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Mastering Data Science: Answers to the Most Common Questions and Answers

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Data science is no longer an interesting idea–it’s now a mandatory investment for companies intent on remaining competitive. That’s why major corporations are hiring Python developers en masse and why more than 91% of Fortune 1000 companies are increasing the size of their investments in data analysis and artificial intelligence (AI). 

This corporate focus on data science is one reason why the business analytics market is worth an estimated $189.1 billion in 2019. Experts believe that growth in this sector will continue to accelerate, with the big data and business analytics market topping $247.3 billion by the end of 2022. 

However, while companies are increasing their financial investments in business analytics, several key challenges prevent businesses from taking full advantage of the opportunities that big data offers. 

First, many businesses are struggling to source the right candidates for their projects. The gap between the demand for data scientists and the number of graduates in this discipline continues to grow. In addition, most companies still struggle to collect and retain data in an organized manner.

In addition, even the most cutting-edge tech companies struggle to create the right goals for their predictive analytics team. Finally, weak data privacy and cybersecurity protections mean that vital consumer data, like social security numbers, banking information, and passwords, are often left exposed on the cloud.

What is Data Science and Why is it Important?

Data science is one of the most important concepts for executives of the future. It involves collecting valuable data about the business and its customers, including purchase and browsing history, historical sales numbers, and relevant industry-specific data. All that can then be analyzed and used to create predictions about the future.

This process begins with data mining, often called Knowledge Discovery by software engineers. During this step, businesses collect the type of data mentioned above and organize it in a way that can be easily read by AI-driven data analysis programs. 

Once data has been collected, it’s time to begin the predictive analysis process. This is an advanced discipline that combines aspects of computer science, statistics, general mathematics, machine learning, and AI to create accurate and meaningful predictions about the future. Executives can use these predictions to determine how to position the business for future success. 

A prominent example of data science in action is the list of related products that customers see when they browse the popular online marketplace Amazon. The company’s proprietary algorithms use individual customer’s past browsing habits, along with those of similar customers, to offer related products that the person is likely to be interested in. 

Common Data Science Challenges & Solutions

The data science and predictive analysis process is a relatively straightforward concept. Yet, it is difficult to implement it correctly in reality. It requires experienced data science specialists, a well-thought-out data collection process, and clear predictive analysis goals.

In addition, a successful data science initiative will always prioritize data privacy and information security from the outset. Collecting and storing such sensitive consumer data also demands a commitment to keeping that same data protected from hackers and others that seek to cause harm.

That’s only the beginning. Some of the most common challenges in data science include the following. 

Lack of Qualified Employees

One of the most important and intractable problems facing executives interested in Big Data is the lack of qualified data scientists. Companies simply cannot launch a predictive analytics initiative without enough experienced employees.

This process is made even more difficult because of the unique skill set required by data scientists. Qualified candidates must possess a degree in the discipline and understand advanced concepts in statistics, computer science, mathematics, and AI. 

Universities have been slow to offer degree programs in data science, especially undergraduate programs with large class sizes. As a result, only 29 of the world’s top universities offer data science programs today, with most programs graduating less than 25 students per year. Even more troubling, only six of those programs are undergraduate degrees. 

The shortage of qualified data scientists graduates is happening at the same time that the number of job openings for these specialists has increased by 75% over the past three years. Research has found that the number of graduating data scientists is unable to make a “meaningful dent in closing the global data science talent gap.” 

Because the demand for data scientists outstrips supply by more than 50% in some reasons of the world, many companies are turning abroad to source the right talent. Businesses looking for qualified data scientists may need to consider working with a Python development service to supplement their in-house team.

Poor Data Collection & Retention

Data collection is the foundation of Big Data. Companies cannot analyze data and create meaningful predictions if their data is poor, corrupted, or stored improperly. 

The first and most important step is to secure institutional support from key stakeholders. It’s vital that the data science team has support from C-Suite executives and middle management. This will ensure that they have the right budget, resources, and support from related teams to accomplish their goals.

Next, businesses will need to carefully decide what type of data to collect. This will vary greatly according to the industry. Financial services firms will likely want to gather information on customer income, spending habits, and financial goals. Retail companies, on the other hand, will likely want to collect data about their customers’ shopping habits, favorite brands, and monthly discretionary budget. 

Regardless of what type of data the business chooses to collect, it’s important to think ahead and establish predictive analysis goals before launching the data collection process. This will make sure the data science team is collecting data that’s relevant to their goals.

Unclear Predictive Analysis Goals

Once enough data has been collected and stored, it’s time to launch the predictive analysis process. This involves creating algorithms and AI systems that can look through the vast amount of data for statistically-significant patterns that can help executives plan for the future. 

While the process begins after the data collection has been completed, the predictive analysis goals should be decided early on. That’s because the goals will determine which data is collected and how it’s stored.

However, many companies fail to plan properly. They collect reams of data that are later found to be useless. This often forces them to restart their data collection process from the beginning, wasting valuable time and money.

Businesses can plan for the future and ensure success in their project by creating a Big Data lab. This group of specialists can test out data analytics initiatives before too much time or money has been invested. 

In addition, they can help the larger data science team eventually reduce the amount of data that is collected. That’s because predictive analysis will help the team identify which data points are most important for their work, and ignore data that is invaluable or will distract them from the most consequential insights. 

Lastly, the Big Data lab can coordinate information sharing between the larger data analytics team and the departments that can make use of these insights.

Weak Data Privacy Controls

Information security continues to be the single most important “external concern” for American CEOs. It remains so important because the number of data breaches continues to increase in number every year. Even more frightening, hackers are beginning to integrate AI and other cutting-edge technologies into their attacks. 

The world’s biggest companies are responding to this crisis by increasing investments in information security. Experts have found that the cybersecurity market is worth an astonishing $167 billion today and will reach a $250 billion valuation by 2023. 

Businesses investing in Big Data should be even more concerned. Every single data point collected as part of a predictive analytics initiative is a target for hackers–and companies have an obligation to safeguard the personal information they possess.

Executives should create a thorough information security strategy during the earliest stages of their data analysis planning. In addition, it’s vital to bring software security experts in during the building of data science software and regularly thereafter, ensuring that this crucial information continues to be protected even as new threats emerge. 

In Summary

Investments in data science today will lead to major benefits for companies down the line. Big data is helping executives increase sales and build relationships with current customers, find new clients, and make informed decisions about the future of their business using predictive analytics.

However, businesses that embrace Big Data are quickly realizing that they must overcome a set of crucial challenges in order to be successful in this space. First, executives must hire the right type of qualified data science experts. The data science talent shortage makes this extremely difficult, which is why many companies are working with Python development outsourcing firms to find experienced candidates. 

They’ll also need to carefully select the best data collection and retention methods for their needs. This crucial step ensures that businesses are collecting the right data and storing it in a way that allows for easy analysis. 

Companies will also need to set clear goals for their predictive analytics team, allowing them to organize data and build algorithms that will shed insight on the most important challenges that their business faces. Finally, executives must create a clear and effective data privacy strategy before launching their data collection and analysis efforts.

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