Data Hoarding

Structured data is neat, tidy, logical and we understand how to use it.

Unstructured data however is a mess that we don’t know what to do with.  It is coming from modalities, end points, IoT, users, consumers and machine outputs.  We have so much, and the growth is continuing to rise.   Describing this as data hoarding is a great analogy because we don’t know what to do with it, but we keep it because we think we might need it someday.

Unstructured data typically makes up eighty percent or more of what is kept and much of it hasn’t been touched in years. What can we do to finally address the problem and use it to an advantage?

Let’s get organized with a three-step plan:  Store.  Manage.  Analyze.

Storing the data seems simple because we already do that. The storage infrastructure provides the entire backend support system required to house, transfer, and safeguard data. It is an essential aspect of data processing and analysis.

The question is though in how much of it is in silos that are kept separated by different storage devices, locations, applications, end-points, and data centers both on-prem and cloud?  Identifying the data location and defining its type(s) is the first task.

Storage at the proper location and in the most effective manner for cost and speed is the basis for being able to turn unstructured data into something usable.

Once the stored data has been identified, it’s time to Manage it because storage itself was never intended to globally manage data.  We don’t need to centralize the data, but we do need to map and index it in a way that clearly defines what is there.

Management of data is a critical bridge to the underlying infrastructure, giving us an ability to view the data from all locations in one global view.  It allows us to produce analytics about the data and put it to a location where specific applications can access it. Once data has been identified and sorted, we can better determine what is of value and what isn’t.

Data deletion has been a part of data management that has been avoided, thus contributing to the problem we find ourselves in. There are times that keeping data around too long can add liabilities to your business besides just the cost of storage. Good data management can help with intelligent decisions about what can be purged.


This is where we can begin to find value in the unstructured data. Data analytics empowers organizations to derive insights and make conclusions from their data.

Begin with focusing on what business problems you have and what you see as potential answers to those issues.  Once that has been defined, Artificial Intelligence tools can take these goals and analyze unstructured data to search for the answers. The learning process begins with a large data set, and the model looks for patterns in the data to make better decisions in the future.

The product of this analysis can be used to convert business data into visualizations for human review, or it can eventually automatically feed other applications that utilize it. This AI process can enable systems to automatically learn and improve from experience without being explicitly programmed.

This is a simplistic outline of the process but hopefully gives you a view of what can be done with all the data that your business has just sitting around today costing you money.

Tim Hollingshead
Dataedge Solutions

Contact Dataedge for a discussion of how these processes can be of benefit to you. 913.780.2515.


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