Enterprise Data Platforms: Features, Challenges, and Key Insights

Everything—from managing datasets, metadata, and dashboards to supporting data science, data analytics, and effective decision-making—becomes vastly more complicated for data leaders who operate at the enterprise scale.

As a result, the heart of data management in these organizations, modern enterprise data platforms, must be capable of powering a multitude of apps, platforms, and systems while feeding advanced analytics. They must also serve as the launch pad for machine learning (ML) and AI initiatives, all while enforcing governance, controlling costs, and scaling on demand.

This article breaks down the basics of what that all actually entails, including the features that separate a true enterprise data platform (EDP) from more simple management solutions like data warehouses, common yet critical challenges that can derail even the most detail-oriented data leaders, and key success factors that keep platforms reliable, compliant, and business-ready.

Defining the enterprise data platform

EDPs are comprehensive systems that facilitate enterprise-wide data integration and help teams manage data ecosystems in large organizations. As such, data leaders rely on their EDPs as part of their ongoing efforts to maximize data quality, accurately index and orchestrate data throughout the enterprise, and ensure that it’s properly accessible across business units. 

Modern EDPs typically provide centralized management and governance while supporting distributed data storage and processing capabilities to meet diverse organizational needs. As such, their functionality mirrors the critical and versatile roles they play within modern organizations, which typically includes the following:

• Data ingestion: One of EDPs’ central operations is to collect structured, semi-structured, and unstructured data, as necessary, from any valid data sources.
• Data storage and processing: As platforms, EDPs can handle different data formats and support both batch and real-time data processing, and they often leverage cloud-based computing resources for elasticity. They’re also versatile since they can store data in multiple repositories—either as rows for transactional applications or as columns for analytics.
• Data transformation, modeling, and delivery: Data leaders rely on these platforms to cleanse, transform, and prepare data for analysis, which ensures that said data is ready for data consumers (like business intelligence analysts, data scientists, and departmental leads). In data mature organizations, EDPs can also enable self-service analytics and rich visualization capabilities and integrate directly with APIs and operational systems as necessary.
• Security and data governance: Using their centralized nature, data leaders will also often leverage EDPs to provide role-based access control for users throughout the organization. While it’s not mandatory, doing so can make it easier for data teams to support evolving business needs without the need for major, security-related overhauls. 
• Data observability and orchestration: Finally, enterprise data platforms help data leaders maintain optimal data quality at all times, which enables teams to monitor the organization’s data flows and and pipelines, identify potential issues or errors, and automate workflows to streamline ongoing data operations.

Key benefits of an enterprise data platform in enterprise data management

Correctly implementing, supporting, and leveraging an EDP as the hub of an enterprise data ecosystem provides a wealth of benefits for large organizations. By centralizing visibility and creating a single point of data access, they reduce data silos and duplication issues that so commonly plague large organizations. A well-engineered EDP also encourages the reuse of high-quality data assets and enables faster onboarding of new data sources through existing templates and connectors. 

While many of these benefits are now foundational in large data-driven organizations, EDPs can be foundational in and of themselves since they support the adoption of data mesh and data fabric strategies that further improve data quality, reduce overhead and management costs, and make the implementation of advanced initiatives possible.

This all said, these advantages don’t produce themselves. As data leaders who’ve been operating at the enterprise scale for years know, the same scope that makes an EDP so powerful also creates a unique, ongoing level of complexity. However, understanding the most common challenges that these complexities create gives data leaders an edge in their management—if not mitigation as well.

8 enterprise data platform challenges to watch for

Data leaders in enterprise organizations need to take special care regarding EDP-related issues, as the sheer scale of data management in their organization can magnify the problems they cause. For this reason, keeping a close eye on the most common yet critical challenges can go a long way toward avoiding the need for prohibitively expensive rework, root cause analysis, or irreparable damage to stakeholder trust. 

Here’s a closer look at eight of these challenges:

1. Fragmented governance structures

The scale and distribution of teams across multiple business units, if not geographies, creates unique challenges for data leaders that oversee EDPs. 

