Data Modelling Growth Projections in 2022

Software development and analytics is a rapidly expanding market that is expected to see a compound annual growth rate (CAGR) of 11.7% from 2022 to 2030, according to statistics from Grand View Research. Industry insiders are actively anticipating how trends and advancements within data modelling could shift as the year progresses. That said, here are some major data modelling growth projections to look out for.

Data Modelling as a Key Industry Growth Driver

According to Business Standard, India's software products sector was able to amass over $10 billion in revenue in 2019-2022. This shows that it is a valuable asset to the country's growth and sets a good foundation for leveraging new and emerging technologies. As a crucial part of any software project, data modelling will significantly impact business growth as it can help garner information that can provide an edge over competitors.

Moreover, the digitisation of data has become increasingly popular thanks to the COVID-19 pandemic, which has prompted more and more companies to invest in data modeling and easy-to-use tools on the market. Data modelling will play an even bigger role in a wide array of industries such as retail, healthcare, finance, supply chain, education, hospitality, agriculture, and many more.

Data Modelling to Aid Booming Startup Ecosystem

In a recent podcast, MongoDB Vice President Sachin Chawla highlighted how India is currently enjoying a robust startup system. More than $38 billion in funding flowed into Indian startups last year — over three times more than the $11.1 billion total funding received in 2020. As a fast-growing company, MongoDB has also provided data platforms, enterprise software and support, and resources to rising startups such as Ultrahuman, Flexa, Bliinx, and many more.

Startups, most especially, will benefit from investing in data modelling tools earlier on in their business evolution. Not only will it aid them in collecting, updating, storing, and analysing information, but it will also identify their key business concepts and map them out to available and prospective data. An effective data modelling plan also improves system performance while saving money, which improves the chances for startups to succeed.

Data Scientists Here To Stay

The rise of AutoML (Automated Machine Learning) was brought about by the rapid digitisation of data in recent years, as previously mentioned. AutoML streamlines data collection, data preparation, deployment, as well as modelling tasks that can be quite time-consuming and repetitive without automation. Because of this, many have begun to question whether or not AutoML will eventually pose a challenge to data science jobs.

Contrary to this, the US Bureau of Labor Statistics predicts that the field of data modelling will grow by 8% over the next 10 years. There is no expected shortage of job opportunities for data scientists, especially data modellers. Moreover, the expertise that data scientists possess will remain far superior to AutoML features, specialists or those training in this field can expect an abundance of opportunities in the coming year.

Data Modelling to Address Societal Challenges

Thanks to improvements in technology, data modelling can even be leveraged to help address modern societal challenges occurring today. Writer and strategist Amy Lynn Smith highlights how the UN Refugee Agency (UNHCR) and the UNHCR's Innovation Service and UN Global Pulse (UNGP) use data and analytics to get real-time feedback on how well policy responses are working. We can expect data modelling to contribute not only to businesses and enterprises but also to societal development as a whole.

Conclusion

Data modelling has become such an exciting field. As a key industry growth driver, a major contributor to information technology, and a necessity to most businesses, data modelling promises industry players a strong job outlook and will likely continue to evolve at a rapid pace in the near future.