Jobs in the data analytics sector are plentiful, salaries are high, and the career paths you can take are abundant. Data analytics offers a wide variety of opportunities across industries and corporate levels. Data is the new oil.
Singapore is at the heart of Asia, and with robust information technology infrastructure, its strong global connectivity can host and process huge volumes of real-time data. The culturally diverse population of Singapore offers the ideal location for testing innovations that are made for Asia. A broad economy enables access to industry-specific knowledge needed for coming up with the best analytics. Professionals interested in analytics can take up data analytics courses in Singapore and become a part of the top tech companies that are working on data analytics related projects.
Here are five sectors with business operations shaped by big data and analytics — and what they have to offer.
Big supply chain analytics uses data and quantitative methods to improve decision making for all activities across the supply chain. In particular, it does two new things. First, it expands the dataset for analysis beyond the traditional internal data held on Enterprise Resource Planning (ERP) and supply chain management (SCM) systems. Second, it applies powerful statistical methods to both new and existing data sources. This creates new insights that help improve supply chain decision-making, all the way from the improvement of front-line operations, to strategic choices, such as the selection of the right supply chain operating models.
Data analysis can also drive strategic decisions. In recent years, one pharmaceutical company has created a database with all bids submitted for packaging. The data has been evaluated to fully understand the cost structure of those suppliers and to create detailed cost models for different types of packaging. Using updated information on commodity prices, factor costs, and plant utilization, these models can be used to aid the selection of the most appropriate suppliers for new packaging projects.
Big data in healthcare is a term used to describe massive volumes of information created by the adoption of digital technologies that collect patients’ records and help in managing hospital performance, otherwise too large and complex for traditional technologies. The application of big data analytics in healthcare has a lot of positive and also life-saving outcomes. In essence, big-style data refers to the vast quantities of information created by the digitization of everything, that gets consolidated and analyzed by specific technologies. Applied to healthcare, it will use specific health data of a population (or of a particular individual) and potentially help to prevent epidemics, cure disease, cut down costs, etc.
We will look at one classic problem that any shift manager faces: how many people do I put on staff at any given time period? If you put on too many workers, you run the risk of having unnecessary labor costs add up. Too few workers, you can have poor customer service outcomes – which can be fatal for patients in that industry. One of the key data sets needed is hospital admissions records, which data scientists crunched using “time series analysis” techniques. These analyses allowed the researchers to see relevant patterns in admission rates. Then, they could use machine learning to find the most accurate algorithms that predicted future admissions trends, allowing shift managers to put the appropriate number of staff.
Finance industries perceive data as an essential commodity and fuel. It churns raw data into a meaningful product and uses it to draw insights for better functioning of the industry. Finance is the hub of data. Financial institutions were among the earliest users and pioneers of data analytics. Data Science widely used in areas like risk analytics, customer management, fraud detection, and algorithmic trading.
Financial industries need to automate risk analytics in order to carry out strategic decisions for the company. Using machine learning, they identify, monitor and prioritize the risks. These machine learning algorithms enhance cost efficiency and model sustainability through training on the massively available customer data. Similarly, financial institutions use machine learning for predictive analytics. It allows the companies to predict customer lifetime value and their stock market moves. We can define machine learning (ML) & deep learning (DL) as a subset of data science, that uses statistical models to draw insights and make predictions.
Data Science Applications that are revolutionizing the Finance Industry
Aventis School of Management offers Master’s in Data Analytics and Graduate Diplomas in Data Science or Business Analytics to cater to the needs of potential data analysts and scientists. Whether you have a relevant background or not, we can help you take the first step. Find out more about our programmes!