Companies investing in Data Analytics would increase their revenues by up to 21%

Qlik and IDC have produced a global study that shows that organizations that invest strategically in building modern data-to-insight capabilities through pipelines (a technique for implementing instruction-level simultaneity within a single processor) of data and analysis have a significant impact on their business results. The study collects responses from 1,200 business leaders from companies with more than 1,000 employees.

The companies collect large amounts of data through unintegrated and leaky data pipelines, and all pursue the same goal: maximizing the value of that data. The research shows a direct relationship between the ability to convert data into knowledge (data-to-insight) through investments in pipelines, deploying management and analysis solutions to close the gaps in those pipelines, and decision making that drives business results.

The 86% of organizations with the best data analysis pipelines also have the highest scores in decision making.

67% of organizations with the highest scores on decision making also have the most top scores on business outcomes.

The companies surveyed with the most significant demonstrable ability to convert data into knowledge (which means robust data pipelines to drive better decision making) have a significant impact on their business results:

76% said operational efficiency improved by an average of 21%.

75% said revenues increased by an average of 21%.

74% indicated that profit

However, there are significant challenges in deploying data pipelines to drive better business decisions and results. Of those surveyed, over 60% have experienced significant problems in assessing the value of data and identifying valuable data sources, often due to the lack of a data catalogue. More than 42 per cent of respondents identify ensuring that data is correct when processed or transformed for analysis as one of the most significant challenges.

Even when organizations invest in techniques such as machine learning or artificial intelligence to improve information generation and analysis, the success of these investments depends mainly on having an agile, automated and agnostic data pipeline that closes the gaps by working with any cloud, system and data source in real-time.

Amazon's idea for workers to keep social distance: an AI camera that knows when they are getting too close

From Amazon, they explain that a preliminary solution to help maintain social distance was to apply artificial intelligence and machine learning to the facility's camera recordings. That way, zone managers could identify the areas with the most traffic and implement the corresponding measures.

Version 2.0" of this idea has resulted in an augmented reality system. Through a 50-inch monitor, workers passing through a zone can see how close they are to other people and separate themselves if necessary. This system uses a camera, a depth sensor and a local computing device that uses machine learning to differentiate people from their surroundings.

When the person passes through the area where the camera is pointed, say a corridor, he or she will see on the monitor a green circle with a radius of two meters. If a person gets too close and the circles cross, they will turn red, indicating that the safety distance is not being respected and that they should be separated. It is more of a visual support measure.

The company says it has already tested the system in a "handful" of buildings and that employees have well received the measure, so it will deploy "hundreds of these units in the coming weeks. The array only needs one power outlet to operate, so, theoretically, it should be able to be deployed anywhere.

They also claim that they will release the source code of the software and artificial intelligence for anyone to use. This will allow them not only to create forks of the technology but to audit the system to see how it works on the inside and what kind of information, if any, is collected.