SEI partnered with EVERFI, an education and training platform, to create a webinar that provides insights into streamlining efforts to enable a competitive GDPR compliance strategy.
Today’s IT data investment dollars are often funneled into the creation of new advanced analytics capabilities: big data integration, machine learning, and data science solutions. In this blog, Stephen Galloway explains why these initiatives will fail unless the challenge of data accessibility is addressed alongside these complementary initiatives.
Companies still have a lot of work to do to ensure they are GDPR compliant. However, like any company that is facing a regulatory change that impacts their core processes, there are significant challenges to success. In this blog, Stephen Smith, Patricia Brady, Jeff Francis and Matt Conner explain a few of the key challenges and how to address them.
Last fall, St. Aloysius, an organization and school that provides psychiatric services to Cincinnati youth, faced a big problem; the “no-show” rate for their programs was almost as high as 30%. In this blog, Lauren McDonald explains how a group of SEI-Cincinnati consultants and the dedicated staff members of St. Aloysius partnered together to solve this problem during a “data-hack-a-thon.”
From predictively queuing a show for a video subscriber to watch next to mapping the building numbers for every street in France, Machine Learning offers a viable solution. But where do you start? In this blog, Sara Showalter explains why framing the problem and selecting the right team are critical to success.
With marketplaces rapidly evolving, the data required to support decision making processes are changing at an equally breakneck pace. Is your technology delivery pipeline equipped to support the data demands of your business stakeholders? In this blog, Stephen Galloway explores how to overcome the disconnect between an organization’s data strategy and the tactical delivery capabilities that support it.
In a previous blog, Storytelling with Data, my colleague offered some great tips on keeping a presentation focused on the data. Unfortunately, the data doesn’t always seem to tell a coherent story. Maybe the principal developer is gone, the documentation is stale, or perhaps the subject matter experts each have their own swirling interpretations of the content. What now? Analytical opportunity is knocking and it’s time for data discovery! There are challenges with any data discovery initiative, however. To help you overcome these challenges and improve your analytical skills, I offer several tips to help you through the data discovery process.
My colleagues and I were recently presented with an interesting challenge while building out a business intelligence solution for a mid-size client. The client wanted to archive, for potential future use, a large amount of data over and above the current reporting requirements. Unfortunately, the client’s proprietary database system was primarily designed for data analysis, not storage, and could not be leveraged as a solution. To identify a solution, our collaboration focused on evaluating options made possible through significant changes in the data analytics field. As we discussed the new technologies and methodologies, I found myself drawing parallels to how Apple Macintosh, in 1984, brought computing power from the mainframe to the masses.
I’m often approached by clients, colleagues, and friends asking: “What’s this Big Data/NoSQL thing I keep hearing about?” Oftentimes they’ve been told that these are the buzzwords that will help them get answers from the plethora of data out there. However, like any tool, it won’t help solve a problem unless used effectively. As an SEI consultant, I’ve often worked with my clients to help evaluate if Big Data and the other trendy buzzwords in tech could actually help solve their problems. At a recent Data Management forum, I presented information on the different uses for newer Data Management technologies, like Big Data and NoSQL, when compared to traditional relational solutions.