Alyssa Farrell leads industry marketing for the SAS Global Health and Life Sciences Practice. In this role, she focuses on the SAS solutions that help optimize health outcomes for individuals and their communities. Alyssa is actively engaged in analyst relations, market research and influencer marketing to stay on top of industry trends and align SAS capabilities to customer needs. She has also supported the global energy and public sector teams during her career at SAS. Prior to joining SAS in 2004, Alyssa was a senior consultant in the Deloitte Public Sector practice. She earned her MBA degree with a concentration in Management Information Systems from the University of Arizona. She also holds a Bachelor of Arts degree with honors from Duke University. Follow Alyssa on Twitter @alyssa_farrell and LinkedIn at http://LinkedIn.com/in/alyssafarrell
Inequities in cancer care cause specific populations in the U.S. and worldwide to bear a more significant burden of disease than the general population, based upon barriers. These barriers to prevention and care have long existed but were undeniably exacerbated by the COVID-19 pandemic. February 4 marks World Cancer Day, which
Predictive models and medical image analysis have the potential to transform health care delivery. To accelerate innovative approaches in health analytics, teams around the world are participating in a global hackathon. In scientific communities, it's well-known that the fastest way to drive progress and discover new ideas is collaboration with
Fraud, waste and abuse (FWA) ravages the US health care system. Estimates from the National Health Care Anti-Fraud Association show fraud costs health care organizations $70 billion to $230 billion each year. The precise figure is unknowable because only 3 to 10% of this fraud is ever detected. With more
How can organizations be more resilient during an uncertain business and public health situation? Lessons learned from organizations at the front lines of crisis response reinforce the importance of good analytic governance. These leading organizations have shown us that the fundamentals of the analytic life cycle – data, discovery and
What do two health systems, 4000 miles apart (6600 kilometers) have in common in their COVID-19 response? Despite different policy responses to the pandemic, they are more alike than you might think. Brought together by SAS and the FutureNHS COVID-19 analytics huddle, these two health systems recently shared their experience
What’s that buzz on the power line? It’s AI, of course! But what is AIoT, you ask? It is applying artificial intelligence (AI) to data from smart devices connected by the Internet of Things (IoT). AI is the science of training systems to perform human tasks through learning and automation.
You’ve probably heard the stats about the number of internet-connected devices, which make up the Internet of Things (IoT). Most likely, you’re part of the narrative. We're all connected in new ways and through more devices than ever before. Sometimes IoT impacts our daily lives – safety sensors on cars,
Widespread flooding. Downed power lines. Broken poles. Failed transformers and breakers. Leaking pipes. Shut down refineries. The toll that a catastrophic storm takes on the energy value chain is significant. A functioning power grid, clean water and reliable fuel sources are critical for public safety and economic recovery. Even with
Utilities can save as much as $10 million a year for every one percent improvement in forecast accuracy by optimizing asset utilization and trading strategies. Changing energy markets and an influx of data from the smart grid are providing more opportunities to reap value from energy forecasting. Improving energy forecasts
Tell me if you’ve heard this before: Your company hired (or re-titled) a talented data scientist and they have great skills and no data. Or they're marginalized by IT because they're misunderstood. They're offered “cleansed” data that will fit into the hardware provisioned. What they want is “all” relevant data
The utility industry is ripe with analytic opportunities. Usually, when I share that perspective, thoughts immediately jump to making the energy grid more efficient. Certainly, there are efficiencies to gain with improved data about where power is being consumed and the sources that are available to support it. However, there
Energy suppliers are fighting for prime position in the domestic energy supply market. Disillusioned customers, increased competition due to a flood of new entrants and tighter regulations are forcing suppliers to reassess their business models. According to UK regulator Ofgem, there were 3.8 million account switches in the first six
Research indicates that IoT and Machine Learning are more valuable to utilities when used in combination but there are hurdles to overcome first. Machine learning and IoT will enable utilities to better realize the next generation of the power grid: a distributed system with power flows among millions of things
The Internet of Things (IoT), sensors and connected devices are all the buzz leading up to DistribuTECH (Feb 9-11), so I sat down with one of our IoT specialists, Lorry Hardt to learn more. Lorry will be at DistribuTECH to support our joint demonstration with Intel and gave me the
Operations technology (OT), such as control systems, are crucial elements in our daily lives. They make the stoplights function at intersections. They guide robots with precise movements on the shop floor. Their switches and routers are the backbone of our internet. But what if they were even more intelligent? What
Whether it’s a smart water meter or a complex combustion turbine, expectations of reliability for complex, connected machines have increased across the board. Due to higher visibility and the availability of advanced analytics, companies know they should be able to identify emerging safety and reliability issues in these assets. By
As utilities expand analytic capabilities into more areas of the business, the reality of the data management challenge becomes very real. Most have accepted the era of "big data." But what about the quality of that big data? Is it reliable? What about the governance? Have the processes changed since
Whether it’s to reduce churn in competitive markets or to elevate customer satisfaction rankings in regulated markets, customer analytics is hot right now in utilities. However, the complexity that utilities have built into their processes and technologies over the past decades makes customer analytics a more challenging issue to tackle
Oil companies are being forced to explore in geologically complex and remote areas to exploit more unconventional hydrocarbon deposits. New engineering technology has pushed the envelope of previous upstream experience. No guidebook existed on how computing methodologies can contribute to E&P performance at reduced risk. Until now. A new book
I was asked to speak recently on a topic that includes two hyped terms: Big data and sustainability. At the risk of igniting an anti-buzzword campaign, I added a third over-used term to that list: analytics. Even though individuals and companies use those three words – big data, sustainability, and
