All companies want to be data-centric, but it can be hard to do it well. There are good practices an organization can adopt to have good data hygiene and ensure that they are utilizing their data well. Karthik Ranganathan, founder & CTO, Yugabyte discusses how companies can improve their data and approach database modernization.
Virtually every organization that people interact with utilizes data in some way as a part of their business model. From the grocery chain you visit every week to the auto company you purchased your car from, enterprises big and small are learning to leverage data as a key part of their business with immense success. But, not all companies understand what â€œgoodâ€ data is and how to ensure that data and the data architecture you build now will remain usable in the future.Â Â
On top of this challenge, technology shifts and higher customer expectations have put a spotlight on organizations that do or do not have â€œgoodâ€ data. Ten years ago, applications typically lived in on-premises data centers with data well controlled and accessed by few. Now, applications are either built-in or are moving to the cloud, with everyone expecting immediate access and visibility. The last two years saw a massive shift to multi-cloud environments, and recent Flexera researchOpens a new window indicates that over 90% of companies have a multi-cloud destiny.Â
Because of this migration to the cloud, applications are becoming distributed. This means that the data is stored across many different locations, putting a demand on the database that did not exist during the days of on-prem.Â
Developers and data architects working in distributed environments require fast development cycles and a flexible database. Modern database architectures, such as distributed SQL, are built to drive fast development cycles and provide data consistency across geographic zones.Â
Understanding What Good Data Is
Through a distributed data layer, developers can automatically replicate and distribute data across multiple servers to accelerate development cycles. But, before you even start building a database or developing new applications, you first must identify and define the â€œgoodâ€ data:Â
- Good data is consistent and allows developers access to the latest information they need immediately without worrying about stale or inaccurate data being returned.Â Â Â
- Good data is accessible to developers and end users from anywhere, all the time. For example, a global enterprise with a remote workforce will have people working across the globe. A distributed workforce requires a global database architecture so employees can access critical data and make data-driven decisions.Â Â
- Good data is protected and can withstand cloud failures. To ensure data is accessible and consistent, the data architecture must be built to deal with cloud outages. Cloud failures are inevitable and outside of our control. Organizations must ensure data is protected, so outages do not impact users and minimize the operational headaches of your organization when they do occur.
- Good data is developer friendly so that they can access and use it to write code without having to deal with the challenges of manual sharding, addressing data conflicts in the code, or tracking data in different places.Â Â
Now that we have defined good data, how do you make your own data â€œgood?â€Â
How To Achieve Good Data
An organization should take the data they currently have and utilize it in a way that benefits the end-user and future developers who will use the data to create innovative applications that will grow the bottom line of the business.Â
Here are four key steps enterprises can take to help make their data â€œgood.â€
- Modernize your Database Architecture. Modernization is a crucial step towards good data. For many, this is a broad term. But we specifically mean enterprises need to migrate data from legacy databases like Oracle, DB2, and Aurora into a system that embraces cloud-native capabilities and naturally distributed data. While this may seem daunting, database modernization tools built on top of popular database architectures and languages ease the burden on internal database architects, making the transition a simple move versus a complete rewrite.Â Â Â
- Shift your mindset â€” data is not an afterthought. Shifting your business approach to one that is data-centric allows an organization to guarantee continuous availability, horizontal scalability, and consistent data. It is important to consider things like data security from the beginning. It can be hard to fix an issue when it has already happened, but if measures are put into place from the start, there will be fewer interruptions.
- Breakdown data silos. Disruption of legacy data architectures and breaking out of data silos are critical. By freeing your data within your organization, you can start to provide end users with more innovative and useful services while simultaneously removing the time-consuming and costly complexities inherent in legacy systems. Many of these were designed and architected before the cloud was even a dream.Â Â
- Future-proof your data. The database is an important step for developers. It can eliminate bottlenecks, modernize processes that allow IT teams to gain a competitive advantage, and enable an end-to-end approach to database management.Â Â
Different database architectures can help create and maintain good data within the enterprise IT environment. However, not all architectures are the same.Â
Because database environments have evolved, enterprises should embrace architectures that enable scaling and resiliency, ensuring consistent, accurate data is always accessible.Â
Distributed SQL is a great option for database architects looking to enjoy the benefits of both SQL and NoSQL databases and cloud-native features like open source and multi-cloud. These advancements in database architecture, along with powerful supporting tools, allow companies to easily move away from legacy systems and embrace a better architecture without disrupting the enterprise.Â
As organizations continue to implement data into their business strategies and become more data-centric, their future success will be based on the availability of â€œgoodâ€ data and the way they approach database modernization.Â Â Â Â