August 3, 2016
Big Data vs. Metadata: What’s the Difference?
If you’re a relative newcomer to the exciting world of digital asset management (aka DAM), then you might be wondering what the difference is between Big Data and metadata. Is Big Data just metadata, but…bigger? Kind of. There are some other key distinctions, but this is a good starting point.
Big Data is a collection of data so large (and moving so fast) that it can’t be examined with standard technology tools. Metadata refers to descriptive details about an individual digital asset. Metadata provides granular info about a single file while Big Data gives you the ability to discover patterns and trends in ALL of your data. If metadata is the needle, Big Data is the haystack. Fortunately, the needle is “smart” now and can assist you in finding it…if you know how to look.
When a digital asset is created, so is metadata about its origin, time, date, format, etc. But that’s not enough to stay organized in the rapidly expanding digital era—the savvy brand manager knows that it’s crucial to invest the necessary time in making sure this asset is properly named, tagged, stored, and archived in language (taxonomy) consistent with other assets in the collection. Maintaining a consistent methodology to asset management makes the assets more valuable because they’re easier to find and distribute. Experts estimate that brand managers spend 35-40% of their time at work searching for assets. That’s a huge waste of resources. A good understanding of metadata directly addresses that problem.
It’s also important to highlight the differences between structured and unstructured data. Structured data is the aforementioned attributes that we use to file and store assets (name, date, format). These attributes are used to process, analyze, and predict key factors related to our businesses. Unstructured data is every call, text, purchase, transfer, audio, video, chat, Facebook post, tweet that’s created by our endless amount of digital devices. Servers, hardware, meters, and robots are all generating data logs that record every action. This unstructured data requires analysis in order to make it accessible and relevant to a human being.
This is where Big Data wrangling becomes so important. Hadoop is an open-source software framework with the power to store and process an incredible volume of unstructured data generated by the ever-increasing number of electronic devices and smart machines in the digital era. Enterprise businesses can now take massive amounts of unstructured data, analyze it, and use that information to make better decisions. With an increasing amount of smart machines transmitting data in real-time, this allows key decision-makers to anticipate problems before they occur—a huge advantage in industries where failure or downtime could severely impact the bottom line.
Smaller businesses won’t need the muscle of Hadoop but can make better use of their resources with a workflow that standardizes a way to find, distribute, and archive its assets. With more and more emerging formats and technologies, it’s never been more important to get everyone on the same page so crucial assets don’t need to be re-purchased. Another key benefit to having a consistent taxonomy is that it eliminates the need to have one gatekeeper or person in charge of knowing where everything is; if everyone speaks the same “language” then it’s infinitely easier for them to do their jobs without needing the assistance of a digital sherpa.
Big Data and metadata have one very important thing in common: they’re each only as valuable as you make them. Our devices and machines might be creating more helpful data than ever before but it doesn’t mean anything unless we can understand it in the proper context and use it to make stuff easier to find and share.
To learn more about metadata and its role in streamlining your workflow, check out our Digital Asset Management Best Practices Guide now.