Big data’s market value will skyrocket up to $103 billion by 2027 globally more than double its market size in 2018. About 2.5 quintillion bytes of data are generated every day online, but more than 90% of data collected have not been put to use by the global market yet. These huge sets of complex, unstructured data created by millions of internet users has led to an increasing demand for data scientists across all industries in the job market.
The reason why data science is important is that businesses today can benefit in mining key insights from customers’ varied online behaviours to strategise and make profitable decisions with their products in a very short time. One of the biggest examples is the difference in time taken for iTunes and Pokemon Go to gain active users. It took iTunes about 100 months to reach 100 million active users by 2003. For Pokemon Go, it took a few days in 2016.
Why data science requires multiple roles
A data scientist makes use of big data in many different ways. S/he has to be proficient in various backgrounds including computer science, statistics, math, data mining, information management and data visualisation. Based on a business’s requirements, a data scientist’s role broadly falls into two categories.
1. Product analysis
A product analyst role is to provide strategies and goals of how the item for consumption will perform. By identifying relevant issues that are quantifiable, the data scientist provides recommendations and solutions. With the help of statistical product analysis, a company can determine the geography and time to sell its upcoming product.
2. Algorithm development
With algorithm development, the data scientist makes use of the current technological advancements in artificial intelligence and machine learning to derive a product’s predictions. By understanding and charting out complex online behaviours through different algorithmic data, the data scientist can help an organisation predict the outcomes. For example, a data scientist can build better full-proof fraud prevention models for the future through machine learning and qualitative risk assessment.
Why data science is helpful for businesses than traditional models
· Data science vs Traditional Business Intelligence
Data science adds value to any business. With Traditional Business Intelligence, tech companies would employ a massive IT team that would create analytical insights across different departments. This scope of work was only limited to analysing structured data. On the other hand, a data scientist’s role is to handle unstructured and dynamic data from various channels. The decision-making process for companies with a data scientist becomes more streamlined and proficient when unexploited data is put to use.
· Better product development
In this fast-paced business world, an organisation is only valuable if it is able to develop its products that guarantee maximum customer satisfaction. By analysing the target audience’s needs with the current market trends through analytical tools, a data scientist helps the company determine how the product can be sold effectively.
· Predicting outcomes faster
By predicting the future through customer segmentation, risk assessment market analysis and sales forecasting, a business can make better decisions. With the help of a data scientist, an organisation can use such accurate data in a much faster way by eliminating problems responsible for any future loss.
· Recruitment automation
Data science has also helped in automating repetitive processes across various industries. One of the finest examples is the recruitment procedure when the recruiter of an organisation has to select a candidate from the huge pile of resumes for one position. Data science with the help of technologies such as image recognition, clustering and classifying analytically can help convert such visual information to provide deeper insights of the potential employee.
Why choose data science in this decade?
As you can fathom these aforementioned benefits of data science for any organisation, choosing this career path will be a lucrative choice. If you are interested in exploring this field, check out our courses such as Big Data and Analytics, Artificial Intelligence & Machine Learning and many more offering a hands-on learning experience virtually.