Ed. Note: Our own Harj Dhillon shares his thoughts on Informatica World Tour in Boston below.
It was a full house. Over 100 people attended the Informatica World Tour city stop in Boston on November 19th, where Knowledgent Informationist, Chris Blotto, keynoted a session on “MDM in a Big Data World.”
The objective of the session was to educate business and IT practitioners on top-of-mind topics, such as data lakes, security, cloud adaption, and MDM. Our partner, Informatica, shares a common vision in viewing data as valuable enterprise asset, with their product suite purpose-built to move data quickly, securely, and with traceability to the end user.
Continue reading MDM in a Big Data World – ON TOUR
“How can we use the technologies we may already have to help our users find information?”
This is a question we’ve been hearing from several of our clients, many of whom are assessing big data and advanced analytic tools and technologies in the hope of using them to enable data-driven decision making. In many cases, they have moved beyond the stage of educating themselves about the strategic benefits of big data to implementing foundational data lakes and analytic sandboxes. Continue reading How MDM Enables Data-Driven Decision Making in a Big Data World
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.
Continue reading INFOGRAPHIC: Anatomy of a Managed Information Object
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
“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
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 Informationist 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
Our Informationist, Chris Blotto, presented at the MDM & Data Governance Summit on Monday, October 6 during the Industry Innovation Lunch.
Continue reading Recap: MDM in a Big Data World
In the era of Big Data, what role does Master Data Management (MDM) play? Our own Chris Blotto, Informationist at Knowledgent, addressed this question at the MDM & Data Governance Summit yesterday at the Industry Innovation Lunch.
Chris’ presentation, “MDM in a Big Data World,” covered a range of topics concerning MDM and Data Governance professionals navigating the Big Data landscape. He discussed using MDM capabilities to enable search and navigation in distributed environments, transitioning existing data management investments into the Big Data world, and distinguishing the hype from the reality through real-world use case overviews.
After his presentation, we sat down with Chris to get his thoughts on the role of MDM in today’s analytically driven environment:
Continue reading Q&A with Chris Blotto on MDM, Big Data, and the Data Lake
Knowledgent strongly believes all companies have the data required to know their customers a whole lot better. But who out there is developing the necessary customer insights to make an impact on their business? Is Big Data the solution to developing some of those insights? The hype tells us it’ll be just that. But will Big Data play all by itself at the deep end of the pool, or will it need a helping hand? The clear answer is that synergistic technologies are the key.
Continue reading Big Data Enrichment with MDM
Many organizations approach Master Data Management (MDM) by taking an inventory of the data in their source systems, defining policies to improve the quality and usefulness of that data, and building a consolidated hub of that data. This “build it and they will come” approach too often results in an MDM hub that fails to meet the needs of the processes that want to consume that data.
Continue reading How Knowledgent Approaches MDM