Siphiwe  Nair

Siphiwe Nair

1622563680

For Digital Healthcare, Too Much Data, Not Enough Context

Delivering quality care digitally means having the platforms and tools that can sift through enormous volumes of data to rapidly identify issues.

Digital healthcare is undergoing a revolution. And it has a problem many other industries only wish they had. The issue can be summarized by the following:

Doctor to patient: “I see the problem. You’re generating too much data.”

That funny-but-true line was delivered by Dr. Daniel Kraft, founder of the Exponential Medicine Conference and a professor at Singularity University, in a recent discussion with CXOTalk’s Michael Krigsman. The challenge, Kraft points out, is there is no shortage of data available to healthcare practitioners, but not enough context. The ideal digital healthcare platform, he says, should “learn from the clinician experience around the world, and synthesize the data into its actionable components,” he explains. “No one wants to see the raw EKG data, blood pressure, or other elements. What does it mean in context and even normalized to that individual? There are lots of layers to it. We’re starting to see the dots connect.”

Kraft was joined by Dr. John Halamka, president of Mayo Clinic Platform, who pointed to the need to align health systems and data in real time to deliver the best outcomes. He pointed to radiation oncology or radiotherapy, in which therapy needs to aligned with a linear accelerator “that needs to be programmed by a physicist and an expert radiation oncologist. It takes six-plus hours of human time to review the films of the tumor and then program the linear accelerator.”

With real-time systems and data alignment, much of this delay could be eliminated, Halamka continued. “What if one developed a cloud-hosted mechanism to ingest images of tumors, AI algorithms that would be able to review those and, in literally near real-time, recommended the safest, lowest dose, most effective mechanism of delivering the radiation therapy to the patient and then auto-programmed a linear accelerator thousands of miles away without a radiation oncologist or a physicist nearby?”

Work is underway on that problem, introducing platforms that are “connecting incoming data and algorithms, delivering something of value back and, ultimately, improving patient care,” Halamka continues. “Broadly, platforms are connecting producers and consumers and building value.”

#artificial intelligence technologies #big data #big data analysis tools #big data platforms #healthcare #industry insights #trending now #digital transformation #smart devices

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For Digital Healthcare, Too Much Data, Not Enough Context
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Siphiwe  Nair

Siphiwe Nair

1622563680

For Digital Healthcare, Too Much Data, Not Enough Context

Delivering quality care digitally means having the platforms and tools that can sift through enormous volumes of data to rapidly identify issues.

Digital healthcare is undergoing a revolution. And it has a problem many other industries only wish they had. The issue can be summarized by the following:

Doctor to patient: “I see the problem. You’re generating too much data.”

That funny-but-true line was delivered by Dr. Daniel Kraft, founder of the Exponential Medicine Conference and a professor at Singularity University, in a recent discussion with CXOTalk’s Michael Krigsman. The challenge, Kraft points out, is there is no shortage of data available to healthcare practitioners, but not enough context. The ideal digital healthcare platform, he says, should “learn from the clinician experience around the world, and synthesize the data into its actionable components,” he explains. “No one wants to see the raw EKG data, blood pressure, or other elements. What does it mean in context and even normalized to that individual? There are lots of layers to it. We’re starting to see the dots connect.”

Kraft was joined by Dr. John Halamka, president of Mayo Clinic Platform, who pointed to the need to align health systems and data in real time to deliver the best outcomes. He pointed to radiation oncology or radiotherapy, in which therapy needs to aligned with a linear accelerator “that needs to be programmed by a physicist and an expert radiation oncologist. It takes six-plus hours of human time to review the films of the tumor and then program the linear accelerator.”

With real-time systems and data alignment, much of this delay could be eliminated, Halamka continued. “What if one developed a cloud-hosted mechanism to ingest images of tumors, AI algorithms that would be able to review those and, in literally near real-time, recommended the safest, lowest dose, most effective mechanism of delivering the radiation therapy to the patient and then auto-programmed a linear accelerator thousands of miles away without a radiation oncologist or a physicist nearby?”

Work is underway on that problem, introducing platforms that are “connecting incoming data and algorithms, delivering something of value back and, ultimately, improving patient care,” Halamka continues. “Broadly, platforms are connecting producers and consumers and building value.”

#artificial intelligence technologies #big data #big data analysis tools #big data platforms #healthcare #industry insights #trending now #digital transformation #smart devices

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Siphiwe  Nair

Siphiwe Nair

1622516462

What is Big Data in Healthcare and How is it Used?

The pandemic is having an enormous impact on the healthcare sector. Between overwhelming hospitalization rates, intensifying cybersecurity threats, and an aggravating number of mental illnesses due to strict lockdown measures, hospitals are desperately searching for help. Big data in healthcare seems like a viable solution. It can proactively provide meaningful, up-to-date information enabling clinics to address pressing issues and prepare for what’s coming.

Hospitals are increasingly turning to big data development service providers to make sense of their operational data. According to Healthcare Weekly, the global big data market in the healthcare industry is expected to reach $34.3 billion by 2022, growing at a CAGR of 22.1%.

So, what is the role of big data analytics in healthcare? Which challenges to expect? And how to set yourself up for success?

How Big Data Can Help Solve Healthcare Problems

Big data has several accepted definitions. Here are two popular ones:

Douglas Laney’s definition. Laney is a former Chief Data Officer at Gartner. He states that big data is characterized by 3 Vs: volume, velocity, and variety. The volume stands for large amounts of data. Velocity refers to the speed of collecting data and making it accessible, while variety indicates the different types of data, such as text, video, logs, audio, etc.McKinsey’s definition. The renowned consulting firm defines big data as datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.

According to an IDC report, the volume of big data is expected to reach 175 Zettabytes by 2025. To put it in perspective, it will take 1.8 billion years to download this amount of data with the average internet speed available nowadays.

#big-data #big-data-analytics #healthcare-and-big-data #healthcare-tech #medical-software-development #healthcare-software #big-data-processing #healthcare-software-solution

Cyrus  Kreiger

Cyrus Kreiger

1618039260

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt