In today’s data landscape, a distributed architecture is driven by the need for real-time insights, compliance, and the scalability provided by cloud computing, with organizations increasingly adopting hybrid models to balance local flexibility with centralized governance.
For years, data management revolved around data warehouses and data lakes – centralized systems promising a single source of truth from a one-size-fits-all solution. As businesses grew more complex and data became more diverse, these approaches showed their limitations. Enter distributed data architecture – a decentralized data management approach encouraging agility and innovation while ensuring data governance and compliance.
How did we get here? The rise of the big data era pushed companies to store and leverage vast amounts of information. Many found that Hadoop’s promise of cost-effective, large-scale analytics fell short. These initiatives often led to poorly governed “data swamps,” where data quality and accessibility issues lurked like hidden predators.
The industry has shifted towards more decentralized data approaches balancing localized agility, centralized governance, and economies of scale. These models let organizations break free from constraining centralized systems to embrace a more flexible approach, enabling real-time insights and responsive decisions across regions and business units.
Why distributed data architecture now?
Distributed data architecture enables business units to innovate independently while adhering to unified governance, striking a balance between agility and control. Organizations need the latitude to innovate at the local level.
Your marketing team, for instance, might want to fine-tune its campaigns by incorporating external demographic data. Your supply chain department may want to optimize logistics with real-time weather data. In these scenarios, waiting for centralized IT approval and time-consuming, expensive implementations stifle innovation and slow time to value.
The rise of cloud computing has turbocharged this shift. Cloud platforms offer tools and infrastructure for supporting distributed data architecture, allowing businesses to spin up instances quickly and scale without the overhead of on-premise infrastructure.
Assessing organizational readiness
Transitioning to a distributed data architecture begins with critically assessing your organization’s readiness as part of your broader data strategy. Assess your organization’s needs, regulatory landscape, and competitive positioning before making changes.
For example, smaller organizations or those with simpler operating models may not require a fully decentralized system. For larger enterprises, particularly those operating in multiple regions with diverse regulatory requirements, the shift may be imperative.
Regardless of size or operating model, this is not an all-or-nothing approach. Most companies will find that a hybrid model is the best fit.
Plan strategically with clear goals
After deciding to adopt a distributed data architecture, organizations should craft a data strategy with considerations for flexibility, performance, alignment, compliance, security, and cost.
For many organizations, digital transformations, cloud migration, intelligent automation, and enabling AI use cases, may provide an impetus for adopting distributed architecture. The requirements of these capabilities and the associated tools and infrastructure are often more conducive to decentralization.
Your strategic planning shouldn’t end with discussing technology. It’s essential to consider the necessary people and processes. Moving to a distributed model often requires reskilling employees and shifting an organization’s culture. This can be challenging, particularly for those accustomed to the control and predictability of centralized systems. With the right approach, though, the benefits usually outweigh the challenges.
The three pillars of strong data governance
As organizations transition to distributed data architectures, data governance becomes increasingly critical. Three key pillars should underpin any strong governance strategy:
- Compliance: Ensure your organization adheres to relevant data regulations (e.g., GDPR, HIPAA, CCPA, etc.) — including managing “right to delete” requests and protecting PII. Compliance requirements such as GDPR can often drive distributed data architectures as data movement restrictions necessitate that data physically remain in a region.
- Enablement: Empower employees to use data effectively by providing the necessary tools, metadata, and guidelines. This includes transparency of information such as data lineage, descriptions, interoperability, and data quality.
- Accountability: Establish clear ownership/stewardship of data domains and ensure issues such as data quality and system outages are promptly addressed. This is particularly important in a decentralized model, where regions or business units may have their own data stewards.
The future of distributed data
Several trends are accelerating the move to distributed data architecture; here are a few:
- Generative AI is moving past the hype and will be key for organizations to respond to fast-changing trends and disruptive capabilities at a business unit or function level, with more direct control of their data.
- As privacy and security regulations evolve and companies grow into new regions and countries, they need the agility and adaptability of a distributed data approach.
- Expanding types and volumes of data will drive the need to manage that data closer to the source. For example, Edge computing’s expansion into new industries and functions is accelerating the shift.
Organizations must understand distributed data architecture to remain competitive. By assessing readiness, planning strategically, and selecting the right approach and technologies, you can position your organization for success in this new era of data management.
For information on how EXL can help your organization with its data strategy, visit our website.
David Crolene, vice president of data, analytics & AI at EXL, a leading data analytics and digital operations and solutions company.
