In a fast-moving field, established frameworks can often benefit from strategic updates. Think of your cell phone or laptop: do they still function using the same operating system that they had when you first bought them? No, they require regular updates–some more significant than others–that routinely improve their functionality and performance.
These types of updates aren’t limited to software. They can be applied to software platforms, practices, and resources in the pursuit of continued improvement. This is where data modernization has become an increasingly popular part of today’s data-driven initiatives. Teams need to make the most of their data resources, and don’t want to be left in the dust as resources and tools evolve around them. In this article, we’ll discuss how data modernization is helping to carry teams into the future of streamlined and secure data use.
Data modernization is the process of updating and transforming data resources, frameworks, and practices in order to better align with modern demands, technologies, and capabilities. In essence, it’s reassessing and reforming the ways that teams store, process, and interact with data sets in order to stay aligned with the continued evolution of data.
The practice of data modernization has become increasingly necessary as data resources have changed and grown. While legacy systems and practices may have been suited for the data of their time, they often struggle to keep up with newer types and formats. This leads to inefficient and occasionally broken data ecosystems, which are not conducive to deriving quick and actionable insights. Data modernization addresses these issues by moving teams away from siloed, legacy data ecosystems to new frameworks that can operate at scale.
With its end goal defined, how is data modernization actually implemented to improve the modern data stack? Each of the following concepts can be found under the larger umbrella of data modernization:
Cloud migration is the process of moving your organization’s data resources and tools from physical, on-premises storage and compute systems to modern, cloud-based infrastructure. These platforms–like Snowflake, Databricks, Starburst, and more–offer teams a more scalable form of data storage and compute than was available with legacy platforms. The cloud expands the possibilities of data use with enhanced flexibility and cost-effectiveness. While there are cloud migration challenges that may play a role in this process, the benefits are undeniably worth the effort for almost any modern organization.
As data is migrated to cloud ecosystems, teams need to choose how to structure resources in order to keep them organized and accessible at scale. This is where concepts like the data lake, data warehouse, and data mesh become involved. These architectural paradigms help structure data in the most effective manner for your organization’s needs.
A data lake contains large masses of data in their raw and unstructured forms, which might be best for teams with a wide array of data types, users, and use cases. A data warehouse, which cleans and structures data in a more deliberate manner, might be preferred for a team with more stable use cases. And a data mesh can offer a decentralized and self-sufficient model for organizations with various data domains and users. Regardless of which makes the most sense for your organization, each of these architectures offers a dynamic and scalable structure for diverse data use needs.
Data resources are no good if your teams can’t actually use them. In traditional paradigms, data owners or IT teams frequently become bottlenecks to data access. This slows down data users, in turn making business-driving insights fewer and farther between. Data modernization efforts combat this by enabling self-service data access. Whether achieved through dynamic attribute-based access control (ABAC), federated data governance across data mesh architectures, tools like Tableau and Power BI, or other means, making data more accessible to the users who need it is foundational to data modernization efforts.
With the rapid development of new machine learning (ML) and artificial intelligence (AI) tools, it’s important to understand the role they play in modernization. ML and AI tools can automate a range of data analysis operations through the application of predictive analytics models, natural language processing, and more. This not only drives consistent data insights, but can also alleviate the burdens of manual processes from your teams and afford them more time for other important work.
In any effort to update data storage and use, your team needs to ensure that data security is maintained across the modernized ecosystem. While controls may have been in place in the existing data stack, they might not carry over to or operate correctly in the cloud. Teams need to focus on applying consistent data security measures in order to keep modernized ecosystems safe from unnecessary risk. The core factors to keep data secure throughout the modernization process include:
No matter where your data is migrated to, it needs to be kept out of the hands of anyone who does not have the right to see it. By employing consistent governance and data access controls across your cloud data ecosystem, your team can be sure that only those with the proper permissions and purposes are able to access the correct data sets. By conscientiously applying access and governance policies to migrated data resources, you can make sure to act in compliance with the most up-to-date data regulations.
Another way to keep data safe while migrating is to apply consistent privacy-enhancing measures throughout your data ecosystem. These can include data masking, which involves a range of techniques–like k-anonymization, differential privacy, and tokenization–that alter sensitive information to make it unrecognizable to anyone with unauthorized access. Encryption does the same, by scrambling the information in a data set and requiring an access key to return the data to its original form. Each of these proactive measures alters the data in a way that helps prevent reidentification in the event of a leak or breach.
When data resources are spread across clouds, they need dependable oversight. This is why data monitoring is crucial to modernization efforts. As resources are moved from legacy platforms to cloud-based ecosystems, there are plenty of opportunities for a mistake to be made. In the event that data is leaked or a platform is breached, you need to be aware immediately in order to react effectively. Monitoring all user queries and activity can maintain a secure atmosphere as you modernize your data stack. It can also allow for holistic auditing that proves compliance and helps maintain ecosystem safety moving forward.
Given these data security requirements, how should teams approach the application of data security to their modernized data stacks?
By implementing a data security platform like Immuta, any organization can automate and consistently apply the security requirements of the modernization process. Teams can secure data through data access and governance controls that are written in plain language and applied dynamically at query time across the cloud-based or hybrid ecosystem. When data discovery tools are applied to any new data entering the modernized ecosystem, privacy-enhancing technologies like differential privacy and encryption can help mask data against misuse in the event of a leak. And through the integration of data monitoring and detection, teams can manage constant oversight of their data resources before, during, and after migration.
To learn more about the crucial role the Immuta Data Security Platform can play in your data modernization efforts, schedule a demo with one of our experts.
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