For this reason, anything short of clear executive sponsorship of—and defined ownership for—EDP initiatives will contribute to data policy fragmentation and issues with consistent governance. Therefore, leaders who are in charge of EDPs must fight to maintain clear decision rights and accountability frameworks.

2. Conflicting internal strategies

Data leaders must also work to align all EDP initiatives with their organization’s broader business objectives. This can be particularly challenging when priorities between various stakeholders don’t align. 

For those who oversee EDPs, this often manifests as pressure to balance demonstrating short-term tangible wins with the payoffs that come from longer-term strategic goals.

3. Complex data landscape integration

EDP operation must also contend with large, complex operational landscapes—the scattered systems, cloud environments, and multiple physical locations that typically constitute modern enterprise organizations. 

These complex data landscapes bring with them a diverse blend of data sources: structured, semi-structured, and unstructured systems, teams, tools, and technologies that, together, complicate both enterprise data platform architecture and management. Additionally, this operational sprawl naturally works against the single source of truth that EDPs exist to provide and can thus hinder ongoing enterprise-wide data initiatives. 

4. Infrastructure limitations and scalability barriers

In addition to these leadership and landscape challenges, data leaders often must also contend with existing legacy infrastructure and large-scale architectures that make it especially difficult to scale data processing efficiently, which raises scalability challenges. 

Due to these realities, the average EDP must handle numerous analytics and ML workloads, plus data volumes, velocity, and variety that far exceed what traditional platforms are capable of. Additionally, the scale of technical debt that enterprise organizations can accumulate in a relatively short time can pose significant barriers to any proposed modernization efforts. 

5. Complicated data ownership dynamics

The larger the organization, the more domains, functions, and operational areas there will be—all of which rely on access to the vital data that the EDP centralizes and provides. This means that a data platform at this scale must be able to account for the perspectives, interpretations, and understandings that differ between these unique business units. 

6. Multi-jurisdictional compliance complexity

Another challenge that’s unique to EDP oversight involves how many enterprise organizations increasingly operate across multiple regions, if not continents. 

Due to this regulatory reality, EDP operations must be able to navigate a complex web of overlapping and region-specific compliance requirements. At the enterprise scale, this calls for functionality that’s both flexible and robust, enabling data teams to protect sensitive data—especially customer data—based on where they’ve stored it, whether that’s across the hall, the country, or the globe.

7. Next-gen data security threats

Data leaders who oversee enterprise-scale data platforms must contend with elevated risks from both external and internal threats. This is because, compared to their smaller-scale counterparts, EDPs handle exponentially more—and more complex—data volumes.  

As a result, the ongoing challenge of managing security control across highly diverse systems and environments, which now increasingly involve AI tools, presents unique challenges for EDP management. Successful management here requires striking and maintaining a delicate balance between security and accessibility while addressing the emergent risks of shadow AI and uncovered AI adoption within organizations.

8. Resource and skillset constraints

While AI is now driving unique, new challenges for EDP management, some of the most pressing challenges remain quite traditionally human—resource and expertise constraints are a prime example.

While organizational leadership may acknowledge the need for EDPs and related management, this may not directly translate into budget allocations that support the data engineers, architects, and security experts who provide and maintain it. Furthermore, the sheer complexity of enterprise data ecosystems requires much more multidisciplinary expertise. As a result, data leaders must balance resource constraints with the need to hire, train, and manage data professionals who have the necessary skills to support ongoing EDP initiatives. 

Altogether, these common issues can seem like substantial obstacles. But while they are indeed daunting, solid leadership, support, and a management foundation that builds upon strategic thinking can keep them from becoming operational liabilities. 

While not exhaustive, the next section covers key practices that can help data leaders do exactly that.

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Best practices for maintaining enterprise data platform performance

Successfully managing EDPs in large organizations requires the right mindset, not just the right set of tools and technologies. This means data leaders who oversee an EDP must embrace key practices that are specific to platform success at the enterprise scale. 

