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Containers Expo Blog: Article

What to Do About the Data Silo Challenge

Using Data Abstraction to Bring Agility to BI and Analytics

Organizations today understand that better access to information assets can improve their bottom-line.

But they struggle with the variety of enterprise, cloud and big data sources, and all their associated access mechanisms, syntax, security, etc.  Further, few data sources are structured properly for business user or application consumption, let alone reuse.  And often the data is incomplete or duplicated.

Data Abstraction Addresses These Challenges
Data abstraction overcomes data source to data consumer incompatibility by transforming data from its native structure and syntax into reusable views and data services that are easy for application developers and business analysts to understand and consume.

Data Abstraction Technology Options
Some data abstraction approaches used today work better than others.

For example, some organizations build data abstraction by hand in Java or use business process management (BPM) tools.  Unfortunately, these are often constrained by brittleness and inefficiencies.  Further, such approaches are not effective for large data sets since they lack the robust federation and query optimization functions required to meet data consumers' rigorous performance demands.

Data warehouse schemas can also provide data abstraction.  Data modeling strategies for dimensions, hierarchies, facts and more are well documented.  Also well understood is the high cost and lack of agility in the data warehousing approach.  Further, data warehouse based schemas don't include the so many new classes of data (big data, cloud data, external data services and more) that reside outside the data warehouse.

Data Virtualization Is a Superior Solution for Data Abstraction
Data virtualization
is an optimal way to implement data abstraction at enterprise scale.  From an enterprise architecture point of view, data virtualization provides a semantic abstraction or data services layer supporting multiple consuming applications.  This middle layer of reusable services decouples the underlying source data and consuming solution layers. This provides the flexibility required to deal with each layer in the most effective manner, as well as the agility to work quickly across layers as applications, schemas or underlying data sources change.

Data Abstraction Reference Architecture
The diagram below outlines the layers that form Composite Software's Data Abstraction Reference Architecture.  Architects and analysts can use as a guide when building a data abstraction layer using Composite's data virtualization platform.  This various layers included in this reference architecture are described in Figure 1.

  • Data Consumers - Client applications want to retrieve data in various formats and protocols. They want to receive the data in a way that they understand. Data abstraction allows the consumers to format the data according to their specifications and deliver over various transport protocols including: Web Services, REST, JDBC and Java clients.
  • Application Layer - The "Application Layer" serves to map the Business Layer into the format which each application data consumer wants to see. It might mean formatting into XML for Web services or creating views with different alias names that match the way the consumers are used to seeing their data.
  • Business Layer - The "Business Layer" is predicated on the idea that the business has a standard or canonical way to describing key business entities such as customers and products. In the financial industry, one often accesses information according to financial instruments and issuers amongst many other entities. Typically, a data modeler would work with business experts and data providers to define a set of "logical" or "canonical" views that represent these business entities. These views are reusable components that can and should be used across business lines by multiple consumers.
  • Physical Layer - The "Physical Layer" provides access to underlying data sources and performs a physical to logical mapping.
    • The "Physical Metadata" is essentially imported from the physical data sources and used as way to onboard the metadata required by the data abstraction layer to perform its mapping functions. As an "as-is" layer, entity names and attributes are never changed in this layer.
    • The "Formatting Views" provide a way to map the physical metadata by aliasing the physical names to logical names. Additionally the formatting views can facilitate simple tasks such as value formatting, data type casting, derived columns and light data quality mapping. This layer is derived from the physical sources and performs a one-to-one mapping between the physical source attributes and their corresponding "logical/canonical" attribute name. This layer serves as a buffer between the physical source and the logical business layer views. Naming conventions are very important and introduced in this layer.
  • Data Sources -The data sources are the physical information assets that exist within and without an organization. These assets may be databases, packaged applications such as SAP, Web services, Excel spreadsheets and more.

Summary of Key Benefits
Data abstraction bridges the gap between business needs and source data's original form. This best practice implementation of data virtualization provides the following benefits:

  • Simplify information access - Bridge business and IT terminology and technology so both can succeed.
  • Common business view of the data - Gain agility, efficiency and reuse across applications via an enterprise information model or "Canonical" model.
  • More accurate data - Consistently apply data quality and validation rules across all data sources.
  • More secure data - Consistently apply data security rules across all data sources and consumers via a unified security framework.
  • End-to-end control - Use a data virtualization platform to consistently manage data access and delivery across multiple sources and consumers.
  • Business and IT change insulation - Insulate consuming applications and users from changes in the source and vice versa. Business users and pplications developers work with a more stable view of the data. IT can make ongoing changes and relocation of physical data sources without impacting information users.

More Stories By Robert Eve

Robert Eve is the EVP of Marketing at Composite Software, the data virtualization gold standard and co-author of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility. Bob's experience includes executive level roles at leading enterprise software companies such as Mercury Interactive, PeopleSoft, and Oracle. Bob holds a Masters of Science from the Massachusetts Institute of Technology and a Bachelor of Science from the University of California at Berkeley.

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