Inventory of Data Assets Necessary to Recognize Value of Data

Inventory of Data Assets Necessary to Recognize Value of Data

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Something we often say during our early conversations with clients is that data governance is what you do when you recognize data is an asset, and when you start to treat it like one. What happens when you apply the same expectations for use, care, management, and accountability to data that you already apply to financial assets, human capital, facilities and property?

In our experience, it’s difficult to fully treat data like a valued asset without an understanding of the individual and collected data assets you have at your organization. In this post we want to describe data assets, and to demonstrate how a proper inventory of them can assist your institution in its quest to recognize the value of its data, and to utilize that data as a strategic asset.

 Some data assets are physical. Mostly we're talking about data centers, servers, disk drives, tape storage, and the like, although some data collections are housed on individual workstations or laptops. Data stored in the cloud still has a physical location somewhere, although usually this storage is leased rather than owned.

In terms of expense, your greatest data assets are probably wrapped up in software purchase or licensing, although this could also include support, consulting, and training expenses. Database applications and BI tools are a couple of the big targets here.

Ultimately, your most valuable data assets are likely your data collections and arrangements. You collect and store data about students, employees, vendors, facilities, real property, etc. As with intellectual property (IP), some of that data might be proprietary. Even if some or all of that information is publicly available, its organization and storage is your responsibility and potentially the source of your strategic advantage.

Once we start talking about data as akin to IP, then it becomes clearer how your data assets are not just commodities, not just assets in the balance sheet sense. The collections and arrangements and transformations and analyses of data are—or they can be—a leverageable resource, something you use in the course of regular operations, ideally to better understand or to improve those operations. And of course, a goal of every organization that wants to be data-enabled is to turn its commoditized data into a strategic asset, something that informs planning and decision-making.

Some of the challenge involved in recognizing and treating data as an asset is that there is often no comprehensive inventory of data assets.

  • How many database and application servers are in operation in your organization?
  • Where are they located?
  • Who maintains them?
  • What applications run against them?
  • Who manages access to those applications?

It’s not as though these are simple questions to answer.

We work with a large state university system that has identified over 150 integrations with its primary ERP package. That’s 150 additional systems about which a full inventory would include vendor, technology, licensing, user management, storage and backup information, etc.  And don’t forget the integrations among those systems outside of the ERP!

Now, not every institution is that large, or decentralized. But every institution will benefit from doing this kind of research, and from staying on top of this information once it’s recorded. Initially, look to answer these questions:

  • What systems do we own or license?
  • What technology/platform do they utilize?
  • Who (which office) is responsible for payment/maintenance?

Once you’ve done that, answer the following questions about your data assets:

  • What data do we store and track in each of these systems, and who is responsible?
  • How long does it stay there?
  • Does data from one system also reside in other systems?
  • Who provides guidance for entering, viewing, and/or extracting data from them?
  • What can be done with the data in these systems? Who decides that? Who enforces those decisions?
  • For shared systems, what does that shared responsibility look like? How much detail do you need?

Incomplete or insufficient answers to either of the previous lists of questions help pave the road to:

  • lost or wasted funds or opportunities
  • data privacy lapses
  • data integrity failures and data quality issues
  • improper sharing and usage
  • erroneous analysis and poor decisions

It turns out that data governance, which is often described as the policies and procedures you put in place to protect against these shortcomings, includes a lot of documentation!

The substance of this documentation could be a blog post by itself.  But at a minimum you will need:

  • functional description of the business use of the data
  • any classification about security, privacy, confidentiality, etc.
  • which systems it can be found in
  • the transformation and lineage as data moves from system to system
  • rules and regulations about archiving, destruction, or other disposition
  • other stewardship responsibilities such as quality indicators or access protocols
  • what key reports does the data get surfaced in

An inventory of data assets, then, includes multiple classes of information, and requires people from all corners of your organization to fully populate it. We at IData are aware of this from our data consulting practice, and so we created our Data Cookbook solution to assist you to organize and document data assets from several directions:

  • Data system inventory for documenting software and data assets.
  • Business glossary with a built-in data stewardship workflow, where your subject matter experts and functional users document how they name and utilize data. The business glossary also allows for in-depth technical documentation of data concepts and definitions, specific to your organization’s use and standards.
  • Data catalog, where your report specifications, dashboards, integrations, BI and warehousing layers and objects, etc., can all be described and in which the data movement can be mapped.
  • It includes a data quality suite, where data quality rules and reference data can be elaborated, and where users can report perceived data quality issues and see the resolution of those issues.
  • All these objects can be related to each other, and the appropriate users can be notified when changes occur. And our impact analysis tool identifies when a change to one object might have an effect on other objects documented in the Data Cookbook.
  • In conjunction with our enterprise service bus, the IDataHub, you can automate data quality checks, reference data management and synchronization, and track changes to your data models.

We recognize that there are many other reasons organizations have trouble leveraging data for strategic purposes: they may not be collecting all the data they need, or in sufficient quantities; many lack the analytical expertise or data fluency to recognize actionable information; as with nearly all IT and data-related issues, bandwidth is always at a premium. The Data Cookbook should be part of your data management toolset. And once you establish the foundation of your data asset inventory, it will be easier to identify gaps in data collection, it will be easier to build the necessary data fluency, it will free up management and analytical bandwidth to improve decision support.

IData has a solution, the Data Cookbook, that can aid the employees and the institution in its data governance, reporting, data-driven decision making and data quality initiatives. IData also has experts that can assist with data governance, reporting, integration and other technology services on an as needed basis. Feel free to contact us and let us know how we can assist.

(image credit StockSnap_WG9DCQX3J3_gold_data_assets_BP #1102)

Aaron Walker
About the Author

Aaron joined IData in 2014 after over 20 years in higher education, including more than 15 years providing analytics and decision support services. Aaron’s role at IData includes establishing data governance, training data stewards, and improving business intelligence solutions.

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