Dave Albano, President and CEO of RestorePoint.ai, has more than 35 years of technology industry leadership experience.
The ability to put data to work in new and innovative ways can prove transformative. But today’s data and analytics landscape poses major challenges for companies deciding whether to build or buy business intelligence (BI) and artificial intelligence (AI) capabilities. With the volume, variety and velocity of data increasing, complexity continues to intensify.
Today, decisions about how to design and engineer an information technology framework ripples into numerous areas—which impact data and performance in subtle yet important ways.
Tech leaders understand that their role has changed—and there is greater responsibility than ever. It isn’t sufficient to be an expert, specialist or independent operator. Deloitte’s 2023 Global Technology Leadership Study found that executives are marching in the right direction by focusing on strategy, execution, reliability, security, risk and compliance.
However, getting to success is difficult, partly because responsibilities keep expanding but also because core competencies now span multiple disciplines like engineering, data science and business leadership. It’s critical to coordinate ever-more complex components, workflows and processes.
The addition of machine learning (ML) and AI has layered on even more challenges. Building, maintaining, operating and updating a business analytics framework requires substantial time and resources that sap energy from more strategic endeavors.
The Pitfalls Of Building Your Own
While clouds offer self-service analytics and AI, stitching together data pipelines and models requires scarce and expensive talent. Meanwhile, each major cloud provider has a different ecosystem of services that require expertise to implement and integrate. This level of complexity could strain organizations.
Acquiring talent poses another obstacle. Demand for skilled data engineers and data scientists far exceeds supply, forcing companies to offer premium salaries if they can recruit them at all. For many companies, especially mid-sized organizations, building a high-caliber team is cost-prohibitive.
There’s also the constant change in data tech that creates a maintenance burden and technical debt over time. Keeping a custom platform updated with the latest techniques requires ongoing investment and distracts from innovation.
A Simpler Path
Conversely, managed analytics and AI services can handle these complexities behind the scenes. Purpose-built for ease of use and maintenance, they abstract away cloud complexity with consistent tooling across platforms. Skilled teams maintain and optimize performance without hiring challenges.
Buying analytics and AI as a service brings flexibility lacking in rigid, built-from-scratch solutions. This agility speeds up delivering insights against shifting priorities.
Choosing The Alternative
Given the complexities of today’s data landscape, a managed analytics and AI service offers a pragmatic path to accelerate value. For example, the right service can abstract away underlying data and infrastructure complexity so that teams can focus on high-impact initiatives rather than platform building and maintenance. When assessing options, it’s critical to align both business goals and IT modernization plans to ensure the desired outcomes.
Look for a secure, composable framework that reduces the burden of constant skill set upgrades. Some of the core capabilities to consider include:
• A comprehensive data catalog and APIs to discover and consume resources on demand
• A data lakehouse purpose-built for next-gen analytics and machine learning
• Sophisticated data pipelines that securely combine structured, semi-structured and unstructured sources
• Low-code tools, configurable templates and on-demand expert assistance that can adapt to an organization’s evolving needs
• A managed data operations center with advanced technologies that can support agility, flexibility and scalability
Ultimately, business leaders should look for services that align both business and technology transformation. With the right partner, organizations can pivot faster while unlocking more value from their data. The key is extracting insights without getting mired in technical debt across skills, maintenance and integration.
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