For every single enterprise trying to generate value from the compiled data, it is important to have proper management of information flow from the source to the destination, like a data warehouse. This task proves to be an intricate and complicated one since there are so many things that could go wrong. Errors could propagate along the pathway of the source and destination or there might be duplication or corruption of data. With an increase in the data volume and the sources, the process gets even more complex. This is where data pipelines can help. With data pipeline automation, the flow of information can be simplified by eliminating all the manual steps in the process.
What Exactly Does Data Pipeline Architecture Mean?
A data pipeline architecture can be defined as a particular arrangement of objects that will regulate, extract, and route the data to various relevant systems to gain valuable insights. While pipelines of big data and ETL tend to extract the data from the source and transform it for loading it into the system, the data pipeline has a much more simplified process. It embraces all the values of the big data and ETL pipelines into one singular subset. One of the main differences between data pipeline and ETL is that the former tend to use proper processing tools to transport the data from a particular system to another one without the transformation taking place.
Robotic Process Automation (RPA) uses software bots to complete all the digitally aligned tasks and business across verticals. According to market experts, the global RPA is expected to rise by 40% by 2027. This shows that more and more companies are willing to invest in this advanced automation. The benefits of RPA will include better accuracy, quick process, and low cost. Moreover, investing in the RPA will help in removing mindless labor and engage in the other aspect of the business.
Let us check some of the benefits RPA can offer to your business:
- Increases the Productivity
Compared to manual data handling, RPA will help users to increase productivity. They are unstoppable and work 365 days a year. If your business demands high-volume output and customer onboarding, then investing in the RPA is the best to go with. Many top companies are investing in this RPA for high volume and better output.
Big data technology is changing the way we extract, analyze and implement data across industries. Among all the industries, healthcare is witnessing a positive change with the implementation of big data. In fact, by using healthcare analytics, there is a good potential to reduce the overall cost of the treatment, avoid preventable disease and improve the overall life quality of an individual.
Big data in healthcare is described as the process of extracting massive volumes of information by adopting the latest digital technologies of patients’ records and in the further working of the hospital. By applying big data in healthcare can help in understanding treatment models, warning signs of illness, and performance of medication or vaccines.
Travel was perhaps the first industry to be hit hard by the Coronavirus pandemic and post COVID-19, it is estimated that more than 50 million professionals would be laid off. With the whole world getting into a lockdown phase and with no saying when things will get normal again, it’s a huge challenge ahead. While people still want to go out and see places, it’s a health risk, not many would like to take on. However, the best response to this crisis is going to be the responsibility of the tech giants. In not just tracking the virus but also to identify and forecast future outbreaks (we already have smaller outbreaks happening all across), it will be data science, AI and related technology that will save the day.
- AI for virus diagnosis
Companies like Infervision have already come up with an AI-based solution that assists healthcare professionals in both monitors and detect the disease. With an improved diagnosis, it could be the new norm of travel.
In the age of public safety, video analytics is becoming the trend, which every other individual is looking forward to. Video analytics, also known as intelligent video analytics is a software that is used to monitor video streams in near real-time. While monitoring the videos, the software identifies attributes, events, or patterns of specific behavior via video analysis of monitored environments. It is designed to meet the need for increased surveillance video that a security guard will not offer.
How does it work?
The video analytics software is available form installed on camera and NVR. Each version of the software comes with different features, but does the same thing. Moreover, the setting of this software also differs accordingly. For example, many business owners use surveillance systems for detecting motion in the store. Using video analytics, even if your business shutter is down, the video system will still give you the result.
Gone are the days of classroom teaching and learning processes for high-grade learning. With the advent of superior technology, we now have machine learning and Artificial Intelligence techniques coming up which has no doubt boosted the e-learning industry. With the state-of-the-art infrastructure and cutting edge technology, it is without a doubt that machine learning would soon take over every platform for education. These processes not only allow any software to behave in a more intelligent manner but also automatically upgrades by itself.
How can e-learning be beneficial from Machine Learning and Artificial Intelligence?
Enabling a better delivery of the content
Designing any course online via a Learning Management Application is not just a one-time thing. The content of the course needs to be revised time and again according to the feedback given by the students who take up the courses. This feedback can be in a sort of any comment, or by a questionnaire or simply by ratings, quizzes and results. Artificial Intelligence enables the utilization of an artificial network of neural connections or even deep-seated algorithms for processing the information to optimize the content which requires a very minimal amount of human intervention.
Even though cats and dogs are different animals, what is the point of distinction between them? You might term the ability to distinguish between a cat and a dog to be common sense, but it is described as deep learning. People are not programmed to recognize different attributes in an object by inputting external information. These capabilities are inherent and cannot be induced through external stimuli, and thus are unnoticeable for us humans.
Computers, on the other hand, need gradual feeding – in the form of deterministic algorithms- to detect even the most simple judgments. Despite the surge in machine learning and connectivity, a computer cannot do what a toddler does unintentionally. The following are the developments in deep learning –
Whether you are a technology enthusiast or not, you must have heard of the term IoT (Internet of Things)? It is a trend that has caught up to our lives and soon we are going to witness a significant change in using smart devices as they will all be smartly connected.
The prime fuel to the IoT ecosystem is the data from the devices that are being managed by edge analytics. Edge Analytics goes beyond just collecting data from the source of its production and is more about the data being processed so that IoT can use it more effectively for better operations. This collected data is then processed, making IoT independent of internet access at all times.
Hence, effectively, with edge analytics in place, IoT devices can use data every time.
Frankly, Big Data isn’t anything without the involvement of professional skills that help turn cutting-edge technologies into actionable insights. While media, education, healthcare, securities, all have taken the advantage of big data, financial industry too has started opening its doors to it. By unlocking the power of Big Data, they have also raised the worth of a data scientist that knows how to push the worth of a high amount of data which currently exists within an organization.
There’s no doubt in the fact that the modern business arena is bombarded with data. As per a report in the year gone by, McKinsey estimated that “big data initiatives in the healthcare industry of the US could account for as much as $450 billion in decreased health-care expenditure or up to 17 percent of the $2.6 trillion touchstones of the cost of US health-care”. It is important to note here that bad data costs the US approximately $3.1 trillion annually.
Therefore, the value of analyzing and processing of data is highly evident. And this is exactly where the spotlight comes on a data scientist. While most professionals are aware of how hot big data science is, they are still unknown to the value that a data scientist holds within a company.