A few of years ago, an English academic described a thought experiment designed to visualize just how much data is captured and created in our highly digital world. When you add up the hundreds of millions of tweets, billions of email and WhatsApp messages, hundreds of thousands of hours of YouTube content (and Facebook data and information collected by sensors, and on and on), you reach the 2020 total of 59 zettabytes.
It takes a bit more math to get to the visualization part. Each zettabyte has eight sextillion (that’s an 8 followed by 21 zeroes) bits of data. If each bit was represented by a .1-inch-thick coin, one zettabyte worth of coins could be stacked together to rise to the nearest star system 600 times. Of course, the data total of 2020 is quaint compared with how much data we generate and consume and store today, and how much it will grow in the future.
Coins stacked to distant stars is a pretty good way to begin understanding what the term “big data” really means. But the truth is that big data is also a massive career opportunity for those who have the necessary skills and experience — which can come from both the traditional higher education route as well as training and certification programs. The reason data-related careers are booming is because there’s so much data being created, it takes a mix of sophisticated technology and people to organize all that information, analyze it, and ultimately use it to make decisions and investments that improve an organization’s prospects and effectiveness. Here are some of the top jobs in big data.
Among the jobs topping the Bureau of Labor Statistics (BLS) list of fastest-growing occupations through 2031 is data scientist — a role expected to expand by more than 35 percent. It’s not just the growing number of jobs that make this a position worth considering. The median pay for data scientists in 2021 was over $100,000. It’s not hard to see why data scientists are in such high demand. Using their skills in everything from math and statistics to computer science, data scientists can take the sprawling sets of data that organizations collect, interpret them, and communicate their relevance. This can involve creating algorithms to recognize patterns and trends and building models that can provide a glimpse into the future. The best data scientists have a knack for using simple charts and graphs to make complicated concepts easy for the rest of us to understand.
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A common phrase in the big data universe is “garbage in, garbage out.” It’s a warning, really, about the need to assemble quality data for algorithms, machine learning, and other uses that professionals rely on to glean insights. Data collectors don’t necessarily require a college degree, and their work entering information into a database and verifying its accuracy is critical to leveraging the potential of data to inform good decisions. The average salary of data collectors is nearly $60,000, though some earn more than $90,000.
All organizations include a mix of people and processes that guide what employees do each day. Now that companies are collecting a large amount of data — and have whole departments devoted to that function — understanding how this avalanche of information can be used to continuously improve the organization has become a challenge. Business analysts play a big role in evaluating both past and current data to identify ways to improve efficiency and effectiveness. It’s a job that requires the technical and analytical skills to evaluate data, and it also necessitates strong communication skills so the analyst can make a convincing case about how an organization should implement what the data is showing.
Data models are ubiquitous in organizations these days. These models are meant to organize information and relationships inside and outside a company in a coherent way. For example, a data model for a bookstore could include rectangles representing books, customers, and orders with arrows connecting each, with more detailed information inside each rectangle — like the title and author of a book, and the shipping address for a customer. These models can be vastly more complex and sophisticated, but their main purpose is to organize and represent a company’s data so that a business can become more efficient and make better decisions. A data modeler, not surprisingly, is charged with creating these models. It’s a job that demands a range of skills, including programming and statistical analysis. It also requires a close collaborative relationship with professionals in the IT department, including software developers and database administrators, who oversee much of the data companies collect.
All data needs a home. Every day, companies collect large amounts of data, from their transactions and communications with customers to website and social media activity, to employee information. Organizing and storing all that information so that it can be easily accessed and analyzed is the job of a database developer, who is charged with designing and building the databases organizations need to house their information. This requires coding savvy so that databases function properly, along with expertise in specific database technologies, like SQL and Oracle. While technical skills are a must, database developers also need to work with executives and others to ensure that the database captures relevant data and can be used easily by those who lack deep technical knowledge.
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Database managers have a symbiotic relationship with database developers. While developers design how a database is configured to store information, database managers are responsible for its day-to-day operations. This role can include ensuring the database is continuously monitored and regularly backed up. The job also requires working with colleagues to ensure they can use the database effectively. To succeed as a database manager, it’s important to have a mix of technical and communication skills. Among the technical requirements are proficiency in data security tools and concepts as well as an understanding of database management systems. Project management, communication, and problem-solving skills are also necessary. The average annual salary for database managers is around $60,000, though some earn over $100,000.
Data can provide companies and organizations with a significant competitive advantage. Accurate market and customer information, for example, can help guide new products and services and the investments needed to support them. But collecting data also results in vulnerabilities that can seriously harm a company’s reputation and its customers. For instance, in 2022 there were 1,802 data compromises impacting 422 million people around the globe. Data security analysts are responsible for protecting an organization’s digital information from a large and multiplying array of threats posed by cybercriminals. It’s a job that involves identifying where a company may be vulnerable to cyberattacks and developing protections against them. Importantly, it also means training a company’s employees to ensure they don’t unwittingly abet cybercriminals.
Lately, tech industry layoffs have been in the headlines. But a recent report by the Computing Technology Industry Association made it clear that despite the negative news, the tech sector is adding jobs. In fact, the association forecast that the industry would add more than 270,000 jobs in 2023, an increase of about 3 percent over 2022. That means there is a lot of hiring taking place, and the work of technical recruiters is in demand. A technical recruiter, a role that doesn’t always require a college degree, is responsible for finding candidates for open tech positions, screening applicants, and ultimately helping to hire them.
Harnessing the full potential of data — especially large data sets — is something people can’t do alone. There’s simply too much information to process and analyze. Machine learning models are algorithms embedded with statistical capabilities that allow them to identify patterns in data and either automatically make decisions based on those patterns or inform company employees about their options. Machine learning engineers develop the algorithms companies increasingly rely on to make better decisions. The job requires strong technical skills, particularly in programming languages like Python and Java, as well as machine learning libraries like TensorFlow. Machine learning engineers must also have a strong grounding in math and statistics.
Note: “Find Your School” examples are listed in no particular order and are intended to be a sampling of the many colleges and universities with departments and programs that prepare aspiring professionals in fields related to managing data. Employers and job openings were current when we researched this story and are intended to serve as examples only to illustrate the diversity of opportunities in this field.
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