The Smart Data Blog

Raj Tekchandani

Recent Posts

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

Cambridge Semantics' Richard Mallah at NIPS 2015

Posted by Raj Tekchandani on Dec 17, 2015 12:53:36 PM

Richard Mallah, our Director of Advanced Analytics recently participated at the The Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), focused on machine learning and computational neuroscience. Richard was a panelist at the symposium higlighting The Societal Impacts of Machine Learning. This symposium aimed to turn the attention of Machine Learning researchers to the present and future consequences of their work, particularly in the areas of privacy, military robotics, employment, and liability.

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Topics: Text Analytics, Machine Learning, Cognitive Computing, Artificial Intelligence, Unstructured Data

In Case You Missed It: On-demand Webinar on Smart Data Lakes

Posted by Raj Tekchandani on Sep 28, 2015 5:58:04 PM

We had a record attendance at our webinar, Smart Data Lakes - Game Changing, Graph-Based Data Discovery, Analytics and Governance for the Enterprise. Presenters Ben Szekely, Marty Loughlin and Jim LaPointe talked about how the Anzo Smart Data Lake™ delivers unprecedented data value, turning data assets into extreme insight and competitive advantage for companies in Pharmaceuticals, Life Sciences, Financial Services and many other industries. The webinar also included a demostration. In case you missed it - you can watch the recording here

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Topics: Smart Data, Big Data, Graph, NoSQL, Analytics

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

Big Data Industry News Watch

Posted by Raj Tekchandani on Jan 23, 2015 10:00:00 AM

A round up of recent industry news on the topics of Big Data and Enterprise Data Management

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Topics: Smart Data, Data Management, Data Integration, Big Data

Putting the Smarts in Data Integration

Posted by Raj Tekchandani on Jan 20, 2015 3:47:00 PM

Driving business value from your data often requires integration across many sources. These integration projects can be time consuming, expensive and difficult to manage. Any short cuts can compromise on quality and reuse. In many industries, non-compliance with data governance rules can put you firm’s reputation at risk and expose you to large fines.

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Topics: Smart Data, Semantics, Data Management, Data Integration, Big Data

2014 - What a year!

Posted by Raj Tekchandani on Jan 15, 2015 2:03:00 PM

Happy New Year !!

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Topics: Smart Data, Data Management, Data Integration, Big Data, Semantic Web

Adding Clarity to Big Data Analytics

Posted by Raj Tekchandani on Dec 22, 2014 11:46:00 AM

We recently announced the integration of our Anzo Smart Data Platform (Anzo SDP) with the KeyLines network visualization tool. The advancement will enable business analysts and IT professionals in Global 2000 companies to gain new “big picture” business insights from their big data queries on diverse data.

The combination of Anzo SDP and Cambridge Intelligence’s KeyLines solution will especially benefit use cases in pharma and financial services including: 

  • Drug discovery – The ability to visualize the connection between research and results in this heavily data-driven process can help avoid effort duplication, identify gaps in understanding, and ensure discovery is more cost efficient.
  • Clinical trials – Ensuring a conclusive trial is key to any drug’s success. Network visualization techniques can help find potential participants with the required profiles, analyze trial results and ensure overall vigilance.
  • Compliance Surveillance – Link and visualize activities, web logs, email and phone archives, IM communications, and other sources to uncover potential violations of regulatory requirements as well as internal policies and procedures violations.
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Topics: Smart Data, Data Management, Big Data, Key Lines

Simplify Clinical Trials Metadata Management with Semantic Technology

Posted by Raj Tekchandani on Dec 17, 2014 11:46:00 AM

The FDA has adopted the CDISC SDTM standard for clinical trial submission. While the standard has the potential to simplify the reporting processes, adoption poses challenges to and raises questions for pharma companies testing their medicines in the clinic. In response, organizations have tasked groups with managing clinical trial metadata in compliances with these standards.

We at Cambridge Semantics have been working with these groups to address these challenges with Anzo Pharma SmartBench, a user-driven platform for developing flexible data collaboration, integration and analytics solutions. Anzo Pharma SmartBench is based on Semantic Web Technology – the same standards used by CDISC to represent SDTM.

Anzo Pharma SmartBench features –

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Topics: CDISC, Metadata, Semantics, Data Management, Clincal Trials

Semantic Technology – the Catalyst to Making Big Data Smarter

Posted by Raj Tekchandani on Oct 15, 2014 9:51:00 AM

We recently contributed an article to CMS Wire, a popular web magazine focused on information management and other enterprise issues.

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