The Increased Diversity Of Big Data:

With the increased improvement in technology regarding speed and scaling, it has not decreased the challenges associated with schema transformation, integration of data, or the complexity to take informed actions.

The influence of cloud computing, distributed computing, and mobile technologies, have all contributed to today's diversified IT environment for big data.  Traditional approaches to data management and data lakes cannot keep up with the requirements to bring together data, no matter where it's located, across the enterprise platform for one singular control over multiple sources.

The Enterprise Knowledge Graph is a platform that combines big data and graph technology, which deals with these limitations and greatly improves big data management.  It offers singular access to data across this platform in any form.  It adjusts data into a standard format and helps assist with actions needed to continually leverage them for many organizational uses.

Enterprise Knowledge Graphs:

Data lakes allow universal access to data in their original formats but do not have the needed metadata and semantic consistency for sustainability for any length of time.  Enterprise Knowledge Graphs have metadata and semantic benefits of MDM hubs and will connect all data together in line with semantic standards.

While businesses have different names for labeling their products, the combination of enterprise spanning connections and data representation brings everything together for immediate recognition by the user.

For example, health care providers can connect to enormous volumes of data in their industry by creating lists of events including patient outcomes, operations, billing, diagnostics, and describing them in a uniform terminology across the data range.

It doesn't matter where the data is located, whether it's in the cloud or a cache, the user can link them in the same format that meets their business purposes.  The standard concepts or categories are adaptable to incorporate new events and into unified terminology to align all data to the knowledge graph's design, no matter what their origination or distinctions might be.

Active Automation:

With data coming from any number of sources, the automatic generation of code for analytics or for transformation is extremely valuable.  One of the biggest advantages of the enterprise knowledge graph, it reduces the challenges for not only accessing data but applying it to actions.

As an example, a healthcare provider attempting to anticipate the occurrence of respiratory failure for patients in various locations, they should use the knowledge graph application to monitor the blood pressure of all hospital occupants.  The graph will allow the organization to create a theoretical description of the blood pressure data, related to respiratory failure, then automatically amass the information into code that will gather the data.

The value of this approach, the user simply does a search for the data they need, no matter where it is located.  The automation abilities of the enterprise knowledge graph will create an action to collect data that matches the search.  The user does not need to know the source system or particulars of its design to get the data.  Access is much easier because all data engineering of cleaning and transforming the data is done prior to the search.

The relevant data for the search can come from multiple systems but will be accessible from a single place.  The user is not responsible for accessing those systems, instead, the search mechanism is able to pull the appropriate data from the various systems and is accessible from the central knowledge graph.

Enterprise Knowledge Graph Unification:

It brings together the means of accessing, automating, representing, and moving the data from many different sources and architectural intricacies.  Along with how users retrieve the data through automation, the knowledge graph will standardize the data in accordance with the relevancy of business terms.  This results in making the data standardized from any number of data sources or types.

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