The data science revolution is coming, and businesses are trying to keep up with the changes that it brings along. That means, businesses around the globe are building big data adoption strategies by encouraging their employees to build data science competency and implementing technology for big data analytics. Check out these stats:
53% of businesses around the world are already adopting big data analytics
79% of executives agree that their business will perish if they don’t embrace big data
49% of entrepreneurs agree that big data analytics helps them cut expenses, and
44% confirm they use data science for innovation
Considering the increasing interest in data science, businesses simply cannot stay behind. And, although data science as a field is still developing, there are already a few things companies can do to build up their data science competency for the future.
1) Increase Competitiveness with Machine Learning
It’s impossible not to know about the impact of machine learning on businesses. Over the past few years, machine learning has become a buzzword, and the solutions that it brings can largely benefit any company both today and in the years to come. Currently, businesses across the globe mostly use machine learning for:
Predictive analysis and the optimization of customer relationships
Product marketing and sales forecasts
Easier data management
More precise selling models
Fraud detection and cybersecurity
However, there are businesses that have taken machine learning to the next level and now use it in a very creative way. For example, Pinterest has built its entire content discovery system using machine learning and data science. This system helps the company predict the likes and dislikes of its users and add more precision to the search results.
2) Use Data Science to Optimize Business Processes
Data science offers numerous solutions to companies in terms of the optimization of various business processes. For example, recently, big data analysis has gained influence in manufacturing. Production facilities around the world get more and more interested in investing in data analytics and IIoT (Industrial Internet of Things). Sensor technology and real-time tracking solutions collect and analyze data, which manufacturers can later use to:
Eliminate bottlenecks in the manufacturing process
Increase the efficiency of the assets
Track product quality and defects
Do product testing
Data science helps manufacturers to reduce production issues that can impact the quality of the product or the logistics within the production facility as well as the shipment process. Apart from manufacturing, data science has other creative applications when it comes to optimizing business processes, and recruitment is a good example of that.
One of the ways that data science is changing the recruitment and employee management processes is by powering HR apps. Employers can use AI-enabled HR apps to automate many employee management tasks, including:
Simplify employee onboarding
Manage employee workload
Run performance reviews and assign employee rewards
Collect employee training data
AI-enabled HR apps also help with recruitment, helping HR managers screen and source job applications as well as analyze the conversations with job candidates, and pick the best talent.
3) Incorporate Data Science Solutions into Risk Management
Apart from optimizing business processes, companies can also start using data science to mitigate risks. Today, the nature of risk management in business has changed significantly because of big data. “Risk management 4.0 is the new reality for companies around the globe if they want to keep their investment appeal high,” says Martin Foe, a researcher and technical writer at BestWritingAdvisor.
In which ways does data science change risk management? Here are a few of its possible applications in this field:
Identify and eliminate customer churn
Help predict and manage financial risks
Detect emerging trends and identify risks connected to them
Detect fraud and improve cybersecurity
In terms of risk management, big data can also be a huge helper for emerging startups and small businesses. One of the ways it can help a future startup launch smoothly is by analyzing its location and identifying potential business-related risks connected to that area.
Big companies are already using data science to manage risks when opening new branches around the world. Starbucks is a good example of that. The company went from human-driven to data-driven decisions quite a while ago. Once Starbucks was able to optimize its data, it started using it to identify potential locations for its future coffee houses.
With the help of machine learning, the company used big data to analyze:
Population in the area of interest
Suppliers and logistics
Starbucks also uses machine learning to compare the above-mentioned factors in a new location vs. the locations where it already has coffee shops. It allows the company to do predictive analysis and identify whether the location is profitable.
4) Encourage Staff to Become More Data-Literate
As data science slowly conquers the business world, it is important that companies get their employees on board and encourage them to become more data literate.
The chance to learn data analytics brings a lot of perks to their staff because they will be able to better understand business processes and what a company needs to increase revenue and remain competitive.
Major companies have been educating their staff about the importance of big data for a while. For instance, Airbnb launched its own internal data science institute using traditional online courses and well as inviting professionals to hold in-house lectures.
The courses offered by the company’s data science institute are not only for their IT teams. Airbnb designed these courses in a way that would also be useful for other teams, including human resources and business development.
When designing the concept of this institute, Airbnb used Google’s experience and provided not only technical but non-technical courses as well. As a result, more than 500 Airbnb employees have taken the course since the launch of the data science institute. It is important to note that educating existing employees will be much more profitable than hiring data science professionals.
In 2018-2019, there was a shortage of data scientists. This situation didn’t get better in 2020 as well, and there are a few factors that contributed to that:
The demand for AI professionals grew by 74% from 2015 to 2019
83% of companies are currently investing in big data
67% of global businesses are expanding their data science teams
With these points in mind, the shortage of data scientists will still be our reality in the upcoming years. However, the data science revolution shows no signs of stopping, and you cannot allow your business to wait for a few years until the situation clears. Instead, companies should invest in making existing staff more data-literate right now. It will cost you less, and you will already have in-house data science professionals.
Building the data science competency of a company is an ongoing job. As this field develops, professionals will find more and more opportunities to enter the emerging world of data analytics and business intelligence.
So, while today you can already take advantage of quite a few data science applications and programs, keep learning and educating yourself around data analytics. Becoming more data-savvy is a great chance to increase your portfolio and remain competitive in the future.
About the author:
Helene Cue is a professional writer, editor, and business expert. She has an MBA degree and loves to educate her audience about the innovations in the business world.