Essential guide to a successful career in big data - Rest Nova Site

Essential guide to a successful career in big data

Essential guide to a successful career in big data


Modern technological infrastructures now enable data storage, processing and analysis at a rate considered almost impossible decades ago. This avalanche of information, from social media posts and online transactions to machine logs, makes up big data. While the name suggests what big data is all about, it’s not only about the size of the data; it’s the incredible speed this data is created and processed.

Big data allows organizations to gain deep insights into customer behavior which helps them customize their offerings and improve customer experience. Companies also use big data to improve their operational efficiency. How? Big data uncover trends and patterns that can highlight inefficiencies and opportunities for companies to improve them. This helps improve operations in diverse industries, from healthcare and finance to retail and transportation.

Big data’s extension into many sectors opens career opportunities for individuals. One such avenue is what a degree affords you. St. Bonaventure University offers students the chance to gain the right skills and mindset to make significant impacts and drive innovation in their industry. Their online business analytics master’s degree trains students for future career paths in big data. The program is a fully online degree that students can complete within two years.

Now, if you want to know the crucial things before you delve into a career in big data, read on. This article will discuss the various roles, required skills and educational pathways and will give you practical tips on how to embark on an exciting career.

What is big data?

Big data are huge and complex data sets that traditional data processing software can’t handle. Think of it as a giant digital library containing everything from social media posts, emails and videos to weather patterns, financial transactions and medical records.

These data sets are characterized by the five Vs. There’s volume, velocity, variety, value and veracity. Volume is the amount of data generated every second from different sources. Velocity is the breathtaking speed at which this enormous data is processed, and variety is the different types of data available, including structured data (like databases), unstructured data (like social media posts) and semi-structured data (like XML files). Value highlights the usefulness of properly analyzed data, and veracity underscores the trustworthiness of the data.

Big data reveal patterns, trends and insights about human behavior and interactions. Analyzing large amounts of information on big data can help businesses, researchers and governments make better decisions.

How is big data utilized?

Big data is revolutionizing numerous industries with its innovations and dynamic use cases. Some of the ways that organizations utilize big data include:

1. Enhancing Customer Experience

Big data familiarize businesses with their customers on an unprecedented scale. It used to be a hassle for companies to understand what customers like and offer their service tailored to their customers’ preferences. Managing and analyzing large amounts of information, such as customers’ past purchases, browsing history, demographics and real-time behavior, can significantly change how well companies interact with their customers.

For instance, e-commerce giants, like Amazon and Alibaba, analyze customer data to help them offer personalized product recommendations. This way, if a customer buys a paranormal romance book on Amazon, predictive models can recommend more paranormal-themed books to them. Predicting what customers want with big data is a two-way success. It makes online shopping more enjoyable for them and enhances the company’s marketing strategy.

2. Operational efficiency

Organizations use big data to identify inefficiencies in their operations, like supply chain bottlenecks or low-quality inventory. Since big data provides patterns and trends from existing data, it becomes easier for companies to discover past mistakes and make amendments for future purposes. An organization might notice that a process takes longer than it should, or a product isn’t selling as it should through customer behavior on those factors.

Identifying these issues help them make improvements to increase their efficiency and profitability. For instance, airlines might use big data to optimize flight paths. Based on past data, they try to find the most cost-effective and efficient route from one location to another. They may consider recorded wind patterns, weather conditions and fuel consumption. This optimization will minimize flight time and fuel costs, increase passenger comfort and reduce unnecessary financial costs.

3. Risk management

The financial sector uses big data to assess risk and detect fraudulent activities. For example, if a client has had a withdrawal limit on their account for years and suddenly withdraws more than the limit, big data helps the bank discover this anomaly. The bank can then notify the individual of the change to know if it’s from them or if there’s been a breach on their account. This helps banks keep their customers safe and also solidifies customers’ trust in their bank.

4. Healthcare improvement

Big data helps revolutionize healthcare by analyzing past data and relevant information from patient records. This helps medical organizations facilitate personalized treatment plans and predict health outcomes. They can analyze electronic medical records, wearable-device data and study genetic information from patient records.

This analysis makes it easier for healthcare practitioners to develop strategies tailored to a patient’s genetic factors, lifestyles and environmental exposures. This automatically leads to more effective treatment that minimizes side effects.

