Introduction
There are numerous companies that are betting on data analysis as the main driver of their businesses in a period of three to five years. Therefore, information collection is becoming increasingly faster and more voracious, generating multiple opportunities.
Companies understand that large volumes of data, together with powerful Cloud processing tools, allow algorithms to be trained and executed on a large scale, thus taking advantage of the full potential of Artificial Intelligence.
A recent study by Gartner identifies the main trends in data collection and analysis, as well as their significant impact on business:
Advanced Analysis:
Gartner defines advanced analytics as the automation of the entire process prior to data analysis. This includes collecting, cleaning, and organizing data so analysts can discover valuable insights for their companies. Today, it is estimated that a data scientist can spend up to 80% of their time preparing and cleaning data. Using Machine Learning, Advanced Analytics can deliver predictive and prescriptive analytics automatically, based on existing information.
Continuous Intelligence:
This is a new technological pattern in which real-time analysis is integrated into business operations to process, validate and analyze current and past data, in order to predict future events.
Greater Use of Artificial Intelligence:
Companies are increasingly investing in AI to boost the analysis of collected data. The use of real, cleansed and reliable data is essential for AI to work correctly. Working with corrupt data can lead to incorrect conclusions. The challenge for teams working with AI is to select the information that will benefit the business and store it correctly to avoid loss or manipulation.
Natural Language Processing (NLP):
It is estimated that by 2020, 50% of analytical consultations will be conducted via NLP or voice. This means that machines will better understand human language, including different accents and forms of expression.
Use of Blockchain:
With incorruptible data thanks to blockchain technology, predictions are more accurate with real, unmanipulated information. Transparency in data generation is another important contribution of Blockchain to data prediction.
Persistent Memory Servers:
New persistent memory technologies will reduce the costs and complexity of using in-memory computing, enabling extremely fast processing. This will help businesses increase performance and perform rapid analysis of large volumes of data in real time.
These trends demonstrate that the amount of data generated is constantly increasing, and companies need to use technology to their advantage to obtain quick responses to market demands. The automation of repetitive processes, AI and Machine Learning are some tools that will improve results and allow data analysts to focus on finding strategic insights instead of wasting time processing data.
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