Editor’s Note: This post from Alex Beals, rockstar big data intern at Knowledgent, shares his experiences and the important lessons he learned during his time here.
As the last summer before college, and my freshman year at Dartmouth College, comes to a close, it’s the perfect time to think about the wonderful opportunity I had to work at Knowledgent as an intern. This was the first time I was able to get real-world experience in the field of computer science (what I’m planning on majoring in), and it allowed me to hone my skills while being able to contribute on a team and further the company. Continue reading Contributing in a Real-World Environment: Three Takeaways from My Internship
Depending on your perspective and what you are trying to achieve, “quality” takes on different meanings. That being said, most people would probably agree that data quality relates to some degree of “correctness”. However, data can be correct in different ways.
So, what is “quality”? Many might say that quality is having the correct information, but that is only part of the data quality story. I like to think of quality as a three-legged stool, where each “leg” or characteristic is critical in supporting the overall structure.
What’s in a name? Everything.
A name is an idea. It carries with it many dimensions of meaning that shape understanding. So why do we continue to speak in the clumsy and imprecise parlance of the day, embracing such emergent monikers as “big data“? (Wasn’t data always big? Isn’t it the economics of using that data that have favorably changed?) Simply put, the trends are racing in so many directions, changing the data landscape so quickly, that in order to keep up, we must employ the language being used, lest we fall short of the productive connections we seek to make. Continue reading What’s in a name? DAaaS is more than semantics.
Editor’s Note: This post from Amanda Sullivan, rockstar intern at Knowledgent, shares her experiences and the important lessons she learned during her time here.
I started my internship with Knowledgent following my freshman year at Rowan University. With my studies focusing on Business Administration and Communications, this summer I was given the opportunity to experience the corporate field first hand. I wanted exposure to the industry I plan on pursuing later in life.
OODT (Object Oriented Data Technology) is an open-source, data management framework currently distributed by Apache. Originally from NASA’s Jet Propulsion Laboratory, this component-oriented software was developed to focus on access to science data repositories and data generation and capture.
We sat down with Arpan Bhattacharya, Big Data Engineer here at Knowledgent, to pick his brain on the advantages of using OODT: Continue reading Q&A: Arpan Bhattacharya on the Advantages of OODT
Our client, a multinational financial services corporation, was already well-versed in traditional analytics and had recently invested significant time, money, and resources in new hardware and software to enable advanced big data analytics capabilities. This high-profile investment was intended to provide short-term benefits and also to be built up in the future.
However, to fully realize the potential of this investment and the resulting capabilities, our client had several parallel and driving priorities: to update and streamline processes, to clarify and define organizational roles and processes, and to enhance and expand the advanced analytics team’s skill sets in relation to the new hardware and software.
How does business process management (BPM) relate to service-oriented architecture (SOA)? Our own Frank Teti, Senior Consultant in Healthcare at Knowledgent, recently published an article for Software Magazine covering this very subject.
Every year, Knowledgent holds a summer party for team members located in the NY/NJ area. This year’s theme? Bowling.
Strategic use of electronic patient health records provided by third parties is trending now in the pharmaceutical industry, particularly as a way to derive real-world insights and clinical outcomes. However, traditional relational databases often cannot handle these huge, unstandardized, and unstructured data sets.