Data Ecosystems to Support All Your Data Workloads

The data and analytics landscape consists of rapidly changing technologies and markets that D&A leaders must navigate to support D&A priorities. This means systems thinking and enterprisewide, value-driven architecture, design and engineering approaches are necessary to develop AI-ready D&A capabilities — all without overlapping and unnecessary investments. And the approach must consider existing technologies, processes and skills to grow the present state into the future.  

The goal is to leverage existing infrastructure into converged D&A ecosystems designed to deploy the D&A platform cohesively through seamless integrations — and position the organization to scale and optimize. The D&A operating model must work to overcome gaps in the data ecosystem, architectures, delivery approaches and team skills.

While D&A has been a top enterprise priority for years, most D&A leaders have little to show for their investment and effort. Now, however, the recent explosion of AI and related technologies puts D&A as a top priority for many organizations. 

To fully realize measurable business impact, organizations must exploit AI-ready D&A technologies, people and processes. While digital businesses rely on data and analytics, many treat D&A and AI as supportive and secondary to business initiatives — rather than as strategic, critical drivers of success.

D&A architectures today are under stress due to a high degree of data distribution and diversity. Traditional data platforms no longer meet the demands of business leaders and customers who want continuous innovation and agility. 

The new environment requires new solutions, architectures and operating models. And market trends are clearly moving toward the cloud. Indeed, by 2025, cloud-native platforms will be the foundation for more than 95% of new digital initiatives — up from less than 40% in 2021. 

In this environment, D&A leaders are considering the challenges associated with moving their data ecosystems to the cloud. And yet almost every factor that makes the cloud attractive — like rapid provisioning or consumption-based pricing — also contributes to potential cost overruns. This means that the total multiyear budget impact of a cloud migration journey will be significant and must be navigated methodically.

2. Balance cloud experimentation and engineering efforts. A comprehensive understanding of cloud services is critical before migrating your workloads to the cloud. This means setting up a pilot program that includes experimentation, testing and validation as phases of your migration plan.

3. Outline your cloud financial governance model. Cloud migration involves a major switch in the budget and spend process — from operating a capital expenditure model with multiyear ROI to an operating expenditure model for cloud data management services. To avoid unpleasant spend and budget surprises, you must adapt robust financial governance practices early in the cloud journey.

4. Complement ops-readiness with data observability. To gain a multidimensional view of your data, you need a data observability solution that includes performance, quality, usage and impact on downstream applications.

5. Evaluate data ecosystems as a possible end state. Consider your data ecosystem in its desired end state and approach the data migration journey through the lens of a cost-benefit analysis.

6. Infuse automation and AI and strategize how to minimize human effort, reduce delays and manage operational costs.

7. Prepare your multicloud and intercloud strategy. While sometimes the result of a merger or acquisition, many enterprises adopt a multicloud strategy to avoid vendor lock-in or to attain different functional advantages from different providers. In any case, monitoring usage and managing costs are complicated in multicloud architectures, and your goal should be to avoid egress charges and remain compliant on data sovereignty regulations.