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Big Data Plus Machine Learning: An Unbeatable Skill Combination for Data Scientists
By Arijit Banerjee
Nearly 1.7 megabytes of information is expected to be created by 2020. However, it is not the volume of data that matters but what organisations do with it. Successfully managing, analysing and using the data insights is fundamental to creating long term business value.
Machine learning (ML) with its massive computational powers helps organisations effectively analyse data to uncover hidden trends and accurately identify patterns. Little wonder forward thinking organisations are making astute investments in ML. However, to make most of these investments, hiring data scientists with ML and big data skills is paramount. The reason: data scientists with ML and big data skills help companies better manage large volumes of unstructured data sets, run experimental analysis, and scale data strategy, by developing powerful algorithms and data-driven models.
Here are three reasons why companies should hire data scientists who can bring the combination skill set to the table :
1. Create better user experience: The team of data scientists at Airbnb uses big data and ML to help hosts get more bookings by predicting the possibility of a host and a visitor matching. This, in turn, helps them enhance customer experience and improve customer retention. Deploying data scientists with ML and big data skills helps companies learn customer behavioural patterns and predict how often and when a person is likely to take certain actions - a sure fire to enhance customer experience.
2. Work easily with large data sets and models: With increasing cost of data storage, it is imperative for companies to put each data set to use. Data scientists use programming languages such as Python and R to build ML algorithms that analyse large data sets from different domains and perform cluster analysis. For instance, data scientists proficient in Python can help companies discover differences and similarities between various customers, and create distinct groups for better targeting.
3. Facilitate personalised customer experience: The combination of ML and big data is breaking new grounds in targeting customers. Data scientists at Flipkart leverage big data and AI to study past customer behaviour and predict future buying patterns. This has helped them better understand customer preference, provide relevant results for every query, and promote relevant products. Lesson to learn: hire and upskill data scientists in developing ML models that provide insights into customer’s gender, brand and store affinity, price preference, and volume of purchases. This not only helps create better user experience but also improves the bottom line by increasing sales and generating customer retention.
According to Gartner, poor data analysis costs companies on average $13 million every year. For organisations looking to make data-driven decisions, having a robust approach to leveraging data is a must. This requires deploying data scientists who can apply ML techniques, construct models that learn from data, and use scalable algorithms to analyse big data. The benefits of using such expertise are manifold: ability to better sculpt marketing and customer support initiatives, and in turn, delight customers.
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