It is extremely time-consuming to dig through huge piles of information to try and establish a pattern. This is where data analytics can come in handy, as it helps companies find various patterns by analysing all the data they have collected. Let’s take a look at this concept and see industries put it into practice.
What is data analytics?
Data analytics can be defined as the combined qualitative and quantitative approach that is carried out to obtain critical insights from collected data. The data is analysed using specialised computer systems, which ultimately helps in identifying patterns and drawing conclusions.
Data analytics can be divided into several types including:
- Descriptive analytics: Descriptive analytics helps in understanding ‘what happened in the past’ and how the information can be used to form better strategies for the present and the future. For example, analysing last month’s average sales can help in forming a sales strategy for the next month. Descriptive analytics can be used to describe any events that have occurred in the past, which can help in preparing better strategies for the future.
- Diagnostic analytics: This type of data analytics helps answer the question of ‘why something happened.’ Diagnostic analytics assist in finding critical insights and the source of any existing issues. Usually, descriptive and diagnostic analytics are used together within business environments.
- Predictive analytics: Predictive analytics answers the question of ‘what is going to happen.’ It uses the assessments obtained from descriptive and diagnostic analytics to help businesses prepare future trends and strategies.
- Prescriptive analytics: This form of data analytics helps in answering the question of ‘what action can be taken’ to take advantage of an encouraging forecast or eradicate any issues that may arise in the future.
5 industries that rely on data analytics
- E-commerce: data analytics is used by the e-commerce industry in a number of ways. Firstly, it helps e-commerce organisations to properly understand their customers’ requirements, which products are customers interested in, which products your customers are not happy with and how to increase overall sales. E-commerce firms can stay ahead of the game by using this information. Data analytics also provides e-commerce organisations with a centralised platform that increase the overall security and speed of payment functions. This helps in putting the customers at ease and in identifying any fraud that may occur.
- Healthcare: using data analytics in the healthcare industry is significantly beneficial to the field and has a number of encouraging effects. Analytics help doctors in obtaining information about patients, thereby assisting them in finding various illnesses and preventing or treating them at an early stage. In addition, forming a comprehensive picture of patients will allow insurance companies to tailor packages to each of the patients. Healthcare data analytics can help hospitals reduce costs by using predictive analysis to determine future admission rates and thus, the allocation of staff. This will help in reducing the amount of capital spent by hospitals and will make use of their investment to the fullest. In fact, as per a survey conducted in 2017 by the Society of Actuaries, 47% of healthcare organisations have already implemented predictive analytics in their operations.
- Entertainment: the utilisation of data analytics has been made popular in the entertainment industry by streaming services like Netflix, Prime Video and Hulu. These streaming services collect huge amounts of customer data such as ratings of a show, searches, the date and time a show was viewed and the device on which it was viewed. Once all of this data is analysed, these services provide customers with suggestions, which ultimately assists in customer retention. It also helps these services provide personalised viewing recommendations to customers.
- Finance: from stock prices and analyst projections to banking transactions, the finance industry has vast amounts of data that needs to be interpreted. Firms in the finance industry have begun employing data analysis processes to cope with the large amounts of data that comes through. Using data analytics in the finance industry also helps in improving a company’s overall efficiency and ensure they are complying with legal requirements, as well as reducing fraud. In addition, finance organisations can use prescriptive analytics to discover new business opportunities and trends and cultivate new revenue streams.
- Manufacturing: manufacturing organisations use data analytics to develop or find new methods to modernise their operations. According to a report created by The Economist entitled, “Manufacturing and the Data Conundrum,” 86% of organisations said that they have started collecting greater amounts of data and 90% of organisations said that they have data analysis processes in place for their manufacturing operations.
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