Whether we like to believe it or not, we live in a world driven by data. The products we buy, the places we frequent, and the movies we watch on Netflix, they all generate valuable data for businesses all over the world.
Even though we are now only witnessing a glimpse of what data science is truly capable of, data analytics is taking some significant steps to becoming the new best friend of business leaders. Through the use of data science, companies can make better, more informed decisions that can lead to improved customer retention and increased sales.
By 2019, the global analytics market has more than doubled, compared to 2014. What’s more, by 2033 it is expected to grow at a CAGR of 30.08%. These numbers show a growing interest in data science, but what’s interesting to observe is the fact that 90% of all data collected worldwide has been created in the last two years. With such rapid increases in data volumes, data science is continuously growing, changing, and adapting.
Below are some trends that are dominating the 2020 data science industry and will probably continue to do so in the years to come.
Businesses to adopt a data-driven mindset
Data has started to play a very important role in both project development and business management, and for good reasons. Data influences decision making; it allows those who use it to base their decisions on real-life customer information.
By adopting a data-driven culture, businesses can train their employees and teach them how to think with data in mind. Constant access to data analytics helps employees tailor their work to better satisfy consumer needs, which are constantly changing.
But in order for data science to really help companies, business leaders need to learn how to work with data scientists and develop a fruitful relationship with them. While it is true that data science can reveal some powerful information for businesses, its uses are still limited, meaning it might take some time to achieve big goals. By improving communication with data scientists and adopting a data-first approach, business leaders can get a better understanding of what data analytics can and can not deliver, ultimately learning how to set more realistic expectations.
Data science as a service
In simple terms, Data Science as a Service (DaaS) is a cloud-based data gathering and analyzing service, which allows businesses to gain important insights into consumer data without having to implement an in-house data management team.
In 2018, it was predicted that 90% of large organizations would generate revenue from DaaS, and while we can’t say these predictions have been accurately met, interest in DaaS has grown tremendously. DaaS allows businesses to access valuable data insights through a cloud-based platform, removing the need to invest in software, hardware or in-house talent. This is especially useful for small to medium businesses, who don’t have the necessary resources to put together a data management department.
Gaining all these valuable insights at a fraction of the cost and in a much faster way are some of the main reasons businesses are becoming more and more interested in DaaS.
Improved data privacy and security
Consumers are becoming more and more interested in how their data is collected, stored, analyzed and shared. Companies such as Facebook and Google, known for harvesting and sharing user data at their own discretion, have faced public scrutiny and legal issues for this, and have since then made changes to improve user data protection.
A 2019 statistic reveals more than half of consumers are now more interested in their data privacy, compared to 2018. To keep up with customer demands for data privacy, companies need to adopt an approach called Data Privacy by Design, which provides a safer, more proactive way to gather and manage user data. This approach allows companies to respect user privacy, while also collecting enough data to train their machine learning models.
The idea behind this is to help businesses collect just enough data to train their models, while also allowing users to opt to erase their data at any time. The ultimate goal is to guarantee reverse-engineering can not identify the users providing this data.
Augmented analytics and data management
Augmented analytics aims to automate the process of finding and revealing important business insights, in order to help leaders make better business decisions. While this is also achievable using manual approaches, augmented analytics does the same job faster and more accurate.
For augmented analytics to work properly, businesses need to shift their approach to a more data-driven culture and propagate it throughout their entire organization.
Data grows at a tremendously rapid pace, making it nearly impossible for organizations to gather the necessary technical skills to keep up with this growth. By adding Artificial Intelligence and Machine Learning to their data management processes, to help gather, store and manage data, technical staff can focus on tasks that require human involvement.
Python takes leading position
Python has grown to be the go-to programming language for data analysts everywhere. In 2019, a survey revealed Python to be the fastest-growing major programming language, earning second place as the most preferred language amongst programmers.
The reason Python has grown to be so popular is its large data science and machine learning libraries, which are all free to use. Python can even be used to develop blockchain applications, and is easy to master, even for beginners.
If things continue to evolve on this trajectory, Python is expected to become the number one programming tool sometime in the next five years.
As businesses continue to develop, they generate more and more data. But without a proper way to gather, store, and analyze these large volumes of data, companies can not benefit from its full potential. Integrating data science into their business processes and keeping up with the changing trends of this market helps companies improve efficiency and stay on top of their competitors, be it large, medium or small businesses.
