If you are part of a large organization, chances are good that you’re up to your eyeballs in data. Not only that, but the data you have is probably from multiple sources, stored in multiple formats, and housed in various file systems, databases, and data warehouses.
It’s a big problem, and it’s getting bigger all the time because your business is growing. Whether organically, by merger or acquisition, or even by both, new data assets are being acquired or created, and all of them are growing by ever-greedier data collection methods.
When you’re drowning in a sea of data, it’s difficult to comprehend the complexity of the data landscape, much less use it to gain insights and competitive advantage.
How do you get enough of a handle on all this data to understand what you have and how to exploit it?
The answer is to build a data catalog.
Enterprise Data Catalogs to the Rescue
What is a data catalog? A data catalog is an accessible, intuitive summary representation of the objects in your data portfolio.
A data catalog provides information about each data asset, such as:
– Where it came from
– How it is (or was) populated
– Who’s responsible for it
– Whether it’s in production use
– Technical information (table structures, views, indexes, and so on)
– Where and how the data is stored
An enterprise data catalog, as you might imagine, is built at enterprise scale and can incorporate data assets belonging to various subsidiaries, joint ventures, and other business or organizational units.
The data catalog platform—the software that is used to compile and access the data catalog—provides a user interface that enables users to search the catalog and display the results.
Who Uses Data Catalogs? And For What?
Data catalogs are generally used by technical resources. For example, a data scientist tasked with performing a certain type of analysis, or a BI professional fulfilling a request for a new report, would consult the data catalog to determine what data sources are needed and whether these data sources are complete and reliable. It can also help them identify gaps—data that is needed for the task at hand but not available anywhere in the enterprise.
Building a Data Catalog? Not Without a Metadata Management Tool
Most data catalogs are built with data catalog tools. A data catalog tool takes the information about the data assets and organizes it in a way that is structured, intuitive, and scalable.
As frequent readers of this blog already know, this information about the data assets is called metadata. Data catalog metadata management is the foundation and the key to success for any data catalog. A data catalog tool is only as good as the metadata catalog on which it relies.
And how do you make sure your metadata catalog is any good? The first step is: Don’t try to gather your metadata manually. Because of the intense and tedious labor involved, manual metadata management is unsustainable, especially in large enterprises with constantly shifting data landscapes. A better approach is to adopt automated tools. These tools can do the job much faster and with more efficiency, and they never get tired of the tedium of poring through endless data assets to pick out and catalog the right metadata.
Automated metadata management tools, such as Octopai’s automated data discovery and lineage platform, can make implementing a data catalog much easier, by doing the heavy lifting of discovering and compiling the necessary metadata.
Data catalogs are becoming increasingly popular in organizations of all sizes these days, because they make life easier for all stakeholders, from data asset administrators to data scientists and BI professionals all the way to the ultimate consumers of the data. Data catalogs enable better management of the data and faster turnaround on data-related demands.
Your data landscape is not becoming simpler or easier to manage. If building a data catalog is in the near future for your organization, as it should be, then be sure you have a proper metadata management tool in place before you begin.