Edna  Bernhard

Edna Bernhard


FRESH DATA: AI Can Help Improve Alzheimer’s Diagnosis

UK study reveals that Artificial Intelligence can help healthcare providers diagnose Alzheimer’s earlier and improve patient prognosis.

Due to people living longer than ever before, the number of people diagnosed with a neurodegenerative disease like Alzheimer’s or Parkinson’s is expected to rise to 115 million by 2050. This will pose a challenge for the healthcare system as they struggle to treat and support these patients. Scientists at the University of Sheffield’s Neuroscience Institute have been studying how the use of artificial intelligence and machine learning in health care can help reduce the time and cost of these diseases, which cause memory loss and cognitive decline, place on hospitals and other medical facilities.

See also: IoMT Devices Will Revolutionize HealthTech in 2020

“It is too early to talk about the outcomes in terms of treatments but, in this study, we examined how machine learning methods can be used to identify the best course of treatment for patients based on their disease progression or how it could be used to identify new therapeutic targets and drugs,” said Monika Myszczynska, a scientist from the University of Sheffield.

#artificial intelligence technologies #news #alzheimer's #healthcare #parkinson's #university of sheffield

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FRESH DATA: AI Can Help Improve Alzheimer’s Diagnosis
Siphiwe  Nair

Siphiwe Nair


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

Gerhard  Brink

Gerhard Brink


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.


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


AI-Enabled Monitoring Can Help Solve Data Storage Issues

AI data storage systems can recognize patterns in arrays and stacks to predict storage issues and help solve them.

With many sources of big data and an increasing volume of available data for enterprises_, _storage capacity planning has become an issue for storage administrators. According to an estimate, 2.5 quintillion bytes of data are generated every day. Now that’s a huge amount of data — equal to 250 million human brains if counted in neurons. And, the same estimate suggests that 90% of the total world data was generated from 2016 to 2018.

It can be simply put that more and more data is generated every day, and with that is increasing the scale and complexity of storage workloads. However, AI can come to the rescue of storage administrators, helping them to store and manage data efficiently. By using AI data storage, vendors and businesses can take storage management to the next level. And, storage administrators can find a solution to the metrics they are currently struggling to manage.

#ai & machine learning #artificial intelligence #big data #data storage technologies #storage administrators #ai-enabled monitoring can help solve data storage issues

Cyrus  Kreiger

Cyrus Kreiger


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

Analyzing Data From U.S. Road Accidents With Data Visualization

Every 24 seconds, a life is lost on the road, and it costs countries around 3% of their gross domestic product - World Health Organization.

With a fatality rate of 12.3% per 100,000 inhabitants, traffic accidents are a leading cause of death in the United States. In 2019, it was reported that 36,096 lives were lost on U.S. roads and according to the National Highway Traffic System Administration (NHTSA), it costs about $871 billion annually to the U.S. economy.

In this article, we would be analyzing data related to US road accidents, which can be utilized to study accident-prone locations and also helps understand the factors that influence road fatalities in the United States.

“Having access to accurate and updated information about the current road situation enables drivers, pedestrians, and passengers to make informed road safety decisions.”

- Association For Safe International Road Travel.

#data-science #big-data-analytics #data-integration #solving-data-integration #data #data-analysis