If a patient visits an emergency room the clinical information about the patient’s condition and their diagnosis can be used to identify risks for that patient in the future. Sharing that data with the patient’s primary care physician can allow that physician to reach out to the patient to discuss indicated treatments and interventions that could prevent potential emergency room visits in the future.
Additionally, these days more and more data is available on other attributes such as social behaviours, residence, and marital status. This may be used to assist in making predictions even more reliable.
On top of that, the pharmaceutical data around which medications a patient may be on is a further enhancement and, therefore, makes the predictions even more reliable. This is called non-rules-based (Discussed later in the article).
#analytics #healthcare #machine-learning #data-science #data-analysis
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
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
What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.
To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.
The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:
#big data engineering #top 10 big data tools for data management and analytics #big data tools for data management and analytics #tools for data management #analytics #top big data tools for data management and analytics
For Big Data Analytics, the challenges faced by businesses are unique and so will be the solution required to help access the full potential of Big Data.
Let’s take a look at the Top Big Data Analytics Challenges faced by Businesses and their Solutions.
#big data analytics challenges #big data analytics #data management #data analytics strategy #business solutions by big data #top big data analytics companies
The overnight transformation of companies adopting new technologies and transitioning to a digital work environment amid pandemic has made upskilling the most critical component in a worker’s repertoire in 2021. While information, data and the ability to make the right decisions serve as a stabiliser across verticals, analytics and data science have become indispensable tools to navigate today’s career scene.
According to a recent Forrester study, the top two challenges decision-makers cited are — the lack of employees with data skillsets and the lack of skills among business users who must use data insights. Almost 66% of organisations believe there is a requirement for data literacy among employees, where 59% demand analytic efficiency. However, with a converged approach to analytics through democratising access to data, automating tedious and complex processes, and promoting upskilling of data and knowledge workers, organisations can create a thriving data and analytics culture within.
#featured #advancing data and analytics #alteryx adapt #alteryx advancing data and analytics #alteryx upskilling programs #analytics upskilling #data and analytics #data science and analytics #start your analytics journey with adapt