Q&A with Peter Gibson on the Big Data Technology Landscape

Fresh off Knowledgent’s recognition as a top Big Data solution provider, we sat down this week with Peter Gibson, our co-CEO, to discuss current trends in the Big Data technology landscape:

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INFOGRAPHIC: Anatomy of a Managed Information Object

The data lake, a data-centered architecture featuring a repository capable of storing vast quantities of data in various formats, has emerged among IT organizations as a solution to the challenges with existing enterprise data architectures. In the data lake, data itself is no longer restrained by initial schema decisions and can be exploited more freely by the enterprise.

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5 Best Practices for Applying MDM to Non-Traditional Data

The concept that, more than any other variable, has put the “big” in Big Data, has to be the notion of uncontexted, unstructured, or non-traditional data and the potential it represents.   The term “non-traditional” when applied to data generally refers to data that does not easily lend itself to be captured in spreadsheets, tables, or relational databases. Some examples of this type of data include non-relational database data, such as documents, email, instant messaging(IM)/texting, and sensor data, and “signal” data like blogs and social media.

But non-traditional data typically can’t be captured with the same old tools or analyzed with the same old methods. Applying MDM to non-traditional data raises a different set of challenges than when dealing with traditional data. Although you will be asking some of the same questions as you would with traditional data, you may need to use a different approach or involve a completely new perspective to realize Big Data’s potential. Continue reading 5 Best Practices for Applying MDM to Non-Traditional Data

Deterministic versus Probabilistic Matching in Big Data

“Information is the new oil” is the latest trend, and like oil, crude data needs to be refined before it can be consumed. In other words, having big data won’t serve any purpose unless the data is good enough to be useful. With the potential for mismatching, duplication, and other quality threats from ingesting data across disparate sources, ensuring the accuracy and quality of data is more important than ever.

This is where big data meets Master Data Management (MDM). Based on the concept of “better to be safe than sorry,” MDM users can apply data matching techniques to resolve some data quality conflicts. Applying these techniques enables users to determine the data that is “most likely” to be correct, and if not perfect, at least at a “Fit to Purpose” level of quality. This post discusses two matching techniques, Deterministic Matching and Probabilistic, or “Fuzzy,” Matching, in the context of big data. Continue reading Deterministic versus Probabilistic Matching in Big Data

Chris Kratochvil on How to Become a Program Manager

Want to know what it takes to work in data and analytics? Every other Friday, our own Informationists will share their thoughts, experiences, and advice on what they do and what they did to get there. Expect to see a wide range of answers from individuals in the same lines of work; our Informationists come from all walks of life, which only shows that there’s more than one way to get on the right career track.

Following our last post in this series on Reed Bradford, Data Quality Architect, this week’s post features Chris Kratochvil, Program Manager. Chris earned his BA in Art and Computer Graphics from Lycoming College. He has worked in the NYC metropolitan area as well as areas of Germany and Ukraine across the pharmaceutical, biotechnology, and clinical research industries.

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Case Study: How a Healthcare Organization Utilized a Next-Generation Data and Analytics Platform to Improve Patient Care and Lower Costs

Challenges

Our client, a major healthcare organization, was experiencing difficulties with their decentralized approach to data management. Insufficient governance processes and a proliferation of independent data assets made it time-consuming and frustrating for users to find and trust the data they needed. Continue reading Case Study: How a Healthcare Organization Utilized a Next-Generation Data and Analytics Platform to Improve Patient Care and Lower Costs

3 Takeaways from the 2014 MDM & Data Governance Summit

We hope you had an opportunity to visit NYC and the Knowledgent booth at the MDM & Data Governance Summit last week on October 5 at the Sheraton Times Square.  The MDM practitioners we spoke with found the sessions valuable and insightful. We particularly enjoyed the engaging discussions we had after our CTO Chris Blotto’s talk on “MDM in a Big Data World”.

This was the ninth year of the summit, and it’s clear that MDM is still a topic of wide interest and only becoming more relevant in a world where massive amounts of data are now available to the business.

Here are our top three takeaways from the event: Continue reading 3 Takeaways from the 2014 MDM & Data Governance Summit

Knowledgent Recognized as One of 2014’s 20 Most Powerful Big Data Solution Providers

We are extremely humbled to announce that Knowledgent has been named among CIO Story’s 20 Most Powerful Big Data Solution Providers for 2014. This technology industry publication focuses on innovative solutions developed by upcoming vendors for enterprises. This year’s Big Data Special feature highlights the top 20 big data companies that are on the leading edge of driving solutions to big data technology challenges and impacting the industry. Continue reading Knowledgent Recognized as One of 2014’s 20 Most Powerful Big Data Solution Providers

Reed Bradford on How to Become a Data Quality Architect

Want to know what it takes to work in data and analytics? Every other Friday, our own Informationists will share their thoughts, experiences, and advice on what they do and what they did to get there. Expect to see a wide range of answers from individuals in the same lines of work; our Informationists come from all walks of life, which only shows that there’s more than one way to get on the right career track.

Our first post featured Dip Kharod, Big Data Architect. Continuing the series is Reed Bradford, Data Quality Architect. Reed earned his MBA in Computer & Information Sciences from Temple University and his BS in Information Management from Brigham Young University (BYU).  He has worked in information management and analytics for 28 years across the financial services, life sciences, hospitality, manufacturing, and retail industries.

Continue reading Reed Bradford on How to Become a Data Quality Architect