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
Data quality is one of the lowest hanging fruits in this field. If the quality of your data is held to high standards right from the moment it is acquired, you’ll have less overhead invested in managing it. People won’t have to sort out problems, and they’ll be able to identify useful sources of data when they look into the data lake.
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
If it’s hard to navigate the system, you’re going to have data management issues. Restrictive policies should be reserved for datasets that deserve that type of treatment due to privacy or compliance concerns. Don’t employ blanket policies that compel users to be in constant contact with admins to get access to mundane datasets.
3. Configure a Robust and Resilient Backup and Recovery System
Nothing could be worse for your data management efforts than watching everything instantly disappear. To keep your data from disappearing into the ether, you need to have a robust solution in place. For example, it would be wise to use local systems for backups while also having automated uploads of files to the cloud.
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.
Just as it’s important to have everything backed up, everything should also be secure. Monitor your networks to determine if systems are being probed. Likewise, set the monitoring software up to send you notifications for things like volume spikes and unusual activity. If an intrusion occurs, you want to be sent a warning that can’t be ignored even at 3 a.m.
5. Know When to Stop Expanding Efforts
Encouraging sprawl is one of the easiest traps you can fall into when it comes to data management. After all, we live in a world where there is just so much data begging to be analyzed. You can’t download it all. If you think something might be interesting for use down the road, try to save it in an idea file that includes things like URLs, licensing concerns, pricing, and details on who owns the data.
6. Think About Why You’re Using Certain Techniques
The best of operations frequently fail to adapt because they see that things are still working well enough. If the thesis for using a particular technique for analysis has changed, you should think about what comes next. Study industry news and feeds from experts to see if you’re missing big developments in the field. Conduct regular reviews to determine if there might be a more efficient or effective way to get the same job done.
Someone someday is going to be looking at a file they’ve never seen before. Without sufficient accompanying documentation, they’re going to wonder what exactly the file is and the purpose behind it. Include the basic thoughts that drove you to acquire each dataset. Remember, the person someday looking at your work and wondering what’s going on with it might be you.