9 Useful Data Management Tips for Your Company or Project

by Carson
data management tips

Whether you are working for a company or working on a school project, you will need to deal with vast amounts of data sooner or later. Therefore, we are going to explain 9 practical data management best practices in this article.

Table of Contents

  1. Back up your data consistently
  2. Prioritize user privacy
  3. Create and rehearse data recovery procedures
  4. Keep your systems secure from data theft
  5. Reject and remove any invalid entries
  6. Making the right data easily accessible
  7. Add metadata whenever necessary
  8. Visualize data
  9. Use the proper data analysis techniques

1. Back Up Your Data Consistently

We would like to start this article by emphasizing the importance of backups. If you don’t have an up-to-date backup, your data will be lost if something undesirable like disasters, fires, or cyberattacks happen. In that case, you will have to go through the tedious process of recreating your data, which can take days, months, or years to complete depending on the size of the database. It could cause your company to lose a significant amount of money or cause your school project to be well overdue while it would otherwise be on time.

As a result, it is essential that you periodically store archives of the data you have acquired, which are called backups. For basic users, it’s great if you can use the 3-2-1 method. The number 3 stands for three copies, the number 2 stands for two on-site copies, and the number 1 stands for one local copy. This means keeping one copy on the cloud, one copy on an external hard drive, and of course, one local copy on your computer or server. For companies with more hardware, it’s advisable to use multiple external drives to store different copies of data taken at different dates. That way, if one of the copies contains malware, the clean copies can replace it and get your system working again.

A diagram demonstrating the 3-2-1 method for backups
Image created using Canva

2. Prioritize User Privacy

It’s also crucial not to be too invasive to users when collecting data. Imagine answering a question that will embarrass you regardless of what you answered. It’s the same thing for your customers or respondents when websites or forms collect too much data from users.

Therefore, collect only what you need from users. Do not collect personal information that is not helpful to the company or your research project. Also, if you have a website, you’d probably choose to obtain user information automatically. Thus, you should write a privacy policy that mentions what data is being collected, why they are being acquired, what the company will sell to what third parties, and how the company will protect their information from theft. The privacy policy should be readily accessible on your website’s homepage.

3. Create and Rehearse Data Recovery Procedures

The fact that you have a backup does not mean you can access it anytime you need it. Therefore, it’s a great idea to create a data recovery guideline consisting of if-else statements on what to do in every possible situation without losing your data. Moreover, it’s also important to rehearse the data recovery procedure once you have created it. That way, your colleagues and teammates can recover the information more quickly, and you can edit the procedure if it’s found to be nonfunctional for specific circumstances or creates bottlenecks that slow down the progress of data recovery.

4. Keep Your Systems Secure from Data Theft

Dealing with data loss every day is not convenient and can cause severe performance issues, so it’s crucial to keep your systems robust and prevent these undesirable events from happening in the first place. Firstly, we are going to explain how to prevent your systems from being subject to data theft. The sensitive data must be encrypted using secure algorithms, and users must be authenticated before accessing their information.

Other than that, access to data among the company must also be restricted. Only people who have to use the information must be given access to the data. Besides, you should also use various measures to prevent malware from damaging or stealing sensitive data and execute data recovery procedures once malware has been detected on your systems. Network security must also be ensured to prevent outsiders from eavesdropping and accessing your data.

5. Reject and Remove Any Invalid Entries

If you encounter an error in the middle of using a program to analyze data, consider removing the problematic entry if it isn’t the first one. Something odd among the database means that your data may contain invalid entries that you need to remove or replace with data of the correct format.

To prevent these annoying errors and manual removals from occurring, you should reject any invalid entries from the start. For instance, if the user is filling out a form, the form should return a message and reject the submission if one of the inputs does not meet a regular expression. Furthermore, make sure your website can successfully defend itself from SQL injection and cross-site scripting attacks as well. They can be extremely perilous and can cause unauthorized manipulations inside your database.

6. Making the Right Data Easily Accessible

On the contrary to the fact that you must protect your data, users should also be able to access some statistics from the database to ensure transparency. This can help build customer trust and help more people use your company’s services. Moreover, in research projects, you should include all your data related to the purpose of your survey in your report. Otherwise, teachers and readers will not have a great experience reading the essay.

