The intersection of technical innovation with an ever-increasing competitive business environment are driving organizations to explore new approaches to analytics. Business leaders are now pushed to demand data value chains to acquire raw data, integrate, provide context, and produce predictions and recommendations at the speed of business. To facilitate this effort, computers must not only deliver what is explicitly instructed, but must also leverage innovative techniques and algorithms to perform autonomously. In this blog, Matt Donaghey answers the question, “What is Machine Learning?” in the first of a series on this subset of Artificial Intelligence (AI).
Problem-solving through collaboration is powerful and works especially well when you have the right tools and channels to make it happen. I was recently reminded of just how powerful collaboration at SEI can be when our team was confronted with a complicated problem. A few months ago, an SEI client in Boston approached one of my colleagues to help overhaul a struggling Business Intelligence (BI) program in order to more effectively manage their data. This is not uncommon, as data volume, variety and variability can make data management seem truly daunting.
News of brazen hacking attacks have become commonplace in today’s business environment. As business leaders, how can we understand the cause of these attacks, and how can we protect our company’s most valuable assets? This blog post breaks down the concept of Cybersecurity (also referred to as Information Security) as an introduction for professionals new to this discipline.
Happy New Year!! While we prepare to make 2017 another great year, let’s look back at some of the highlights that made 2016 so memorable for SEI:
We opened a new office in Washington DC!
Continued to hire rockstar consultants into our SEI family!
Added new clients to our growing list of partners!
Increased our involvement within our communities!
Don’t just take my word for it, check out what our consultants have been saying…
Next summer, myself and a group of SEI colleagues from various offices will embark on a journey across Grand Canyon National Park. For many of us, this trip checks off a bucket list item of hiking from one rim of the Grand Canyon to the other in a single day, then turning around and doing it again the next day. For those that are familiar with this hike, it is commonly known as R2R2R (or Rim to Rim to Rim). With a distance of 23.5 miles and a change in elevation of 10,141 feet total, a trip like this requires significant planning and a lot of training.
Our team recently completed a solution assessment document to get project sign-off for a local client. In an effort to consolidate and document our recommendations, we pulled together a group of resources with a variety of business and technical backgrounds. Our primary output was a traditional text document, but we quickly realized we could more effectively communicate our findings with a supporting illustration or model.
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.
A few years ago I picked up a book called Zen and the Art of Motorcycle Maintenance. A friend had recommended it to me with very few details other than, “You would like it.” What I found was not a manual about keeping your cool while tackling tricky motorcycle problems, but a treatise on quality. The author, Robert Pirsig, wove a beautiful tale of a man driven mad attempting to understand “Quality” and his cross country motorcycle road trip towards reconciliation. My friend was right, and I started thinking about how quality impacted everything. How we evaluate things and make decisions is all built on the foundation of Quality.
Data provides us with powerful opportunities to tell a story. With the advances in data visualization tools over the recent years, the possibilities are truly endless. However, with the vast quantities of data at our immediate disposal, storytelling with data has become more difficult as authors try to do too much or lose focus on their original intent.
Over the years, I have created countless reports for dozens of clients. In some cases the data was limited and straight-forward, while other times it was large and extremely complex. In either case, it was my job to tell a story with the data at hand through education and engagement. What’s helped me tell these stories is following the guiding principles below: