The Smart Data Blog

Anzo Smart Data Manager and the Necessity of Data Transformation

Posted by Patrick Wall on May 26, 2016 10:00:00 AM

Data transformation is one of the most vital facets of data management. Prior to integrating data sources, conducting analytics, or utilizing data in most operational applications, data must be transformed from its native state to one suitable for the target system—even if it’s just a data mart.

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Topics: Smart Data, Data Governance, Anzo

A New Approach to BCBS 239 and Beyond: The Smart (Semantic) Data Lake

Posted by Marty Loughlin on May 24, 2016 9:30:00 AM

We are at an inflection point in the financial services industry. The evolving and overwhelming demands of regulatory compliance have forced organizations to acknowledge the need for data governance and most are developing their strategy.

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Topics: Data Governance, Financial Services, FIBO, Data Lake, Anzo

Controlling Data Governance with Semantics

Posted by John Rueter on Apr 14, 2016 2:33:01 PM

Regardless of the ROI of any data-centered solution, upper level management will not support it unless it adheres to governance and security conventions. By definition, data governance formalizes the roles, responsibilities, and rules required for data’s long-term sustainability. Its symbiotic relationship with security ensures that data is protected from the people and practices that negatively affect organizations.

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Topics: Semantics, Data Governance, Compliance

The Three Pitfalls of Data Lakes

Posted by Raj Tekchandani on Jan 28, 2016 3:14:04 PM

Data lakes are no longer anomalies. Consolidating all of an organization’s data—unstructured, semi-structured, and structured—into a single repository for integration, access, and analytics purposes is rapidly emerging as the preferred way to manage big data initiatives.

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Topics: Semantics, Data Management, Data Integration, Big Data, Data Governance, Data Lake, Anzo, Analytics, Smart Data Lake, Unstructured Data

5 Data Lake Capabilites Essential to Business Success

Posted by Marty Loughlin on Aug 31, 2015 9:13:33 AM

Many data lake projects achieve their IT objective: cheap storage of all enterprise data in raw form, but fail in their business objective to deliver value from this data. Why? Because making the data accessible and usable for business users is hard.

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Topics: Smart Data, Data Management, Data Integration, Big Data, Data Governance, Hadoop, Data Lake

Retiring (Your Applications) to the Lake

Posted by Marty Loughlin on Jun 25, 2015 3:27:08 PM

Legacy applications that have exceeded their useful life can be expensive to maintain. They often require specialized skills and old versions of software and hardware to support. But, they can also contain very valuable data that needs to be retained for business or compliance purposes.

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Topics: Smart Data, Semantics, Data Integration, Data Governance, Compliance, Data Lake

Key Regulatory Reporting Challenges Solved by the Smart Data Lake

Posted by Marty Loughlin on Jun 15, 2015 1:19:11 PM

Ernst and Young just released their 2015 Federal Reserve regulatory reporting survey looking at how firms are adapting to the new standards. The survey covered 5 key topics but two stood out as areas where new smart data solutions could add immediate value for the banks: Report Preparation and Data &Technology.

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Topics: Data Management, Data Governance, Compliance

Understanding Smart Data Integration in Just 2 Minutes

Posted by Raj Tekchandani on May 7, 2015 4:11:46 PM

Data integration projects can be time consuming, expensive and difficult to manage.Traditional data integration methods require point to point mapping of source and target systems. This effort typically requires a team of both business SMEs and technology professionals. These mappings are time consuming to create and code and errors in the ETL (Extract, Transform, and Load) process require iterative cycles through the process.

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Topics: Smart Data, CDISC, Semantics, Data Integration, Data Governance, FIBO

Semantic Fix for SOA Chaos

Posted by Marty Loughlin on Jun 17, 2014 9:21:00 PM

Large organizations typically require hundreds of integrations between disparate legacy systems to meet enterprise business requirements. Even with a SOA approach, this complex web of point-to-point integrations can be difficult to govern and manage. The result is a proliferation of overlapping services, poor documentation, limited reuse and a challenging support environment.

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Topics: Smart Data, Semantics, Data Management, Big Data, Data Governance, SOA