Five steps to data modernization success
To move your business to the top of your industry, you need to deliver superior customer experiences, create new revenue streams and make fast, strategic decisions. And today’s modern data capabilities make this possible — so you can deliver business insights that drive real innovation, speed and flexibility.
But few companies have been able to make the foundational shifts necessary, to achieve such outcomes. According to a report by McKinsey Digital, even though 70% of financial institutions have had a modern data architecture roadmap in place for 18-24 months, almost half still have disparate data models. And most have integrated only a quarter of their critical data into their target architecture.
To achieve data modernization success, you need to get the right data, to the right application, at the right business moment, while delivering a new level of business insights. If you’re relying on disparate data models and outdated data architectures, you’re setting yourself up for data quality issues — which ultimately adds complexity and cost and holds you back from achieving your target outcomes.
But by taking five key steps, you can deliver — and continually optimize — your modern data architecture. This forms the foundation for innovation, superior customer experiences, business insights, real-time agility and the ability to make the most of modern cloud native applications.
Step 1: Data discovery
Start by getting your team on board. Focus on understanding the high-level data issues and goals, and brainstorm ideas about how to drive a better data architecture that satisfies business and application demands now and in the future.
Step 2: Data architecture assessment
Understand your current data architecture in depth, including what data is available and what is needed by business processes and applications. Identify use cases to focus on, as well as their high-level architecture. Define the strategy and roadmap.
Step 3: Data architecture and engineering
This is where the strategy and roadmap are implemented. Design the overall architecture — including which clouds, technologies and platforms to be used. Develop pipelines to ingest, transform and store data. Build out data lakes, marts and platforms as needed to implement the modern data architecture. Work with cloud native application development and operations teams.
Step 4: Business intelligence and reporting
Now it’s time to turn your data into actionable results. Define your data models, build dashboards with real-time refreshes and automate reporting.
Step 5: DataOps
Set up a virtuous cycle of continuous improvement and innovation. This involves an orchestration of people, processes and technology to deliver trusted, high-quality data. With a DataOps approach, you can create predictable delivery and change management of data, data models and related artifacts. This includes:
- Data lifecycle process framework - Incorporates DevOps, agile development and statistical process controls.
- Collaborative data management practice - Improves communication, integration and automation.
- Cloud platform and tooling - Orchestrates and automates security, quality and metadata.
Expert guidance for your data modernization journey
With Rackspace Data Modernization, you can build intelligent applications and experiences, drive targeted real-time action, and make predictive, data-driven decisions that accelerate innovation and increase ROI.
Our data specialists will help you harness the power of a modern data architecture and AI — so you can monetize your data by uncovering new use cases for increasing business efficiencies and customer engagement with up to 70% reduction in implementation time and cost. The Rackspace Data Modernization solution spans the five key steps described above, meeting you wherever you are on your data modernization journey.
And when you’re ready for process maturity and agile delivery, combined with world-class managed support focusing on data lifecycle management, turn to Rackspace DataOps. Rackspace DataOps is delivered in two parts: Rackspace Elastic Engineering for Data complemented with Rackspace Data Pipeline Management.
Rackspace Elastic Engineering for Data
Our experts work closely with your business across one joint, agile team to collaboratively accelerate data initiatives, while passing on collective knowledge gained from multiple Rackspace Technology clients to your internal team.
Rackspace Elastic Engineering for Data provides access to a truly innovative managed service that enables data lifecycle management to help increase operational efficiency and accelerate innovation.
- “Do with” approach: Our Elastic Engineering Pod works in an agile, sprint-based model right alongside your team
- Consistent team: No matter which tier of hours you use each month, you’ll always work with the same pod that knows you, your environment and your business.
- Flexible, tiered pricing: Purchase ‘fractional access’ to your pod via straightforward hours-based tiers and scale up and down monthly should your business needs change
- Multi-faceted skill sets: The Rackspace Elastic Engineering Pod for Data consists of an engagement manager, architects and engineers working together as one unit.
- Ongoing innovation: Rackspace Technology leverages leading cloud platforms tools and frameworks to deliver progressive improvement, enablement and transformation.
Rackspace Data Pipeline Management
With Data Pipeline Management for Rackspace DataOps, you can focus on outcomes and innovation, and maybe getting some sleep at night. Our automated pipeline monitoring system is supported with reactive 24x7 response by our global Rackspace DataOps team. In addition to monitoring, alerting and incident management, you’ll receive troubleshooting, platform escalations, runbook-based resolution, and operational support.
Get started today by signing up for a complimentary Data & Analytics Strategy Session. You’ll discover how a modern data platform can accelerate your path to becoming a truly data-driven organization.
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