The <\/span>evolution of Data Analytics <\/span>has witnessed adaptation in various forms, gradually through the phases of technological advancement. It has offered multiple advantages to organisations and individuals involved in data analytics. Data Analytics is an ever-expanding discipline.<\/span><\/p>\n Become a Data Analyst<\/b><\/p>\n The field of data is one of the top-notch areas that is growing in the corporate world. It would be very profitable for individuals to have a career in data science<\/a><\/strong>, as technology is scaling every day. A data science course with placement can be a huge game changer when a young professional is trying to understand<\/span> what is data analytics <\/span>and looking to start a career in a related discipline.<\/span><\/p>\n History and<\/b> evolution of Data Analytics<\/b><\/p>\n Read on to learn about the <\/span>evolution of Data Analytics<\/span> and how <\/span>the four eras of analytics<\/span> have grown over the years.\u00a0<\/span><\/p>\n Relational and non-relational databases\u00a0<\/b><\/p>\n Edgar F. Codd created relational databases<\/strong><\/a> in the 1970s, and they gained significant popularity in the 1980s. SQL queries were very famous at that point in time, and users used to derive data from several databases using RDBMS. Relational databases and SQL are still widely used and accepted, but technologies like MySQL are not that prevalent.\u00a0<\/span><\/p>\n Relational databases like MySQL were intended to convert unstructured data and are typically highly strict. Soon, the internet had a surge in popularity, and the relational database couldn’t keep up. That is when NoSQL platforms like MongoDB were created to combat the shortcomings of RDBMS.\u00a0<\/span><\/p>\n Business intelligence<\/b><\/p>\n Business Intelligence refers to the process of improving business trends and choices by seeking, aggregating, and evaluating the accumulated data preserved by an organisation. Power BI cannot be underestimated, as it was one of the most innovative and futuristic concepts in the 1990s.\u00a0<\/span><\/p>\n Power BI is the ability to make decisions that are related to data technologies. Business intelligence was initially in the form of systematic consumer data but later took part in the decision-making process of large businesses.<\/span><\/p>\n Data visualisation<\/b><\/p>\n One can have a great career in Data Analytics if one understands the deep-rooted concepts of data visualisation. Visualisation means the representation of data in graphical form using representative tools like charts, graphs, diagrams, etc. With the <\/span>evolution of data analytics<\/span> in the big data era, the tools and technologies of data visualisation have been crucial for analysing a vast volume of data and making data-driven decisions.<\/span><\/p>\n The transition from Florence Nightingale\u2019s diagram of mortality in the Army in the East to Tableau has been historical and remarkable. Florence Nightingale was a pioneer in the field of data visualisation who changed the ways of representing data for good. Such formats of statistical charts and graphs are now used in modern software like Tableau.<\/span><\/p>\n Big data<\/b><\/p>\n Big data emerged in early 2000 and Roger Magoulus first coined this term. The technology of big data emerged when handling immense amounts of data was not possible with only Power BI tools. Hadoop emerged as a big platform for processing large amounts of data.<\/span><\/p>\n The<\/span> evolution of data analytics <\/span>and big data in <\/span>the four eras of analytics<\/span> has become a very important part of the world of technology. It allowed the pooling of a large amount of data. The open-source platform gave its users the additional advantage of versatility. Many other open-source platforms developed after the success of big data, such as Apache Spark, Apache Cassandra, and so on.<\/span><\/p>\n Data Analytics in Cloud<\/b><\/p>\n Cloud computing tools and services have become more important as businesses have become more complex over time, allowing firms to stay up-to-date. The <\/span>evolution of data analytics <\/span>from traditional hardware to Cloud-based infrastructures is a good example.\u00a0<\/span><\/p>\n In 1997, a famous professor, Ramnath Chellappa, defined Cloud Computing as a new type of computing paradigm where economic considerations dictate the ways of computing, only focusing on technical constraints.\u00a0<\/span><\/p>\n![\"Data](\"https:\/\/imarticus.org\/blog\/wp-content\/uploads\/2017\/03\/PGA-1-300x300.png\")
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