7 Best Practices That Help To Avoid Common Data Management Mistakes

Considering big data applications are growing at such a rapid rate, more and more firms are opting for digital transformation to stay relevant and up to date with the latest trends.

Organizations have acknowledged the value of big data and are treating it as an asset (perhaps the most valuable of all as it has the power to determine growth trajectory and provide an upper hand over competitors), but they are yet to get any valuable insights from it.

True, data leveraging isn’t for everyone, and translating it into information that is consistent, correct, and thorough, is frequently found to be lacking. Many companies throughout the world have increased their enterprise data management efforts in recent years, hiring experts in data management services, but the rate of success of these projects has been discouraging.

Those of you who work with big data will agree that businesses are having a difficult time controlling and making sense of the huge amounts of data that must be controlled and made sense of in order to sustain competitiveness, meet customer needs, and, obviously, comply with the law. The battle to design programs that permit organizational sustainability and determine corporate integrity is real. With effective data management services, this can be easily achieved.

In this article, we have mentioned seven best practices for your business to consider for effective and efficient data management.

Build strong file naming and categorizing conventions

If you want to use data, you must first be able to locate it. If you can’t manage it, you can’t measure it. Create a user- and future-friendly reporting or file system, with descriptive, standardized file names that are easy to identify and file formats that enable users to search for and discover data sets while keeping long-term access in mind.

A typical format for listing dates is YYYY-MM-DD or YYYYMMDD.

It’s ideal to use a Unix timestamp or a defined 24-hour notation, such as HH:MM:SS when listing times. Users can keep note of where the information they need comes from and find it by time zone, whether your organization is national or global.

Consider Metadata for data sets carefully

Metadata is essentially descriptive information about the data you’re working with. It should
include details about the data’s content, structure, and permissions so that it can be found and used in the future. You can’t rely on being able to use your data years down the road if you don’t have this precise information that is searchable and discoverable.

Items in the catalog include data author, what data is contained in this set, descriptions of fields, when and where was the data created, why was it created, and how was it created.

This information will then assist you in creating and analyzing a data lineage as the data flows from its source to its destination. It also comes in handy when mapping relevant data and recording data relationships. The first step in developing a solid data governance process is to collect metadata that informs a secure data lineage.

Data Storage

Storage strategies are a necessary part of your workflow if you ever want to be able to access the data you’re creating. For all data backups and preservation methods, develop a strategy that works for your company. Consider your requirements carefully because a solution that works for a large corporation may not be suitable for the demands of a small initiative.

Consider the following storage options:

  • Desktops/laptops
  • Networked drives
  • External hard drives
  • Optical storage
  • Cloud storage
  • Flash drives

The 3-2-1 methodology

The 3-2-1 approach is a basic and widely used storage strategy. The following strategic
recommendations are suggested by this methodology:

3: Back up your data three times
2: Using two different storage techniques
1: Storing one of them offshore

Without being unduly redundant or complicated, this strategy provides smart access and ensures that a copy is always available in case one type of place is lost or destroyed.


We can’t disregard documentation when it comes to data management best practices. It’s generally, a good idea to create numerous levels of documentation that explain why the data exists and how it can be used.

Documentation levels:
Software used

Commitment to Data Culture

Ensuring that your department or company’s leadership prioritizes data experimentation and analytics is part of a commitment to data culture. This is important when leadership and strategy are required, and if budget and time are needed to ensure that adequate training is conducted and received. Furthermore, having executive sponsorship and lateral buy-in will enable better data collaboration throughout your organization’s teams.

Data Quality Trust in Security and Privacy

Building a data-quality culture necessitates a commitment to creating a secure environment with high privacy standards. When you’re trying to provide secure data for internal communications and planning, or when you’re trying to develop a trusting relationship with a customer by ensuring that their data and information are kept private, security is important.

Your management procedures must demonstrate that you have secure networks and that your staff is aware of the importance of data privacy. Data security has been acknowledged as one of the most important decision-making elements in today’s digital market when firms and consumers make purchasing decisions. One breach of data privacy is too many. So, plan

Invest in Quality Data Management Software

It is recommended, if not needed, that you invest in quality data-management software when evaluating these best practices together. Organizing all of the data you’re collecting into a usable business tool can make it easier to find the information you need.

Then you can construct the appropriate data sets and data-extract scheduling to meet your business requirements. Data management software will help you design your best governance plan by working with both internal and external data assets. Tableau has a Data Management Add-On that can assist you in implementing these best practices in your analytics environment.

Using trustworthy software to help you build, catalog, and control your data can help you gain confidence in the quality of your data and lead to self-service analytics adoption. Take your data management to the next level with these tools and best practices, and build your analytics culture around managed, trustworthy, and safe data.

Trying to solve all of your data challenges in the early stages of data management is a recipe for disaster. To minimize difficulties and accomplish your organizational demands on schedule, we recommend going slow and taking baby steps.