How to Source Specialized Data & Analytics Services Better


Heightened economic uncertainty and the associated risks of growth are making business leaders cautious. They must modernize and integrate advanced techniques and tools at the same time as they leverage existing investments. This includes their investments in data and infrastructure.

This requires aligning technology investments with the business strategy. Here are three ways to get started:

1. Unlock value from current investments in data and analytics.

Inadequate use of investments in data, digital technologies and AI can substantially reduce ROI. To navigate this, enterprises are augmenting and integrating digital capabilities in business processes across IT and OT . Leveraging advanced AI and analytics models to achieve operational excellence helps companies extract value, optimize costs and enhance customer experiences.

Steps for maximizing existing investments include:

  • Aim for enterprise-wide agility through a data-first approach to modernization
  • Upgrade data and computing infrastructure to provide for large-scale computing to spearhead business decision-making
  • Reorientate data, technology, business and domain expertise to expedite data engineering and process improvements that translate into improved ROI
  • Take an enterprise-grade, data-as-a-product approach by leveraging data to develop new revenue streams and transform CX
  • Orchestrate data estates for improved access, security, governance and computing to swiftly process data and generate real-time insights
  • Aggregate and integrate BI investments to realize and unlock data value across tools and ecosystems

2. Prioritize innovative investments to explore new avenues of growth.

The evolution of advanced analytics and generative AI (GenAI) is encouraging enterprises to invest in AI, data and digital technologies to optimize business processes. The result is data-driven business insights, deep data synthesis and analytics that can improve self-service and enhanced experiences for customers and end users.

To improve business decision-making, organizations need to overcome inconsistencies in data processing and analysis. This has many enterprises investing in data mesh as part of their shift toward Data as a Product with a focus on decentralized architecture, better user access to data, domain-oriented data ownership and federated governance.

3. Use data responsibly.

Enterprises must commit to upholding the highest standards of data trust, security, ethics, responsibility, compliance and governance. A strict focus on data compliance regulations and cybersecurity will eliminate risks and losses. The need to be compliant with regulations, industry standards and internal policies means companies must emphasize data accuracy and protect sensitive data with robust data quality management that can navigate associated complexities and potential risks.

Specialized Capabilities of Service Providers

Chief Data Officers, Chief Digital Officers and CIOs are seeking specialized data-handling competencies from IT service providers as part of their business. Over the years, these providers have invested heavily in analytics and AI capabilities to help their clients with informed decision-making at scale.

The following are some of the specialized capabilities service providers offer:

  • Scaled analytics solutions that align with enterprise objectives: Service providers are leveraging their expertise in data management, infrastructure and industry-specific domains  to revamp the way they help enterprises manage their data. This has led to scaled investments in GenAI, ML, NLP and other advanced technologies for the development of use cases, solutions and platforms. Service providers are also investing in innovation labs and CoEs and are establishing partnerships with hyperscalers, data analytics and cloud vendors to co-develop solutions that offer actionable real-time insights for agile decision-making.
  • A “data first” approach for reinventing operating models: Service providers are using a data-first approach structured to architect and reinvent operating models for self-learning that are suitable for highly dynamic environments. They are using customer-centric design, process automation, process redesign, advanced AI, ML and analytics to build cost-efficient and scalable operating models with next-generation architectures.
  • Embedded data synthesis for real-time data-led business decisions: Recent investments by service providers in complex event processing, LLMs, GenAI, ML, NLP and other advanced capabilities reflect a strong commitment to helping enterprises synthesize and analyze data with existing data models so they can avoid delayed reporting and intelligence.
  • Industrialized solutions: To provide ready-made solutions to their clients, service providers are developing highly industrialized and verticalized solutions across the data and analytics ecosystem. Over the years, they have invested in and developed IP assets such as accelerators, frameworks and platforms to rapidly address domain-specific and industry-specific challenges and meet the requirements of enterprises.
  • Modern data architecture and management for federated governance: Service providers are helping their clients decentralize their data to overcome the limitations of data silos. Their approaches allow individual domain teams to access and manage their data through decentralized data architectures (such as data mesh), while also allowing other teams to discover, access and use the data. This can lead to the democratization and monetization of data, unlocking new revenue streams for enterprises. Providers are also incorporating a modern data fabric architecture to encompass data of any scale or origin and provide unified, consistent UX and real-time access to data.
  • Ways to build trust, security and responsibility: Service providers are addressing the dynamic changes in data regulations, such as GDPR, CPPR and ESG, and are positioning themselves to handle diverse data sets and help their clients manage security and governance. They are using AI- and ML-enabled compliance and governance tools, frameworks and solutions to help enterprises automate compliance management. They are also helping enforce responsible and ethical AI standards and develop data governance, data stewardship and client data protection programs by anticipating the critical needs of enterprises.

The recently published ISG Provider Lens™ Analytics Services - 2023 reports for the U.S. and Europe explore these enterprise challenges and showcase providers and vendors that are actively addressing them. The reports also highlight the specific capabilities of these players that can help enterprises choose the right partner to sustain and grow in the current economic environment.


About the authors

Gowtham Sampath

Gowtham Sampath

Gowtham Sampath is a Manager with ISG Research focusing on emerging technologies and their impact on businesses. He is also responsible for authoring Provider Lens quadrant reports for Banking Industry Services and Analytics Solutions & Services market. Gowtham’s responsibility includes authoring ongoing research articles and blogs on the data analytics market covering a broad spectrum of verticals and across functional domains. In his role, he also works with advisors in addressing enterprise clients' requests for ad-hoc research requirements within the IT services sector, across industries. 
Saravanan M S

Saravanan M S

Saravanan M S is a Research Specialist at ISG.