives that are typical to peers in their own verticals and industries. These may
- include sales prospect identification, product placement, price and sales forecasting and others. Providers are using standardized playbooks and reference models todeploy platforms and accelerators to fast-track these projects. As an extension,providers are also offering data science-as-a-service models to lower the cost of projects with standardized offerings.
- Data science projects are more often including data from computer vision, video, audio and emotion analytics to identify and leverage behavior, hyper-automate,hyper-personalize and deliver enhanced user experience and business outcomes. The increased use of data from IoT, social media and open source data from weather, traffic and agriculture are driving these analyses.
Data engineering trends:
- The convergence of DataOps and MLOps has pushed the boundaries of data engineering with these frameworks to improve data quality, analytics insights and AI and machine learning models. DataOps provides a standardized approach to deliver the highest quality levels across sources, right from identifying the data source, processing it for analytics readiness to the analysis itself. The combination with MLOps makes the process faster and easier with less manual intervention.
- The market is experiencing a shift to multi-cloud and hybrid cloud environments across enterprises. Service providers are extending partnerships with cloud providers, cloud data integration vendors and hyperscalers to customize their portfolios and suit their offerings to match market needs. While connectors and accelerators will gain momentum, service providers should showcase a clear roadmap to successfully transition and migrate their customer environments to grow in this environment.
- Data sources are increasingly expanding to accommodate edge and IoT-based products, and service providers are targeting these enterprises by industrializing their offerings with a verticalized approach. Dedicated centers of excellence and thought leadership to exemplify their capabilities in these data sources are perceived as strong differentiators in the manufacturing, automotive, shipping, logistics and similar industries. Service providers are showcasing supply chain and other related capabilities to ensure that compliance and data quality requirements are met.
Data lifecycle management trends:
- Service providers are increasingly viewing data management offerings as part of data engineering due to the functionalities and capabilities becoming overarching and holistic. Although there are clear distinctions to some aspects of data management, data engineering has taken on increased coverage of data quality, storage and compliance measures to a certain extent. Several data management functionalities are increasingly merged with data engineering, especially with DataOps becoming a prevalent trend.
- Service providers are making investments for developing numerous proprietary tools and accelerators to help with complex master data management (MDM) and data quality at organizations. These tools are aimed at providing better governance and control as well as enhanced security controls to protect data assets.
- Service providers are renewing their focus on compliance, governance and regulatory practices, especially as ethical AI and data privacy are becoming concerns for enterprises and business leaders. ISG expects providers to rely on automated compliance and regulatory technology (RegTech) solutions to better help with data management.
Access to the full report requires a subscription to ISG Research. Please contact us for subscription inquiries.