Why the Semantic Database Will Disrupt the Document Management Marketplace

Semantic-database-document-management

Ever bounced the idea of “going paperless” in your organization? Many companies are attracted by it but very few actually do it. The shared folder that comes standard on computers is not robust or secure enough to handle business sensitive information. There are just too many software applications to choose from and most are industry or department specific. But most importantly, there is a massive learning curve involved for employees’ due to changing the way they store and retrieve information. This is a complex problem as it creates a new dynamic between technology and humans, which is difficult to manage.

What’s challenging about this problem is that employees have to abandon the way they access information in their filing cabinets, shared folders, DropBox, SharePoint, SalesForce, etc. and learn something new. The learning curve is high for most because in popular software and storing practices today, information is one-dimensional. It lives in a particular location and you can only retrieve it by going to the location or using a search feature. In this environment, humans analyze and dictate the importance of information that lives in a document and where that information should be stored or filed.

“The logical data structure, whether hierarchical, network, or relational does not satisfy the requirements for a conceptual definition of data because it is limited in scope and biased toward the implementation strategy. Therefore, the need to define data from a conceptual view has led to the development of semantic data modeling techniques. That is, techniques to define the meaning of data within the context of its interrelationships with other data. The real world, in terms of resources, ideas, events, etc., are symbolically defined within physical data stores. A semantic data model is an abstraction which defines how the stored symbols relate to the real world. Thus, the model must be a true representation of the real world” (NIST, 1993). This is where a semantic database comes into play.

Consider the following example: An invoice from Verizon goes into Accounts Payable May 2017 folder instead of a Verizon Vendor folder. However, what happens when you want to see your business relationship with Verizon? Now, you have to retrieve every payment you have made to them (Quickbooks), every contract you signed with them (Document Management software), and every e-mail interaction you have had with them (MS Outlook). That is a cumbersome process and it limits the value of the information that you already have. The fact is, we live in a world dominated by data. Isn’t it time to maximize the value of your organization’s information?

With a semantic database, a document’s storage location is based on its relationships. In other words, it will appear everywhere it needs to be based on those relationships. Now, that invoice from Verizon will appear in your Verizon Vendor folder, Accounts Payable May 2017 folder, and it could appear in other locations as well, such as a technology expenses folder.

Semantic modeling eliminates the necessity of human intervention which in turn, mitigates risks and reduces expenses to your business. It creates a universal platform where all of your information can live, rather than having many different applications for many different business purposes. It eliminates the possibility of human error while managing and accessing information. It reduces time spent by employees compiling and analyzing information. It increases the security of an organization’s information. Most importantly, it gives greater business meaning to underutilized information.

If you are looking for technology that incorporates the semantic database, check out Sirma Enterprise Platform. Click here if you would like a 20-minute demonstration.

Thank you for reading,

Shamit Patel, Sirma Enterprise Systems

Photo Credits from Ontotext.com, a Sirma Group Company

Resources:

FIPS Publication 184 released of IDEF1X by the Computer Systems Laboratory of the National Institute of Standards and Technology (NIST). 21 December 1993.

 

 

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