The following build on and extend beyond general data management practices to do this:

Ongoing alignment with business goals

Arguably, the crux of EDP success hinges on a data leader’s ability to develop a comprehensive data strategy—one that influential business leaders and stakeholders endorse at the highest levels of their organization. By soliciting and maintaining buy-in in this manner, data professionals who oversee an EDP can more effectively align senior leadership in the org with how they’re leveraging data to achieve desired business outcomes. 

To do so in a way that affords both operational transparency and flexibility, leaders should establish a roadmap for their EDP that keeps the scope narrow. It should also feature built-in review processes that occur on a regular basis to ensure that platform management and performance continue to align with evolving business needs.

EDP-focused data architecture leadership

Any data leader who manages and oversees an enterprise data platform must be capable of providing specialized guidance on EDP-specific data architecture. This may directly involve them designing and implementing an initial scalable, secure, and high-performing data platform architecture that’s capable of integrating the multifarious data sources that enterprise organizations rely on, such as data warehouses, data lakes, and operational databases. 

As part of these responsibilities, those who are responsible must also implement data pipelines that are capable of handling both batch and real-time processing across the enterprise’s array of interconnected systems.

Platform-aligned governance and collaboration

Data leaders should also specifically design their data governance frameworks to complement the unique demands they place on EDPs. This includes establishing or validating standards for data quality that account for the diverse data formats and sources that the EDP integrates. Additionally, leaders may need to create enterprise data objects for commonly used data as part of ensuring consistency across the various systems that the EDP connects. 

Furthermore, data leaders must promote specific collaborative practices that extend beyond those they use for more general data management practices. For instance, in addition to securing an executive mandate, many data leaders must also collaborate with senior leadership throughout the organization to ensure that the EDP’s goals synchronize with those across multiple departments and functions. This often requires staying in lockstep with IT while fostering partnerships with key stakeholders in different departments or domains. 

Specialized team development and training

Effective EDP oversight can’t end with relationship building at the leadership level, however. Data leaders must also ensure that they’re assembling and retaining teams that have robust, diverse skill sets and backgrounds. 

But these highly valued data professionals—data engineers, data scientists, governance experts, and others—still need continuous training and development related to EDP-specific technologies and methodologies. As a result, mentorship is a must here, as the demands of enterprise-scale data platforms require nothing short of passion, expertise, and a culture of innovation and excellence. 

Performance monitoring and optimization at scale

Considering their unique and dynamic role as the heart of enterprise data management, EDPs require specialized monitoring approaches to operate at their full potential. In practice, monitoring often requires data teams to implement regular performance checks and proactive enhancements and track specific performance indicators, such as query response times, transaction throughput, and resource utilization across all integrated systems.

Strategic planning for platform growth

More so than when managing data platforms in small- to mid-sized organizations, EDP management requires more extensive, expansive, three-dimensional thinking from data leaders. This means that as the enterprise scales and evolves, leaders must holistically prepare for both vertical growth (adding resources to existing servers) and horizontal scaling (adding to the organization’s total number of servers) as data volumes grow. 

With this additional thinking, however, comes more doing. In other words, data teams must balance the EDP’s need for updated components and technologies with investing in net new features and improvements, all while maintaining compatibility with existing connected systems. 

But this “more is more” approach can’t end at the operational level. Data leaders must also think ahead to where the data work is going. Only then can they ensure that their EDPs will continue to deliver on their full potential in the future—not just in the moment.

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What’s next for enterprise data platform management?

It should be now clear by now that an EDP’s ability to turn the cliched “big data” into actionable insights, elevated customer experiences, and the raw fuel that ML and AI workloads need more than justifies its operational challenges. Even so, the complexities and challenges of day-to-day oversight can’t come at the cost of leadership’s ability to prepare for what’s coming next in the data world.

To explore one leading perspective on where enterprise data platforms need to evolve next, take a moment to dive into The Shift Left Data Manifesto from Chad Sanderson, Gable.ai’s CEO and co-founder. In it, he outlines how his own experiences navigating the challenges of enterprise data management have led him, and others, to some compelling conclusions about where the platforms organizations rely on need to evolve to next.