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What Is Data Modernization in 2023?

Data modernization means moving to the cloud and making data more accessible and usable for users across your organization. Find out what else it means in 2023.

Brenna Buuck
By and Posted in Data Integration August 29, 2023

The explosive nature of data today calls for more efficient ways to govern every step of the data lifecycle while ensuring it remains accessible, findable, and usable to harness its maximum value. One such practice employed by most data-driven organizations today is data modernization, which grants them the speed and agility needed to adapt and remain competitive. 

Data modernization often involves the migration of data workloads from legacy, old systems that can no longer serve growing business needs to the cloud. The strong influence of the cloud on data modernization makes most professionals refer to it as cloud data modernization.

Let’s explore data modernization, its effect on data management, some features of modern data infrastructure, and how StreamSets can help facilitate and build your modern data infrastructure.

What Is Modernized Data Management?

Data management refers to the steps, processes, and practices employed to handle and maximize data value as it flows within an organization. The modern approach to data management ensures:

  • Data accessibility by easing data integration through connectors and APIs to move data seamlessly in and out of systems to create a centralized data source for business analysts to make accurate business decisions
  • Safety and security by employing measures like authentication and masking as data flows through the data pipelines to prevent unauthorized access, breaches, and data loss
  • Compliant data operations via defining policies and regulations that provide accountability and safety of data
  • Metadata management to better understand business data and its existing relationships

The Goals of Modern Data Management

However, with the evolution of Big Data, keeping up with the numerous data sources and volumes while ensuring proper management becomes challenging. This limitation calls for a modern approach to data management that emphasizes the need for a flexible data architecture that adapts quickly to accommodate changing business needs.

Modernized data management goes beyond adopting the latest tools and technologies for your data stack but also involves a well-rounded mix of quality assurance, data principles, governance, people, and initiatives to achieve the following:

  • Agile, flexible architecture: Your business objectives may call for a change in how you import data or an increase in ingested data. Modern data management employs automation and dynamic scaling to ensure your architecture is flexible to add resources to accommodate any growing demand.
  • Simplified data integration efforts: The distribution of organizational data across multiple locations creates the need for data consolidation efforts like data integration to collate these data and create a unified, robust data view for operational purposes. Modern data integration tools facilitate cloud data lake and data warehouse integration by employing automation and multiple connectors to create effective pipelines that simplify the ingestion and transformation of this data for storing for analysis, data science, or ML purposes, thereby saving time and increasing the speed of access and productivity of your engineers.
  • Infrastructure cost-savings: Modern data management leverages the cloud, offering numerous opportunities to save costs. For example, using cloud data lakes and warehouses saves you the time and costs of acquiring and setting up storage and backup software.
  • Operational efficiency: Modernized data management uses automation and promotes self-service, which increases productivity for engineers while allowing users to maximize the value of the data.
  • Data democratization: Modern data management aims to improve data value by granting those needing data access instead of a select few through data democratization. Data democratization reduces the occurrence of data silos by empowering more people to access and use data for driving analytics. Self-service is the most significant enabler of data democratization, allowing data users with little to no technical knowledge to harness the power of data. This method frees up time for the data team, allowing them to work on more advanced issues, thereby improving productivity.

The Benefits of Cloud Data Modernization

Most people view the cloud as the biggest enabler of data modernization as the cloud enables organizations to reap the benefits of its agility, flexibility, scalability, and cost-saving offerings for collecting, storing, and harnessing insights for business use. Here are some other reasons organizations are increasingly employing the cloud for their data modernization efforts:

  • Eliminated data integration friction: Data integration utilizes pipelines to collect data from multiple sources to create a single source of truth from which analysts and engineers can use for developing business insights. However, the process involved with collecting, transforming, and processing this data can be slow and result in several disconnected data silos. Cloud data modernization employs automation to construct seamless integration pipelines to collect, transform, and load this data into storage solutions like data lakes or warehouses.
  • Digital innovation: Cloud data modernization promotes ‘fail-fast’ innovative thinking via easy adoption. With the cloud, developers and engineers can provision and set up cloud resources to test new ideas and shut down these resources when no longer in use, thus promoting experimentation and innovative solutions.
  • Improved agility and flexibility: The cloud helps you build a flexible and adaptable architecture that can adjust to the changing business needs and vast data landscape today. For example, the easy scalability offered by the cloud allows you to scale up/down on resources as business needs change.
  • Automated dynamic data pipelines: With the continuous ingestion of data and how data changes, there’s an increased risk of data drift during data ingestion, which can break your pipelines, reducing model and insight accuracy, which can negatively affect business decisions. Cloud modernization allows you to use dynamic pipeline mechanisms and advanced monitoring and alerting tools like data drift to capture and adapt to any data changes without breaking your data workflow.
  • Infrastructure management: Infrastructure management helps manage your distributed cloud infrastructure. With the increasing adoption of multiple services across multiple cloud providers without appropriate management, it’s easy to accumulate cloud costs. Infrastructure management acts as the central control plane for your distributed cloud infrastructure, enabling teams to configure, scale, and retire their cloud services when needed, offering them a competitive advantage.

The Features of a Modern Data Management Infrastructure

Data infrastructure involves the collection of tools and technologies used to collect and deliver data for building data-powered solutions. To enable organizations to benefit from the massive growth and diverse data systems present today, here are some essential features of a modern data management infrastructure:

  • Data-agnostic architecture for multi-cloud data management: Data modernization usually employs a multi-cloud approach by using multiple cloud services from different cloud service providers to benefit from the cloud’s scalability, security, and cost savings. Hence, a proper modern data infrastructure must easily manage various workflows across multiple clouds.
  • Flexible and reusable data pipeline fragments: Most data processes involve repetitive steps like data cleansing, duplication, and other transformation steps, which can otherwise be automated to save time. Your data infrastructure should utilize reusable data fragments that enable engineers and developers to perform data tasks without writing pipelines from scratch.
  • Embedded monitoring and validation in your workflow: With the diverse data types and increasing volumes of data from numerous data sources, ensuring your data infrastructure incorporates monitoring and validation checks throughout your data lifecycle helps make sure your data is safe, secure, and of high quality.
  • Unified management platform for effective data governance: Data fragmentation creates inaccessible and unusable data in silos, causing organizations to use various technologies that cater to data governance, quality, discovery, safety, and security, creating a myriad of fragmented tools that require extensive maintenance. Your modern data management infrastructure should act as a unified tool, consolidating your data discovery, transformation, and other aspects of your data workload, reducing management workload and improving productivity and operational efficiency.
  • Metadata management: Data fragmentation creates isolated data silos that are hard to locate and leverage for business decision-making. Inadequate knowledge of your business data means a longer and more challenging road to analytics, hence the need for metadata management. Metadata management acts as the control plane for your data platform, allowing you to discover data, understand the usage of this data and the relationships present, and the risks and potential value offered by the data.

The Role of Data Governance in Data Modernization

Data modernization goes beyond employing the latest tech stack but involves initiatives, people, and data governance policies. Data governance can empower your data modernization efforts in the following ways:

  • Promotes seamless data integration across source systems like cloud storage services, SaaS applications, and many more by applying governance policies that monitor and guide data flow from end to end
  • Ensures data security in transit and at rest via access control policies on its data pipelines during data integration
  • Creates new, hybrid models of data governance that sit at the middle of the traditional, top-to-bottom governance and decentralized approach, bridging the gap between IT and business needs while equipping every business unit with enough control and the right tools to access, transform, and analyze data.
  • Enables federated computed data governance that offers the right balance of governance control while allowing innovation. This governance approach enables you to implement the decentralized data mesh approach to data management that centrally defines its standard governance policies while granting domains autonomy and accountability for their domain data, enhancing business agility and data value.

Facilitating Data Modernization With StreamSets

StreamSets help facilitate your data modernization efforts for your business needs. For example, its reusable pipeline fragments enable reuse and logic reconstruction, allowing you to build data pipelines without having engineers rewrite everything from scratch, saving them time for other operations and improving work efficiency.

Additionally, StreamSets offers seamless infrastructure and multi-cloud management that lets you build analytics, ML and AI solutions atop numerous cloud providers like GCP, AWS, and Azure without constraints. Even those with no coding experience can build and manage pipelines thanks to StreamSets easy drag-and-drop UI. Furthermore, the built-in alerting and monitoring mechanisms help capture errors as data moves in and out of systems. 

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