Data Architecture vs Information Architecture: What’s The Difference?
There’s a well-known argument around data architecture versus information architecture. And the question often asked is: Are they the same thing?
Enterprise architect and Microsoft blog contributor, Nick Malik, recognized the inherent confusion when he was part of a group working to clean up the Wikipedia entries on the subjects. His team believed the entries should be combined. However, in 2014, when he polled the IT community he soon discovered a split audience, where about half of all survey participants believed the two should remain separate.
Let’s take a look at the differences between data and information and the key considerations your enterprise organization needs to understand.
This author agrees that information architecture and data architecture represent two distinctly different entities. There are a couple of reasons for this as described below:
Simply put, data refers to raw, unorganized facts. Think of data as bundles of bulk entries gathered and stored without context. Once context has been attributed to the data by stringing two or more pieces together in a meaningful way, it becomes information.
Similarly, it’s also important to understand the difference as it regards infrastructure. Information architecture refers to the development of programs designed to input, store and analyze meaningful information whereas data architecture is the development of programs that interpret and store data.
Since we’ve established that data and information are not the same, it stands to reason that they can’t be treated the same way in their architecture platforms.
Data architecture is foundational. It looks at incoming data and determines how it’s captured, stored and integrated into other platforms. One such platform is likely a piece of information architecture, like a CRM, that uses raw customer data to draw meaningful connections about sales and sales processes.
The CRM is the information architecture in this example because it specializes in taking raw data and transforming it into something useful.
That’s the clear distinction between data architecture and information architecture. Data architecture defines the collection, storage and movement of data across an organization while information architecture interprets the individual data points into meaningful, useable information.
An “information asset” is the name given to data that has been converted into information. And creating information assets is the driving purpose of information architecture.
Information assets can exist in one of several categories:
Each category suggests the conversion of data into something that is helpful for business initiatives, whether it be a grouping of like data or a visual representation that can offer a meaningful snapshot of data to stakeholders.
Another distinction relates to requirements from a lifecycle management perspective. Besides the obvious difference between data and information, each has a unique lifecycle and best practices for managing it within an organization. Similar to how data infrastructure is at the foundation of solid information infrastructure, proper data lifecycle management will be a key driver of the information lifecycle management process.
Now, let’s dive into some more definitions.
Data lifecycle management refers to the automated processes that push data from one stage to the next throughout its useful life until it ultimately becomes obsolete and is deleted from a database. On the other hand, information lifecycle management looks at questions like whether or not a piece of data is useful, and if yes, how? In a nutshell, information lifecycle management seeks to take raw data and implement it in a relevant way to form information assets.
In addition, information assets have their own lifecycle and value, which are determined by the quality and usefulness of data involved as well as the type of asset as described above.
