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Improving access to high-quality data powers successful businesses as they navigate today’s complex data landscape.
Published on: July 14, 2023
The central element of a digital transformation initiative, data modernization is the process of moving data from the legacy systems of a fragmented, siloed infrastructure to an interconnected ecosystem of modern technologies, often in the cloud.
Modernization makes data more accessible, letting organizations maximize value at the scale and speed of today’s business.
Data modernization provides a roadmap for improving enterprise data quality and fostering better, more responsive business decisions.
Enterprises turn to modernization when their legacy infrastructure becomes too inflexible for today’s business demands. Dark data and data swamps consume expensive resources while hindering insight generation. Data modernization strategies help companies improve legacy data quality while applying a first-principles approach to managing data going forward.
Modernization teams can plan what data should make the journey, what to archive, and what to discard. Fresh data governance policies will define consistent rules for managing data in the future. This structured, intentional approach will improve data quality and support better business outcomes.
Old business analysis approaches are too slow and cumbersome for today’s dynamic business environment. Creating delightful customer experiences requires real-time engagement. Companies must identify — and act upon — new business opportunities before the competition can react.
Similar to how DevOps streamlines application development, modern data management practices combine the cloud, automation, and scalability to foster more agile data analysis.
Higher-quality data and more agile analysis improve decision-making throughout the enterprise. Delivering more timely and accurate insights to everyone in the organization, from the C-suite to the frontline, enhances the quality of everyday decision-making.
Data modernization also empowers organizations through advanced data analytics. Cleaner, more complete datasets give data scientists a stronger foundation for their machine learning and artificial intelligence initiatives. Predictive analytics go beyond traditional forecasting to anticipate shifting market trends.
While more agile, effective decision-making creates long-term benefits, modernization offers near-term cost savings. For example, companies can significantly reduce data center costs by migrating data to cloud platforms. Reducing the reliance on data warehouses, eliminating dark data, and clearing data swamps deliver additional savings.
Cloud architectures optimize these savings by decoupling storage from compute and converting both from capital expenses into operational expenses.
Data migration is a general practice ensuring data continuity as companies move data from an old system to a new one. As companies moved more resources to platforms like Microsoft Azure, data migration practices expanded to include cloud migration.
On the other hand, data modernization is a strategic initiative focused on improving the accessibility and productivity of ever-growing data assets. Data migration, especially to the cloud, is a standard component of data modernization strategies.
Data modernization strategically aligns a company’s data management practices with the reality of the modern data landscape. While specifics will vary from use case to use case, these initiatives share seven common elements:
Assessment and planning — Modernization teams must understand how the company collects, stores, accesses, and uses its legacy data. From there, the team can plan the company’s modernization roadmap.
Technology selection — Keeping certain legacy systems may be unavoidable, while concerns over vendor lock-in may require multiple cloud service providers. These and other technology decisions will shape the modernization program.
Data Integration and Migration — How much of your existing data will make the move? How will you transform the data to improve data quality? A clear roadmap will guide how to prepare existing data for the migration.
Data Governance and Security — Placing data in the cloud offers many benefits but makes security more important than ever. Modern architectures require improved governance systems to ensure access, quality, and security.
Analytics and Insights — At first, decision-makers should get the same analyses they always have. New and enhanced analytics will appear over time as modernization’s benefits take effect.
Training and Change Management — Data modernization is as much cultural as it is technical. Executive-led change management programs must reinforce the importance of modernization. At the same time, employees must learn how to make data-driven decisions.
Continuous Improvement — Modernization is never complete given the dynamic data landscape. Technologies and requirements will constantly change, requiring continuous monitoring, review, and improvement.
Data modernization should do more than simply replicate legacy systems in the cloud. Adopting a modern data lake analytics platform like Starburst streamlines cloud migrations and empowers data-driven cultures through speedier access to insights.
Data modernization and cloud migration are not necessarily synonymous. Enterprises may have compelling security or compliance reasons for on-premises modernization. However, the cloud’s benefits for most companies far outweigh the value of in-house data centers.
Infinitely scalable, the cloud responds elastically to rising and falling demand. The cloud is also instantly accessible from anywhere. Modernizing to a cloud-based data architecture delivers performance, productivity, and cost improvements.
Starburst streamlines cloud migration by unifying every legacy system and new cloud platform within a virtual data layer. Through this single point of access, modernization teams can easily assess the company’s existing infrastructure and map datasets to their new destinations. This holistic visibility prevents disruption to data access before, during, and after the cloud migration.
Modernization is a chance to improve data governance and reinforce security practices. In fact, a Deloitte survey found that C-level executives consider security and data protection the top reason for such dramatic change.
These new governance policies must balance the competing demands of security and access. Security rules may protect data, but creating new silos defeats the purpose of modernization.
Starburst’s secure analytics platform helps improve compliance while expanding data access. End-to-end encryption, policy enforcement, and activity logging features secure sensitive data and protected personal information. Meanwhile, Starburst enforces role-based and attribute-based policies with fine-grained controls that ensure authorized users get the data they need.
As the volume and velocity of data grow, the center of gravity in data architectures shifts from on-premises storage to cloud-based platforms. Apache Iceberg and other modern data lakes provide more scalable, open, and cost-effective ways to consolidate enterprise data.
That does not mean data modernization must be monolithic. Modern data is distributed data. Data lakes hold much of it. Some remains at the source, where domain experts can manage databases more effectively. Concurrently, “first class” data continues to find a home in data warehouses that efficiently feed dashboards and other analytics.
With connectors to more than fifty enterprise data platforms, Starburst federates access across all data sources. Data modernization teams can design the best data architecture for their needs without compromising between accessibility, manageability, and cost.
Data modernization lets people analyze data independently without help from the data team. Simultaneously, data engineers spend less time on low-level requests and more on higher-level projects that generate game-changing insights.
However, this cultural transformation can falter in the face of new technologies with steep learning curves. Modernization’s benefits get buried by inertia as old habits remain in place.
Starburst fosters self-service analytics in two ways. First, by providing a single point of access to all data services. Data consumers can explore datasets across the enterprise and discover data faster thanks to deep catalogs and rich metadata.
Second, Starburst lets people use the tools they already know. Rather than learning new technologies, analysts can use their existing business intelligence tools like Tableau. Data scientists, data engineers, and other technical users can develop SQL queries as always.
Extract, transform, and load (ETL) and extract, load, and transform (ELT) pipelines have been essential to legacy data warehouse models. These pipelines were the only way to ingest quality data into legacy data stores. But not without consequences. Data pipelines required careful maintenance to keep pace with changes in data sources, new business requirements, and migrations between data storage platforms.
Data modernization and data lake analytics platforms like Starburst make these legacy pipelines unnecessary. Queries can now pull data from multiple sources at runtime without loading massive datasets into data warehouses.
The scalability and elasticity of modern data lake analytics are more cost-effective. Inflexible compute capacity is replaced by on-demand cloud resources, and data teams no longer need to maintain fragile pipelines.
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