Big Data

6 Data Management Best Practices for Modern Enterprises

Data Management

Data is at the center of everything companies do today. Data management and storage are critical functions in modern enterprises as a result, and companies are increasing the amount of data they collect. Unfortunately, putting this data to use is a challenge.

From infrastructure issues to data formatting errors, poor data management is letting companies down. Here are six best practices to ensure your data management solution is effective, and you’re squeezing the most out of your data.

Build cataloging standards

If you want to use data, you first need to find it. This task is tougher than it sounds. Most organizations have a haphazard way of naming and storing their data, leading to confusion. For instance, one department might store data differently from another.

The key is to create a standard for all file names and datasets. Your file names must be descriptive, allowing users to search for relevant information easily. For instance, create a standard date (DDMMYYYY or YYYY-MM-DD) and timestamp format.

Note that these conventions must make sense to international users too. Most enterprises are global, and a local standard might not make sense if adopted globally. Make sure your data management solution helps you create these standards seamlessly.

Record metadata

Metadata refers to data about data. For instance, the identity of a file’s creator is metadata. Organizing and cataloging metadata is critical since it gives users critical context. At the very least, your metadata logs must contain information about the data’s content, structure, and permissions.

Metadata makes files discoverable. For example, a user can search for all data logged by a department or an employee. Data of this kind helps you use critical insights for a long time. Make sure you also include information about how the data was created and why.

Often, the reasons behind data’s existence get lost, reducing the context for future use. Without this context, data might be misused down the road, leading to less than satisfactory results.

Choose the right storage model

Once you’ve gathered data, you need to put it somewhere. Storage plans and choices play a critical role in successful data management. Note that a plan that works for one company may not work for another. Take the time to think deeply about how your data is accessed and who will use it.

Security is also a critical factor to account for when deciding storage options. Shared drives are great for common knowledge or reference material. They do not work for sensitive data though. Cloud storage is great for complex data sets.

However, security might be an issue so consider using a solution that guards your keys and secrets. Note that there is no single “best” storage solution. Each has its positives and negatives.

As a rule of thumb, follow the 3-2-1 methodology. This states you must have three copies of your data, two types of storage methods, and one must be offsite. This way, your data will be secure and accessible at all times.

Develop documentation

Context is critical to data analysis. Documentation offers this context to users and without it, you’re risking incorrect conclusions. While metadata offers some context, it doesn’t offer deep explanations behind data. For example, metadata cannot record the assumptions a user had when data was collected, and conclusions reached.

Documentation is what does the trick. Product several levels of documentation to offer a well-rounded context to future users. At the very least, you must have a project, file, and software-level documentation.

Make data a cultural point

Many organizations take the right steps when initiating an analytics program but fail to instill the right culture among employees. Most departments stick to what they’ve been doing, and transitioning to new data management practices can be challenging.

The first step is to communicate the shortfalls in current workflows. Once employees understand where the shortcomings are, they’ll be receptive to adopting new work practices. Executing data management changes is a tough task but with employee buy-in, it becomes a bit easier.

For example, tasking a department with changing its file naming conventions is easier if the employees in it are on board with the changes. Executives must always communicate the need for a data-driven culture and ensure their decisions reflect that philosophy.

Choose the right tools

Data management used to be executed on spreadsheets, but the volumes of modern data-gathering practices render this method obsolete. Evaluate all data management platforms out there and choose the one that best fits your needs.

Most platforms offer metadata management as standard but check whether you require any additional features. The volume of data you gather, and its quality often dictate what you need.

Sophisticated data management is key

Successful organizations manage their data and squeeze more out of it than their competitors. To make the leap, your company must embrace modern data management practices and put those insights into action. Failure to do so might cost you more than money.

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