In any organization, one of the key challenges end users face is finding data easily enough that they can use it in their daily work.
The challenge users face in simply “finding the dashboard” is evidenced by a commonly implemented hack: bookmarking the frequently used report. While these simple solutions might serve as an adequate band-aid for a user of your data, the inability of users organization wide to find what they need serves as a key impediment to widespread adoption of your data.
Data asset management solutions can accelerate the democratization of your data, particularly if they have a robust system for automating collections of data assets.
A collection is, simply put, a consolidated library of assets — the most impactful are organized around specific themes, job functions, or projects. Collections also represent a step forward over more traditional folder systems, bringing a higher level of flexibility and automation to your method for organizing assets.
While the associated assets might be from a single analytics tool, the most powerful and useful include assets from different systems where users access and analyze data. This could be as simple as a self service asset from your BI solution, alongside a Google Sheet.
In its ideal form, a collection should encourage users not to even think about what systems they are using, and instead focus on accessing data they need.
Unlike a traditional folder system, collections are not mutually exclusive, allowing assets to exist in multiple collections at once rather than being restricted to an enforced hierarchy. Collections can also be automatically populated and shared with your target audience of end users, saving your team the manual work of maintaining folders and ensuring everyone who needs access to a specific set of assets has it.
Collections are meant to address many of the persistent issues organizations face with data asset sprawl, and the resulting disorder that can infect even the most data driven organization. Among the problems solved:
There are many good use cases for collections, but at their most basic, collections solve the problem of where end users can find the data they need, and draw attention to the relationships between different assets.
We touched on this briefly in the previous section, but the differences between collections and other methods of organizing data are worth exploring in more detail.
One obvious advantage of a collection over a folder in a native analytics tool is that collections are inherently tool-agnostic, meaning that your relevant data from many different tools can all be housed in the same place for easy reference. The organization can happen within a single place – your data asset management solution – rather than scattered across the different systems where your data and analytics might live. This is a boon to data users across your organization, as they can more easily locate what they need in a single place.
Moreover, because you can automate the maintenance and sharing of a collection, you save your team the monotonous work of curating folders, and ensure the ongoing quality of which data assets populate a collection and therefore are consumed.
In contrast to traditional folder structures, intranets are another way teams have tried to highlight the most important assets users need to know about. While having the benefits of being tool agnostic, an intranet’s inherent limitation is that it does not contain the actual assets it is referring to, nor is its maintenance automated. At best it will contain links out to the relevant system, and at worst it will be so out of date that the information will be meaningless.
Collections, by contrast, are a single access point for the real assets and their live data, removing this additional layer of maintenance and navigation between the user and the final data product. This approach brings your organization one step closer to real time data-driven decision making.
In a data asset management system, asset collections are not just drag and drop folders, but are instead powered by an underlying rules engine. Rather than relying on manual maintenance, collections can be automatically populated, shared and updated in real-time. This solves pain points for both your data builders and your data consumers.
For data builders, the benefits are primarily in the amount of time saved both in maintenance and in having to answer fewer questions from other teams about which data to use. Keeping an intranet or folder structure up to date is a laborious process — automated collections save all of that busywork.
For data consumers the benefits are even more evident. With fewer uncertainties about which data to use for which task, the time to value for your data, and the relative confidence about that data, improves exponentially. Collections allow data consumers to spend their time driving positive impacts in the organization, rather than sifting through several different tools or asking their data team to find what they need.
The real benefit, of course, is the greater efficiency for your business in taking advantage of your existing analytics, and more time for your analytics teams to build the data products that make your organization successful.
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