Data Science is helping business create value by using data, software and algorithms to better serve customers. There is a lot of debate among practitioners and data scientists about the term Data Science. Data Science as a term was used first during 1970s and proposed as an alternative to computer science. However Data Science is not just computers, hence many statisticians also argued that Data Science is not a new field, but rather another name for Statistics. Others argue that Data Science is distinct from Statistics as it focuses on problems and techniques unique to the digital era.
Data Science is thus a combination of the use of statistics, computers and data to solve business problems. In the new digital era with massive amounts of data and computational abilities, we use bigdata and machine learning to add value to businesses and hence we need new terms to define these new processes and understandings. We can thus use Data Science to mean processes that combine analytics, machine learning, artificial intelligence and uses big data and state of the art storage and computational power to achieve business results.
Three major happenings that made Data Science Popular and Trending
There are three major events that have happened in the last decade that hastened the adoption of Data Science and the use of data by business organisations to answer business questions and decision making.
The availability of large amounts of data called Big Data, made available due to social media, sensors and digitalization of our systems. The internet of things and sensor data from our systems provides massive amounts of data to organisations every day.
Advances in Computing power and storage of computers from availability of massive storage devices to larger servers to cloud computing. The availability of internet, software as a service and cloud resources have massively enhance both storage and computing capabilities and organisations now have the ability to scale rapidly and with better processes.
Machine learning algorithms which work not just with structured tabular data but contemporary unstructured data including images, pictures, video and audio files. Mathematical and statistical algorithms and logic that were part of literature could now be applied to run with the help of software, computational power and data to experiment and bring about new and improved versions.
How Data Science adds value to Businesses
Businesses have widely adopted Data science as they can see the value addition data usage can bring. As data is becoming richer in variety (not just numbers but also images, text, videos and audio data) it is increasingly becoming important for businesses to use and analyse the data to add value to their businesses. Giants such as Facebook, Google, Microsoft have become successful by the smart use of their data. Data along with computing power and software gives companies the abilities to answer business questions that help them make better decisions, automate processes, reduce redundancy and scale up with more efficient and effective techniques than ever before. The areas where businesses can use data analytics, predictive analytics, machine learning and artificial intelligence include:
Machine learning helps companies in getting to know and serve their customers better: Companies can know better who are their high margin customers, which customers have a higher probability to churn and why? What factors, products impact the loyalty of customers and why customers prefer certain products over other products? Analytics and machine learning also helps companies to better serve customers by using recommendation engines, by automating processes for faster and error free delivery.
Analytics and machine learning helps companies to deliver products and services efficiently and effectively: Using analytics and machine learning companies are able to know which distribution channels are most effective and how they can ensure that their delivery methods attract more customers and repeat customers. Companies can use algorithms to make routine manual decisions and hence reduce the time to delivery of products and services.
Machine learning and Analytics helps companies to predict best prices of each product and services to maximize their revenue and profit margins: Companies use sophisticated algorithms to price and service their products to get maximum revenues. Amazon, Walmart and all Airline services use automated analytics systems that employ internal and external data to predict prices of products and services.
Analytics helps doctors to understand patients better: machine learning and analytical systems helps doctors to make better predictions about the conditions of their patients and hence better treatment outcomes. Managing records and analysing and predicting techniques have helped the medical and pharmaceutical industry to discover newer and better drugs for each disease.
Analytics and machine learning helps companies to know which promotions are most effective: companies are able to know the most appropriate marketing activities to undertake to reach their customers and to predict the impact of each of their promotional techniques for best advertising and target marketing.
Analytics helps manufacturing firms to manage processes better: Manufacturing firms are able to predict the likely chances of machines and process breakdowns using data and are able to prevent negative events from impacting their revenue and sales targets. Digitalization of all production process provides data to algorithms to manage and maintain information about machine breakdowns and profitability of various processes and products throughout the departments.
