It is not always easy to overview the current trends in Data Analytics: Eight promising trends in Data Analytics are briefly described below.
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1. Automated Machine Learning (AutoML)
AutoML enjoys a steadily increasing popularity. Not least driven by the numerous successes in practical analyses. In a world where more and more devices produce data and are networked with each other, the data “produced” grows disproportionately. Therefore, AutoML is of urgent necessity to gain knowledge from these rapidly increasing data on time. We assume that AutoML becomes even more critical in the coming years and that the analysis methods deliver even more precise and faster results. The field of activity of the data scientist will not disappear, but rather, his focus will shift to more specific or sophisticated analysis techniques. In short: AutoML saves time and money (you don’t need a larger team of data science and machine learning experts). It is also the easiest and cheapest way to enter the world of artificial intelligence or machine learning.
2. Explainable AI (XAI)
Explainable artificial intelligence (XAI) is the attempt to make the finding of results of non-linearly programmed systems transparent to avoid so-called black-box processes. The main task of XAI is to make non-linear programmed systems transparent. It offers practical methods to explain AI models, which, for example, correspond to the regulation of the data protection laws of the European Union
3. Blockchain Analytics
Blockchain analysis is the process of inspecting, identifying, clustering, modeling, and visually representing data on a cryptographic distributed-ledger known as a blockchain. The goal of blockchain analysis is to discover useful information about the different actors transacting in cryptocurrency. Analysis of public blockchains such as bitcoin and Ethereum is often conducted by private companies.
4. Augmented Data Management (ADM)
Augmented data management (ADM) involves using machine learning (ML) and artificial intelligence (AI) engines to automate some of the manual tasks involved with managing data. This means making data quality checks, metadata and master data management, and data integration “self-configuring” and “self-tuning.” […]
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