Envisioning and Cultivating an Effective Data and Analytics Architecture at Data Summit 2024

Critical to any business is the architecture which supports present and ongoing business success. However, cultivating a great architecture is more easily said than done, as such infrastructures harbor a variety of interdependencies—from individual roles and skills that span a multitude of business areas—that require acute nuance.

Constructing a practical road map with the agility to scale and the strength to make significant business impact is key toward achieving an effective data and analytics architecture.

John O’Brien, principal advisor and industry analyst at Radiant Advisors, offered his expertise at the annual Data Summit’s pre-conference workshop, “Enterprise Data & Analytics Architecture Road Maps That Scale,” exemplifying how to leverage your organization's business drivers to define and execute a road map that powers advanced analytic capabilities.

The annual Data Summit conference returned to Boston, May 8-9, 2024, with pre-conference workshops on May 7.

“A roadmap is basically a plan; it’s just breaking down your architecture in such a way that it’s executable,” explained O’Brien.

The key in O’Brien’s words is “breaking down,” as many enterprises become instantly overwhelmed with the extensive interdependencies associated with modernization. “Going to the cloud” is not as simple as many businesses would like, taking on too much due to embarking on something too broad results in a lack of actionability and scalability in the long run.

The launching point for crafting an enterprise data and analytics architecture roadmap is aligning with business strategy, O’Brien emphasized. Ensuring that you simultaneously understand and deliver on business goals is a modernization necessity. O’Brien broke it down into four steps:

  1. Identify the business outcomes to achieve.
  2. Translate these outcomes to data and analytics capabilities.
  3. Prioritize the cloud roadmap for analytics.
  4. Plan to implement technologies that create an optimal ecosystem in the cloud.

O’Brien further suggested that adopting principles for modern data architecture, integration, and cloud native themes, as well as continuously optimizing the modern analytics lifecycle for enterprise scalability, is crucial.

Complementing this business goal-centric approach is cultivating a data-driven culture. This culture should empower employees to work with data and analytics in a self-service decision-making model. To enable this, setting analytics as a strategic priority, as well as adopting intuitive tools—such as modern data visualization and data and report discovery—will encourage using data to invite greater business value.

O’Brien examined business capability frameworks that aid in establishing an effective enterprise data and analytics architecture. By pointing to a greater data need that requires resolution, enterprises can better understand what the goals of each capability should be. O’Brien offered the following examples of business capability needs:

  • Simplify finding data, “What data do we have?”
  • Simplify data access, “How do I get data access?”
  • Data governance and security, “Am I using the data correctly?”
  • Business self-service data, “I can do it quicker myself.”
  • Value of near real-time data “This data available is too old…”

Thinking of your architecture as a puzzle—where the picture on the box is your reference architecture—creates a holistic, high-level vision architecture that serves to guide transformation. Each puzzle piece, then, is a business analytics delivery project, and a best practice is to start on the edges and work your way in, according to O’Brien.

Furthermore, enterprises must embrace that delivery may work as a learning experience rather than an immediate success. Real data and scenarios will help surface architecture patterns and decisions; making a data and analytics project work first, then improving based on the things discovered in the process, is key.

Amid delivery as learning, O’Brien emphasized that you should begin with pain relief, and start small. Direct business benefit helps demonstrate ROI while allowing room to grow as pain points are addressed in increments. Adopting a “Minimum Viable Projects,” or MVP, mindset establishes end-to-end delivery of a reusable architecture pattern, where solving current challenges with a lean, cost-conservative approach then scaling is an effective way to go.

O’Brien also explained that resource planning and managing expectations plays a crucial role in establishing a comprehensive data and analytics architecture roadmap. Ultimately, there are three ways to work with the business better:

  1. Think like a business stakeholder—share in business stakeholder goals and frustrations as a trusted partner.
  2. Balance architecture with delivery—enterprise data architecture is a journey of delivering business analytics projects and learning.
  3. Establish an agile architecture mindset—data architecture evolves through lean, iterative, refactoring projects from the edges and delivery.

Many Data Summit 2024 presentations are available for review at