7. Add Metadata Whenever Necessary

Do you want to have to search through data manually or through a complicated program that takes days or weeks to write, just to find one single entry? If not, you should consider adding descriptive metadata when dealing with a chunk of raw data. Metadata is information that describes other data or files and can be incredibly useful in identifying files and information from a database. Remember to name your file appropriately, too, as filenames provide another practical method to search for data.

8. Visualize Data

This one is critical, especially if you’re writing an essay for a research project. Instead of listing your data in sentences, resort to making graphs of your statistics. If you list megabytes of raw data in plaintext onto your research paper for your readers to figure them out by themselves, the essay will be distracting or even fishy, leading to readers to abandon the article. On the other hand, graphs are more compelling, concise, and easy to read than text alone. They always come preprocessed so that readers can grasp their ideas quickly and move on.

Do we need to write complicated programs to create graphs? Definitely not. In fact, there are many tools available to create graphs. One of them is Canva, the app used to compile most images on our website. The “charts” feature in this app enables us to insert data manually or import data from Google Sheets to make graphs. However, if your dataset does not use spreadsheets (which is perfectly normal), the matplotlib library is your best bet. Using it requires basic Python programming skills, though. Therefore, if you want a general walkthrough of what the library could do and how to access these features, look here for a brief tutorial.

Top left: A bar chart; Top middle: A line chart; Top right: A histogram; Bottom left: A pie chart; Bottom right: A scatter plot
There are many types of graphs, which can be used to convey many types of data
Image created using Canva

9. Use the Proper Data Analysis Techniques

Last but not least, if you don’t scan through the data and derive a solution, what’s the point in managing your data? Therefore, you should choose algorithms and methods to analyze your information. For instance, if you’re searching for a correlation, you should conduct a two-dimensional analysis involving the change between two continuous values. If one value increases as the other does, there is a positive correlation. In contrast, if one of the variables decreases as the other increases, there is a negative correlation.

Left: Positive correlation; Right: Negative correlation
Image created using Canva

However, if you compare differences between two datasets where only one is expressed as numbers, you should use the null hypothesis. By analyzing the differences between the two datasets using normal distribution curves and standard deviations, you can obtain the p-value. The lower the p-value, the higher the chance that there is an actual difference between the two datasets, the lower the probability that the differences are simply random. If the p-value is lower than 5%, statistical significance is reached, and it can be concluded that the difference between the two datasets is actually due to the external factors that are studied.

The pandas library is an excellent tool for more advanced functions used to analyze data. However, using it requires basic Python programming skills. For a walkthrough of what the library can do and how to access these features, look here for a brief tutorial. That’s why we didn’t go too mathematically in-depth in the last two paragraphs. Most, if not all, of these mathematical functions can be found in data analysis libraries.


In this article, we’ve mentioned 9 data administration tips that can come in handy any time when you’re working on a project or working for a company. If you want to learn more about these tips, please visit the webpages in the references below. Moreover, if you notice mistakes or incomplete ideas in this article, please post your suggestion on the comments below to improve this article.

References and Credits

  1. datachick. (February 24, 2014.). 10 Tips For Managing Database Management Data. Retrieved March 8, 2022, from https://thwack.solarwinds.com/resources/b/geek-speak/posts/10-tips-for-managing-database-management-data
  2. Mark Williams. (2019, January 24). 8 essential data management tips and best practices for small business owners. Retrieved March 8, 2022, from https://www.pensar.co.uk/blog/8-essential-data-management-tips-and-best-practices-for-small-business-owners
  3. (n.d.). 7 Best Practices for Successful Data Management. Retrieved March 8, 2022, from https://www.tableau.com/learn/articles/data-management-best-practices
  4. Dillon Phillips. (2020, June 9). How to Stop Data Theft: 10 Simple Tips. Retrieved March 8, 2022, from https://www.tokenex.com/blog/how-to-stop-data-theft
  5. (n.d.). 10 Tips for Data Theft Prevention. Retrieved March 8, 2022, from https://launchworkplaces.com/10-tips-data-theft-prevention/
  6. Admond Lee. (2019, July 13.). P-values Explained By Data Scientist. Retrieved March 8, 2022, from https://towardsdatascience.com/p-values-explained-by-data-scientist-f40a746cfc8
  7. Deborah J. Rumsey. (2021, July 13). How to Determine a p-Value When Testing a Null Hypothesis. Retrieved March 8, 2022, from https://www.dummies.com/article/academics-the-arts/math/statistics/how-to-determine-a-p-value-when-testing-a-null-hypothesis-169062

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