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.
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.
Last month, SEI’s Matt Walton, PhD and John Halamka, MD, CIO of Beth Israel Deaconess Medical Center (BIDMC), co-presented a technical and operations overview of BIDMC’s ICD-10 program at Massachusetts Health Data Consortium’s (MHDC) ICD-10 conference held on March 10, 2014.
Speaking at the conference, Dr. Halamka said that the technology itself, moving from ICD-9 codes to ICD-10 codes is mechanical and he is not worried about that. What he is concerned about, is getting meaningful data into that new ICD-10 code and actually being audit-proof. He added, “this will be a bounty hunter’s delight as they find a disconnect between what was actually documented and what was coded. That’s the real technology dilemma.”
For any company of sufficient size and complexity, managing changes to both systems and processes requires a tentacle understanding of the interrelated components and partners involved. Many leaders default to the assumption that major business disruptions or innovations require an army of experts to map and plan for the change, a task frequently handed over to large teams from outside consulting firms. Is this really necessary?
Rising out of the Big Data movement and dubbed the “Sexiest Job in the Next Decade”, Data Scientists are in hot demand. New online courses and university programs are popping up to remedy the looming skills shortage. Big Data conferences are filled with professionals wanting to discover the magical path to the next golden professional. Akin to Big Data, the purpose of Data Science is not well understood.
Today the word “Data” is used in everyday jargon within businesses of all sizes. With the emergence of social media, applications over the internet and telecommunications, the world is generating unprecedented amounts of data. This is in addition to the ever increasing volume of internal data our organizations are generating. Everyone wants to get their hands on more data, with a feverish desire to enable better and better insights and answers to business questions.
Big Data is polarizing. The concept can elicit both overzealous enthusiasm and pointed disdain depending on the audience. Recent trends in the technology press show a strong backlash to counter the hype over the last few years. As with most developing trends, both camps have valid points but the practical approach lies somewhere in the middle.