7 Best Practices for Effective Data Management

One of the biggest challenges in data management is focusing on how you can make the most of your existing resources. A common solution tossed out as an answer is to implement best practices. What exactly does it take to turn that suggestion into action, though? Here are 7 of the best practices you can use to achieve more effective data management.

1. Know How to Put Quality First

Quality standards can be enforced in a number of ways. Foremost, data scientists should scrub all inbound data and make sure it’s properly formatted for later use. Secondly, redundant sources should be consolidated. You’ll also want to perform reviews of datasets to ensure quality control is in play at all times.

2. Simplify Access

3. Configure a Robust and Resilient Backup and Recovery System

Right down to the hardware you employ, you should care about resilience, too. If you’re not using RAID arrays on all local machines, include desktops and workstations, start making use of them.

It’s also wise to have versioning software running. This will make sure that all backup files aren’t just there, but that they’ll point you toward what versions of the files they correspond to. You don’t want to be using portions from version 2.5 of a project when you’re working on version 4.1.

4. Security

5. Know When to Stop Expanding Efforts

6. Think About Why You’re Using Certain Techniques

7. Documentation

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Big Data & AI Enthusiast at Inzata Analytics