“A place for everything and everything in its place.”
In today’s world, where digital data is part and parcel of every application, a process like spring cleaning our data is equally as important as what we do for our households and office premises. Organizing and getting rid of data one does not need is an essential practice that every organization should follow. Also, we may find some valuable things during our cleaning process. You might get some great insights from hidden or old data. In this blog, we will discuss ways to keep data in your databases and dashboards clean and reasons to do so.
Why cleaning matters
How does your attic look? Full of clutter and untouched for a long time? It’s the same for data that sits in physical devices or somewhere in cloud, in folders or databases untouched for years. Data regulation laws like GDPR apply not only to future data, but also to data you’ve stored from the past, too. So, it becomes important to make sure you cleanse any data you shouldn’t retain. If you don’t clean unwanted data, it will also result in unwanted costs incurred for backup and replication.
Dirty data eats your time and energy
Auditing existing databases, especially ones used by marketing and sales teams, is very important. For example, it’s not a good idea to keep old information for opportunities, as it might be expired or invalid. Those people might now be in a different company with different email addresses. As a result, a marketing team’s efforts and time will be drained by using that information to try to find a person who might not be there to target. Leaving such data undated and not updated will exhaust waste the time of marketing and sales campaigns.
Debate the best solution
While cleaning out your home, often one spouse will come across an object and say to the other, “Darling. We haven’t used this for a long time. I don’t know why we bought it in the first place. Shall we trash it?” The response from the spouse? Often not agreement! It’s the same in a company while cleaning up old data stores. Before deleting a database or table, or a file, a conversation needs to be held by a lead with his or her engineers. They’ll need to ask why something is implemented if it hasn’t been used for a long time. Or else, why it was not created or maintained properly, and whether it’s still needed. But after all, the resulting debates end in a good thing. Either the team changes to an improved version of the database or file, or they clean up the stored one.
7 tips to avoid data clutter
- Descriptions of data sets should be clear even for a layman to understand.
- Verify email addresses regularly, not just once during sign-up. For example, tracking marketing email engagement will help you identify and purge frozen leads and spend more time on hot leads.
- A retention period for your data should be determined from the beginning and tracked with a reminder.
- Reduce having nullable columns as much as possible in your database schema. This will make sure that a proper table is prepared at the initial stage itself.
- Make sure naming conventions and best practices like database normalization in storage are strictly followed to the end-level data engineer.
- If you’re talking about either a mobile phone or a database, the following would be best to find some extra space:
- Find duplicate copies and remove whichever copy is not required.
- Try cleaning out large-sized items (files/tables) you feel are not important anymore.
- If you see that the schema changes frequently, investigate choosing document databases like Mongo DB or Azure Cosmos DB. But do it with clear documentation about each field.
Finally, repeat this mantra periodically:
“A place for everything and everything in its place.”
Spring clean in dashboards
Dashboards, reports, and extracted data sources in dashboards and reporting tools should be cleaned out because:
- Over time, some dashboards and reports may get archived, i.e. not used at all. Cleaning up or organizing such dashboards and reports will tell you whether you might be missing one or more dashboards that could be helping you make certain decisions with more insight.
- Some dashboards with sensitive information might have been left lying around after their purpose was served.
- Extracted data sources, if scheduled to automatically refresh and left unnoticed, will use an application’s resource call and storage. So, it’s good to stop unwanted refreshes or delete the data sources once their purpose is over.
There are many data cleansing tools that could automate the process, but there will be some extra benefit in case of manual data cleansing. Also, it’s good to have some benchmarks to ensure data cleanliness. Cleaning data isn’t a one-time process. Put it on your schedule to do the same on regular cycles.
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