The evolution of Data Science: A Comprehensive Study

January 9, 2022by Marktine Technology

The evolution of analytics spans decades. From its first iterations as a manual, offline process to today’s innovations in predictive decision-making, analytics has grown beyond recognition. Your business can be more successful if you know your customers and use data to help make decisions.

The history of data and analytics is divided into four eras, each shaped by the technology available at that time. Let’s dig deeper into it:


Here are four eras of Analytics (so far):

  1. Analytics 1.0:
    Previously, companies used to collect data offline and analyse it manually to look for ways to improve their operations. Most early attempts to do this were done without an awareness of data science as a discipline. When the World Wide Web was created in 1989, businesses were quick to see its potential. Now that they could start to collect data online, they could automate the manual processes involved. Database managers, data warehouse specialists, and business intelligence (BI) professionals were the early analytics pioneers. Business intelligence and data warehousing, as well as similar things outside of business such as government monitoring, pushed forward by technologies like relational databases and SQL. This early version of analytics is referred to as Analytics 1.0.
  1. Analytics 2.0:
    The first decade of the 2000s saw the dawning of the analytics age, when business data started to become more accessible, computers and connectivity were becoming more ubiquitous in businesses. This era saw the emergence of early big data initiatives, as businesses began to realise that they could harness large amounts of information to gain user insight.
    Analytics 2.0 is a new approach to the analytics process, in which disparate data can be combined and processed with the help of professional analysts. The objective, as before, is to provide target groups with real-time insights and recommendation information to guide their actions, rather than simply reporting on past actions and completed processes.
  1. Analytics 3.0:
    Previously called ‘phase two’ of digital analytics, Analytics 3.0 focuses on how your data and analytics practice can drive change within your organisation, to deliver value and impact. The period from 2010 to 2015 was a transitional phase in which analytics came to be generic and all-encompassing term for the collection and analysis of data. Analytics 3.0 is all about making data-driven decisions at scale, harnessing the shifts towards more mobile, customer-centric experiences, more context and deeper insights.
  1. Analytics 4.0:
    Analytics 4.0 will make your data science team more agile and effective, helping you to make better decisions that can drive everything from digital transformation to market strategies. The role of a data analyst is shifting following the rise of big data and cloud technologies. As data volumes continue to increase, it becomes more difficult for organisations to store and analyse all information available to them. However, businesses can no longer afford to ignore the opportunities that come with it.


Wrapping up:

From basic statistical analysis to sophisticated machine learning and data science approaches – analytics industry has evolved a lot over the past decade. We stay up to date with the latest innovations in data science and AI, to serve our clients’ needs as they change from year to year – or even from day to day. Whatever your analytics needs, we’re here to create better outcomes with actionable insights.