A crucial part of every company’s business intelligence (BI) is its data dictionary. When you have a well-structured data dictionary, you provide BI teams with an easy way to track and manage metadata throughout the entire enterprise. The following is a common point of contention for many analysts:
What makes a good data dictionary and how can it actually streamline your BI team’s workflow? Let’s take a look at the process.
What Exactly is a Data Dictionary?
A data dictionary provides information about and context for your company’s data. It’s a handy tool that provides BI teams, analysts, designers, and developers an easy way to understand their metadata at a glance. More specifically, data dictionaries are inventories of database schemas, columns, and all tables that exist within an enterprise.
Found within a data dictionary are various properties that highlight each dataset in your system:
- Business Definitions
- Constraints
- Data Attributes
- Length
- Relations to Other Data Fields
- Type
- Valid/ Default Values
A data dictionary is essentially a one-stop-shop for all of these terms and definitions. It allows BI teams to be on the same page, no matter what databases they’re working with. It is also important to maintain a data dictionary when teams are working remotely. In simplest terms, a data dictionary is a handy guide that shows business owners what tables exist and acts as a foundational part of a company’s data discovery.
Keep in mind that a data dictionary should not be confused with a business glossary. Although they may sound similar, the two are separate items. A data dictionary is mainly composed of more technical terms. In contrast, a business glossary is a wider tool that defines terminology used by many departments throughout a company. A business glossary should be able to be understood by anyone working in the organization.
The Key Word is ‘Automated’
Compiling a data dictionary by hand is easier said than done. Just think about how much effort and time would be required to create an enterprise-level data dictionary manually. Due to the sheer volume of data that most companies work with, automation is necessary to unlock the complete potential of a data dictionary.
Data dictionaries aren’t a new concept, but they’re one that many businesses don’t leverage fully. It can take months to manually input metadata, and most BI teams get burned out performing this tedious task.
The easiest way to both construct an automated data dictionary, as well as simplify the metadata management process is with a dedicated platform. It collects information from each of your databases and then constructs the data into an easy-to-use interface. With the right tools, it’s relatively easy to collect relational database information and develop a data catalog your BI teams can reference. The data then becomes searchable so BI professionals can quickly locate and isolate relevant information.
Best Practices for Creating Your Data Dictionary
As far as data dictionary best practices go, it is beneficial to have an idea of your starting point before you begin. You may have some reference tables, spreadsheets, or data dictionary tools already in use. Your metadata management provider can use those already established items to better understand what type of workflow you imagine for your automated data dictionary.
For example, healthcare companies rely on efficient, structured BI catalogs. There’s no other way for medical professionals to manage data assets, locate patient information at a moment’s notice, or secure personal protected information so that it’s HIPPA-compliant.
A metadata management provider can easily access healthcare companies’ assets. It can also develop cross-platform capabilities that reach across every patient file, billing record, and client communication portal that may exist in the enterprise.
After deciding how you want your data dictionary to be used, create a cohesive team so everyone is on the same page. This includes limiting abbreviations and maintaining definitions across the board. Once your data dictionary is established, be sure to update it frequently so it continues to be effective.
Bring the Benefits of a Data Dictionary to Your Enterprise
With an automated data dictionary, your company can eliminate data redundancies, easily trace data lineage, better utilize your data, and ensure higher-quality data. In other words, it’s a must-have for every business that works with data, no matter the industry.
