By: Paul Azorin, Chief Technology Officer at BairesDev
Investments in data science today will determine which companies dominate the global economy in the next ten years. In fact, big data is already helping companies develop more successful products and strategize for the future.
That’s why the number of job postings for data scientists increased by 75% over the past three years. However, demand is currently outstripping supply and companies are having a hard time hiring skilled data scientists.
Many companies are turning to nearshore development services to help them master data science and develop custom software. These outsourcing partners are helping businesses utilize their massive troves of consumer data while simultaneously safeguarding that sensitive information against malicious actors.
Consumer Data & the Internet of Things
The Internet of Things (IoT) is poised to become one of the most important technological developments in history. The upcoming release of 5G wireless technology will enable the creation of a globally-connected network of electronics that will improve the lives of consumers and change the way that businesses operate. This network of devices is referred to as the IoT.
This technology will make developments like self-driving public transportation networks, human-like AI assistants, and the ability to speak any language with a simple earpiece a reality. In addition, the billions of sensors and internet-connected devices will provide companies with an amazing collection of consumer data that they can use to streamline and focus their business.
Businesses are working with nearshore development services to develop new products with confidence. Traditional product development was essentially a shot in the dark. However, data analytics allows companies to collect millions of data points from existing consumers–data that can be used to easily identify unfulfilled customer needs.
In addition, data science is already helping businesses tailor their services to specific customers. Banks are using customer demographics and spending patterns to offer custom services to their clients, such as freezing an account when suspicious and out-of-character spending occurs.
Bank of America is at the forefront of data science in the finance industry. Their new chatbot, Erica, uses data analytics and predictive software to help customers accomplish tasks and to offer new services. This sophisticated AI is capable of helping clients apply for a loan, schedule credit card payments, or replace a stolen debit card.
Improving Healthcare Outcomes
The healthcare industry is rapidly becoming a leader in the data science field. That’s because despite recent advancements in medical technology diagnoses are still performed by physicians or radiologists, and are subject to human error.
In fact, research has shown that an estimated 5% of diagnoses are incorrect, impacting roughly 12 million patients each year. Furthermore, these diagnostic failures result in 40,000 to 80,000 annual deaths as well.
Many healthcare organizations are relying on nearshore software development partners to build custom data analytics software. They’ve used the Python programming language to create extensive healthcare databases and sophisticated data science software that is already beginning to positively affect the quality of healthcare in the United States.
Today, healthcare providers are beginning to use these hundreds of millions of patient records to analyze trends and help physicians diagnose diseases early on. Furthermore, research organizations are using this data to identify the best treatment protocols for specific diseases.
The data analytics company IQuity is an excellent example of healthcare data science in practice. The company used data from more than 20 million insurance claims in New York to analyze how patients were diagnosed with multiple sclerosis. IQuity then used this data to create a custom algorithm that can predict MS “at least 8 months” before the traditional, physician-based approach would diagnose the disease.
Rapid Developments in Industrial Technology
The American manufacturing industry has struggled to keep pace with international competition and changing consumer demand over the half-century. In fact, the U.S. manufacturing industry had roughly the same amount of workers in 2019 as it did in 1949–roughly 12.8 million people. In addition, the manufacturing industry’s share of U.S. employment fell from a historical high of 30% in 1949 to just 8% in 2019.
However, the U.S. industrial manufacturing industry is using data science and machine learning to revitalize itself. In fact, they employ nearly 20% of all data scientists. This focus on smart manufacturing led the industry to hire more workers in 2018 than they added in any year since 1988.
Industrial manufacturers have used nearshore software outsourcing to build predictive analysis software. By analyzing historical production and sales data, executives are reducing overproduction, idle time, surplus inventory, and other inefficiencies. In addition, big data is being used to hire workers during the busiest times of the year and reduce payroll during slow periods.
Companies are also using data science to predict machine failures and schedule preventive maintenance. Equipment failure costs businesses money because a single broken piece of equipment can halt production and cause orders to go unfilled. Now companies can avoid these production delays by predicting machine failures and implementing a data-backed preventative maintenance schedule.
Maintaining Data Privacy
Nearly a half billion individual consumer records were accessed during data breaches in 2018. The vast majority of these cyber attacks were directed at consumer businesses, financial services, and healthcare providers.
The rise of cloud computing and data science means that billions of sensitive personal records are stored online–with the number increasing every year. These records are often poorly secured and vulnerable to attack. This is why American CEOs ranked cybersecurity as their number one “external concern” for 2019.
Many companies are turning to nearshore development services to shore up their cybersecurity defenses and to protect their consumers’ sensitive data. The best outsourcing partners use multidisciplinary development teams to integrate data protection into every stage of the software development life cycle.
These collaborative development teams consist of software engineers, cybersecurity experts, design specialists, and testers. They ensure that important priorities like data privacy are included in the initial structure of the software, as well as every subsequent iteration.
In addition, nearshore development partners are at the forefront of security testing field. They help businesses perform key security analyses, such as threat modeling, static and dynamic application tests, and penetration tests, to ensure that sensitive data remains secure.
Paul Azorín is the Founder and Chief Technology Officer at BairesDev. He is responsible for coordinating the technology department as well as the Presales team. Paul also leads the Marketing and Communications team and works passionately to communicate the identity and values of the company.
A Developer by trade, Paul led a software development team at Hewlett-Packard and was a Project Manager for Electronic Arts and Nike before founding BairesDev. He was always recognized as an Agile Methodologies Evangelizer and a developer with a keen eye for business opportunities. Noticing the potential for Latin American Software Exporting, he decided to start BairesDev.