Solutions

Adding Real Meaning to Data Catalogs: for Humans and Machines

The promise of Data Catalogs

Data catalogs provide access to organizationally relevant data. They can help knowledge workers locate data, and they can help Chief Data Officers ensure regulatory compliance and implement appropriate cyber policies.

The Problem with Data Catalogs

Data catalogs are still expressed in terms of the data’s original labels. If the data’s label is unknown, both persons and computers need to search by catalog metadata such as by business group that queries the data or time of last refresh. Which is useful. But not as useful as being able to query by meaning.

Users (persons and computers) should be able to ask for information about something without knowing specific labels. Instead of having to know that information about large customers in danger of turning over is found in fields with the labels ‘customer analytics’, ‘pipeline forecasts’, and ‘sales meeting minutes’ users should just query for information about large customers in danger of turning over

Add real meaning to your data catalog

Using the Ontological computing language of our C.O.R.E. technology, Blender Logic can map the contents of data catalogs into real meaning that humans and computers can understand. Enabling knowledge workers, Chief Data Officers and analytical routines to query and analyze the big data content made accessible through data catalogs without having to remember, lookup or discover all the labels.

Discover what you really know

Armed with an understanding of the real meaning of the data referenced in the catalog, Blender Logic can use its C.O.R.E. Technology to help you discover are as of data redundancy, data contradiction, cross organizational interest, and risky organizational ignorance.

Blender Logic’s consultants can help you add real meaning to your catalogs

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Quantifying the value of the data in your catalog

The need to understand data value

Data is an organizational intangible. Depending on its real meaning it may be an asset and/or a liability. The value of your data small or large, positive or negative is the value and impact of the decisions made internally and/or externally with the data¹. The higher the decision value, the more you can cost-justify data quality improvements or charge an entity for the right to use the data. For most organizations, a small percentage of their data accounts for a high percentage of organizational decision value

Your data value solution

Using our C.O.R.E. technology in combination with Catalog metadata, you can track, estimate and refine value estimates for all the information currently or potentially in your organization. And build automated data catalog functions driven by information value.

Blender Logic’s consultants can help you benefit from adding values-based processing to your data catalogs

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Enterprise Analytics

Enterprise analytics is largely dominated by structured data such as sales data, inventory data transportation and manufacturing logistics and quality data. For enterprise analytics, AIML is most frequently introduced as a way to extend the powerful statistical analysis capabilities already in use, especially for correlation detection and predictive analytics.

C.O.R.E.'s cross channel validations can add confidence, adaptability and robustness to your AIML processes working on enterprise structured data in the same way as they benefit the analysis of unstructured data. The table below illustrates how cross validation can provide the same kinds of benefits for structured as unstructured data.

Cross validation provides the same benifits to structured data analytics
as to unstructured data (e.g. image) analytics

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