Have you ever wondered what it takes to work in data and analytics? What background should you have? What skills or technical expertise? How do you know if you’ll like the work?
Whether you’re outside the field looking in or inside looking for a new challenge, you’ll benefit from our new Q&A series. 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.
Continue reading Dip Kharod on How to Become a Big Data Architect: “You have to love data”
Data Quality Management (DQM) is a major concern in most data-driven organizations. But many organizations are challenged with improving and remediating data quality. From the beginning, they struggle with questions that impede the progress of their data quality efforts.
For example: What is DQM? How do I get started? Do I even need it? What metrics should I use? What data quality rules should I define? Continue reading White Paper: How to Build a Successful DQM Program
In case you missed it, the NJ Big Data Palooza MeetUp group held its first event last Wednesday, September 17. This awesome MeetUp focused on the topic of stream processing with Apache Kafka.
Continue reading Slides: Stream Processing with Big Data/Apache Kafka
We are extremely pleased to announce our Silver sponsorship at the upcoming MDM & Data Governance Summit. This event, happening at the Sheraton Times Square in New York City October 5-7, is in its ninth year and is geared at helping you understand how to engage your business processes and reach beyond the IT department. Our own Chris Blotto, Informationist, will be presenting “MDM in a Big Data World” at the Industry Innovation Lunch on Monday, October 6, from 11:45am to 12:45pm. Continue reading Don’t Miss Knowledgent at the MDM & Data Governance Summit October 5-7
Whether mandated by regulatory considerations, driven by executive dashboards, or meant to enable personalized targeting of marketing messages to consumers, the rapidly increasing reliance on analytics has made Data Quality a higher priority than ever before. In turn, this new status has reshaped the very meaning of Data Quality. There was a time when Data Quality really meant one thing: a simple, binary assessment of the accuracy of data. That was the beginning and end of the Data Quality discussion. Today, however, the questions have grown more complex.
From “Is my data correct?” to “What does my data actually mean?,” the questions surrounding Data Quality are undergoing a rapid transformation. This change has been driven by four major factors:
Continue reading 4 Factors Driving Data Quality Transformation
This past Sunday, the Knowledgent Riding Club, a group of cycling enthusiasts that include Knowledgent employees, their families, and clients, took part in Century for the Cure, a charity bike ride to benefit cancer research at the Cancer Institute of New Jersey.
Participating in the Century for the Cure was very inspirational. I rode 62 miles with Shail Jain, our CEO, and our team captain Nathan Lynn did a great job keeping the team motivated and challenging us to raise funds for such a great cause. Many family members and friends joined us. Continue reading Bikes, Barbecue, and Bagpipes at the Century for the Cure Ride
Data Quality Management, or DQM, is an important component of data management. However, many Data Owners and Data Stewards stumble when it comes to implementing DQM successfully. Should you profile data or define data quality first? What can you use to report data quality metrics? What should you do if data quality issues arise when you’re trying to make improvements based on your initial assessment?
Continue reading Infographic: Critical Steps to Successful Data Quality Management
“How can clients trust us with their money when we can’t even get their name right?” With statements like this, the business stakeholders at our client, a major financial services organization, expressed their frustration with the lack of consistency, accuracy, and completeness of their data.
Continue reading Case Study: How a Financial Services Organization Operationalized DQM to Support Data Governance
The Data Lake, a next-generation data storage and management solution, was developed to meet the ever-evolving needs of increasingly savvy users. However, building a successful Data Lake is often a cause for concern among business and IT users alike. Why do you need a Data Lake? What does the architecture look like? What elements are necessary? Continue reading White Paper: How to Design a Successful Data Lake