Harnessing the benefits of business data science

data science

Data science, the harnessing of new technologies and approaches, can deliver competitive advantage and accelerate growth.

Data science is the culmination of several fields like statistics, artificial intelligence (AI), data analysis and more. This field aims to study internet-generated data and reap value from it. Field experts, also known as data scientists, feed harvested data to enable machine learning that, in turn, leads to smarter AI.

Over the course of history, humans have made the most of resources like agriculture, gold, coal and more to prosper. In today’s age these driving resources have gone digital too. According to research, out of 7.8 billion people on earth, 4.93 billion, that is 64 per cent of the world’s population, has access to internet. Today, the internet is an ocean of a resource.

Data science is making a huge headway in business growth. Some ways in which data science is being used commercially include predicting business outcomes. Data science has the power of statistics and can be used to predict outcomes.

Predictive causal analytics in data science, as the name suggests, can help a business foretell if a venture or a decision will yield success. For example, this style of data science can be used by a bank or a financial institution to predict if money lent through a home loan to a homebuyer will be successfully repaid or not. Predictive causal analytics will delve into the homebuyer’s payment history and financial patterns to generate predictions about future payments.

Data science, as well as AI, uses multiple techniques to obtain insights from raw data. These insights can be used by businesses and brands to gauge the performance of new products even before their launch in the market. Similarly, it can also help us understand which business models would perform well and which would not succeed.

In enhancing efficiency, data science feeds raw data to AI which identifies patterns and learns or observes behaviour. As a result, data on the productivity and efficiency of machines in a factory or employees in an office can be analysed to identify problem areas. These problem areas can then be fixed to maximise output and grow businesses.

To harness the power of data science to the advantage of a company, the minutia of data is expertly managed by data scientists who develop strategies for analysing data, plan how data is to be used for machine learning and determine what type of data needs to be analysed to benefit a business.

It takes several factors and parts in order to manage projects, with five essential elements including purpose, people, processes, platforms and programmability. The key steps in the data lifecycle include understanding and framing the problem; the next step is to collect the right set of data; cleaning data; exploratory data analysis (EDA);  model building and deployment; communicating your results and lastly CRISP-DM and OSEMN.

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