4 Tips for Optimizing Your Data Infrastructure
Building a data infrastructure is one thing. Building one that is efficient, reliable and cost-effective is another.
What can you do to optimize your data infrastructure and keep it running at peak performance? Keep reading for tips on data infrastructure optimization.
What is data infrastructure?
For the purposes of this article, we’ll define data infrastructure as all of the infrastructure tools and components that help you store, manage, analyze, integrate and back up your data.
That ‘s a somewhat broader definition than you might find elsewhere. Some folks take it to mean just the tools that help you do data analytics, like Hadoop.
But if you want to optimize the way your data infrastructure works, you need to add efficiencies to every tool and component that touches your data. That’s why it’s helpful to take a broad, high-level approach to data infrastructure optimization.
Optimizing data infrastructure: Four best practices
Whether you are building a new data infrastructure from scratch or (as is more likely the case) improving one that is already in place, the following strategies can help you to optimize it.
1. Adopt a flexible tool set
It is rarely the case that a single tool is always the best for managing, integrating and analyzing data. In most cases, you will want to have multiple tools at your disposal – and you’ll want to be able to swap in new tools for old ones as your needs change and as tools evolve.
For these reasons, you should strive to avoid lock-in and adopt tools that are as flexible as your needs. At the same time, you’ll want to ensure that the tools you use can support both legacy data management needs and those that you will face in the future.
Learn how you can create a modern data architecture that includes any data source regardless of the data’s type, format, origin, or location in a manner that’s fast, easy, cost-effective, secure, and future-proof.
2. Educate your employees
In most organizations today, everyone interacts with data in one way or another. By extension, everyone has a role to play in keeping data operations efficient.
If your employees don’t understand data management best practices, now is the time to educate them. Turn all of your employees into citizen data scientists by helping them to understand concepts like data quality and the importance of efficient data integration.
3. Optimize data quality
On that note, let’s talk a little more about data quality.
No matter how well designed your data infrastructure is, it won’t deliver great results unless the data that you feed into it is of high quality. Low-quality data takes longer to integrate, is more difficult to manage and can deliver inaccurate analytics results.
So, don’t settle for low-quality data. Build data quality into every stage of your data operations. For more on this, check our blog post:Importance of Data Quality: How to Explain it To Your Boss
4. Embrace next-generation technology, but remain legacy-friendly
Most organizations fall into one of two camps when it comes to the way they build data infrastructure. They either strive to deploy next-generation technology wherever they can – which leaves them at risk of failing to support legacy data workloads – or they use only legacy technology because they are afraid of upgrading and breaking their existing processes. Neither of these approaches is ideal.
Instead, you want to find the happy medium between next-generation tools and legacy data needs. Take advantage of modern analytics platforms like Hadoop and Spark where you can, but don’t overlook the importance of supporting legacy platforms at the same time. If your next-generation tools cannot work directly with legacy data sources, look for solutions (like those from Precisely) that can help integrate them.
To learn more, read our eBook: How to Build a Modern Data Architecture with Legacy Data