How to Take Advantage of the Data Science Revolution

The data science revolution is here. The largest companies in the world have spent the past several years investing billions of dollars in data analysis--with research showing that over 91% of Fortune 1000 companies are ramping up their data analysis and artificial intelligence (AI) investments.

Corporate investments in data science are one reason why the industry is estimated to be worth more than $77 billion by 2023, with an average annual growth rate of 30.08%.

Businesses are responding to the data revolution by hiring experienced data scientists in droves. However, the demand for these specialists is outpacing supply--causing many companies to work with IT staffing services to find these experts as-needed.

Investments in data analysis today will pay huge dividends in the future. Companies can set themselves up for future success by using the following data science strategies.

Hire the Right Talent

The single-most important step in improving data strategies is to hire the right people. Data scientists require a unique and rare combination of skills to be successful. They’re required to be experts in computer science, statistics, machine learning, and more.

This unique combination of skills is difficult to train in current programmers, meaning that most data scientists are coming directly out of universities. However, universities simply cannot keep up with demand and are not producing enough graduates to meet corporate demand.

For example, the number of job openings for data scientists has increased by 75% over the past three years. Yet, only 29 of the world’s top universities offer data science programs--with only six offering undergraduate degrees in the discipline. The same study found that the number of graduates in data science is unlikely to make a "meaningful dent in closing the global data science talent gap."

That’s one reason why companies intent on hiring U.S.-based talent should begin recruiting data scientists well in advance of graduation. That’s because there are a limited number of schools offering data science programs, with most universities graduating less than 25 students per year.

This shortage of talent is one reason why the global demand for data scientists currently outstrips supply by more than 50%.

Businesses can avoid competing for the limited number of data scientists in the United States by working with a high-quality offshore development firm that specializes in data science. These companies work with an array of experienced data scientists who are ready to begin their next project on-cue.

Improve Data Collection

Data collection is the heart of data analytics. Companies must collect and store the right data first in order to analyze it and identify important trends later on.

The first step in this process is getting buy-in from stakeholders. For data collection and subsequent analysis to succeed, managers must first get support from company leadership, owners, and other crucial stakeholders.

Next, look at what type of data is needed and accessible. Financial service companies have very different data collection needs than retail businesses. A bank, for example, will likely want to collect information on income level, spending habits, their relation to other customers, and their family status. They can later use this information to sell specific services or to prepare for their customer’s life events, like having a baby or purchasing a car or home.

They may also want to collect valuable data that relates to processes. For example, what percentage of people leave a credit card application unfinished. What step of the process do they typically give up at? This type of data can help companies improve their communication process, website user design, and offer proactive help and advice for the most difficult step of the process.

Once the target information has been identified, it’ll be time to work with a data scientist to figure out how to collect, store, and organize this information.

Use Predictive Analytics to Plan for the Future

Predictive analysis is the final and most important step in the data analytics process. It involves processing the huge amount of data gathered in the previous stage to look for patterns that can help a business plan for the future.

This discipline is where data scientists really shine. It requires a solid knowledge of computer science, statistics, general mathematics, AI and machine learning to complete successfully.

Businesses can improve their predictive analysis efforts by first organizing a Big Data lab. This group of specialists can test out data analytics prototypes before large investments are made. Such an arrangement helps companies test out early software iterations while still collecting usable insights that they can implement today.

In addition, companies should slowly focus on reducing the amount of data collected, or at minimum, analyzed regularly. That’s because information overload can prevent executives from seeing the most important insights when they are lost in a sea of numbers.

Finally, businesses have to figure out how to best disseminate this information. A Big Data department can have the best insights in the world, but that information is only useful if it's shared with the right executives and front-line managers. Create a strategy to ensure that this information is given to those who can use it the most

Develop a Data Privacy Strategy

Data privacy is the number one external challenge faced by American executives today, regardless of their industry. That’s because the number of data breaches continues to increase every year and because research shows that hackers attempt to break into another computer "every 39 seconds on average."

Businesses are responding to this crisis by investing huge sums of money in cybersecurity. The staggering amount of investments in data privacy is one reason why the cybersecurity market is worth an estimated $167 billion today and is predicted to be valued at nearly $250 billion by 2023.

This reality is enough to keep most executives up at night. It’s even more worrying for those whose companies are investing in Big Data. That’s because data analysis requires a huge amount of information to succeed--information that hackers find very valuable.

Businesses must begin investing in data privacy today to avoid embarrassing data breaches and reduced customer trust in their brand. The best way to do this is by following the financial services industry’s lead. Finance companies currently spend an estimated $2,300 per employee per year on cybersecurity.

Executives will need to coordinate the efforts of the software development and data science departments to ensure that cybersecurity protections are integrated into each step of the development process and that information is stored securely in the cloud.

In Summary

Data science will determine who succeeds and who fails in the future. That’s because predictive analytics helps businesses understand their customer’s habits and needs, utilize past data for future sales, and determine which products have the best market potential.

Businesses are already investing billions of dollars in Big Data and hiring specialists in droves. However, the demand for experienced data scientists is much greater than the number of trained data scientists in the United States. That’s one reason why companies are working with IT staffing companies to source these specialists as-needed.

Companies can prepare for the Big Data revolution by collecting better data, analyzing that data effectively, and using predictive analytics to accurately plan for the future. Finally, all businesses must have a strategy for safeguarding the massive amount of data collected through these efforts.

Pablo 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 works passionately to communicate the identity and values of the company.