Another way the healthcare sector can utilize big data is through predictive analytics. Healthcare professionals can use data and advanced algorithms to predict future health outcomes. How? Doctors can anticipate the risk of chronic diseases, such as diabetes or heart disease, by identifying patterns in large datasets. This prediction allows for early intervention and reduces the disease’s impact.

5. Supply chain management

Big data helps optimize supply chains by providing immediate information about demand, inventory and transport conditions. This information can include customer demand trends and transportation conditions, such as weather, traffic or shipping delays. Companies, like Coca-Cola, can analyze datasets, like past sales records, economic indicators and even social media trends, to predict how high their future demands will be.

6. Product development

Companies rely on big data to inform and guide their product development processes. This involves analyzing various data sources, such as industry model standards, online reviews and direct customer feedback. These digital conversations can reveal what features or qualities consumers value most in a product. It can also show the issues with current offerings or desires that a new product can meet.

Five career options in big data

Big data opens individuals to several career opportunities, and courses, like St. Bonaventure’s business analytics program, prepare them for these opportunities. Some of the career options in big data include:

1. Big data engineer

Any organization dealing with vast amounts of data needs a big data engineer. These professionals are responsible for designing, building, testing and maintaining the systems and tools to handle big data.

Big data engineers are the architects of the data systems within an organization. They create the blueprint for data-processing systems to handle the influx of data. They also select the appropriate software and hardware an organization should use, ensuring the system is secure.

For example, a big data engineer working with Google might be responsible for developing systems that handle data from billions of search queries daily. A professional working in a financial institution might build systems that detect fraudulent transactions in real time.

One of their primary tasks is to develop, construct and test technological architectures, such as databases and large-scale data-processing systems.

Big data engineers ensure the data infrastructure is available and optimally functioning, then give data analysts and scientists the go-ahead to perform their tasks. These professionals can work in any industry that generates and utilizes voluminous data, from technology and finance to healthcare and retail.

2. Data scientist

A data scientist uses scientific methods and processes to extract knowledge and insights from data. These professionals digest and interpret complex data to help organizations make decisions. Simply put, data scientists take in data and bring it back out in ways others can understand and get insights from it.

Like big data engineers, data scientists can also work in any organization collecting and using large amounts of data. Employers seek these professionals in the technology, finance, healthcare, marketing and retail sectors. Prominent companies, from tech giants, like Google and Amazon, to financial institutions, like Goldman Sachs, employ data scientists.

Some of the roles of data scientists in an organization include:

● Data analysis: They explore and analyze large datasets to extract meaningful insights. This usually entails identifying trends and anomalies in data to help a business improve its operation, products or services.

● Predictive analytics: Data scientists can use machine learning algorithms to predict future events or behavior. A data scientist in a company might develop a model that predicts customer churn, helping the company take measures to retain those customers.

● Data visualization: They create visual representations of data. Non-tech decision-makers in a company need someone to digest and rewrite data in a way that they’ll understand, and data scientists can provide data-backed insights to these executives to help them make strategic decisions. An example could be creating dashboards showing key performance indicators (KPIs).

3. Business intelligence (BI) analyst

Business intelligence analysts turn data into insights. They sift through large amounts of data to identify business and market trends and correlations that can provide actionable insights. Business intelligence analysts use several tools and methodologies to visualize data patterns, create reports and design dashboards that transform complex data into understandable and usable data.

BI analysts can get employment opportunities in the finance, healthcare, consulting, retail and technology sectors. These analysts might be open to more options with the rise of data usage in other industries, like manufacturing and logistics.

Some other job roles BI analysts should expect in an organization include reporting, as they’ll often create detailed reports, such as charts and graphs, showing the results of their data analysis in a way that non-technical stakeholders can understand.

These professionals also ensure the accuracy and reliability of the data used. This responsibility includes cleaning data, handling missing data and verifying the integrity of the data. Experienced BI analysts are also usually tasked with training other staff members to use data tools and interpret data visualizations.

4. Database manager

Database managers, or Database Administrators (DBA), maintain and manage an organization’s database systems. They ensure the performance and security of databases to protect the operations of modern businesses. That’s one part of their job role.

Database managers also plan and develop databases, monitor their performance and prepare to expand them. Similar to Lea Dilallo in an episode of Good Doctor, they also maintain backup and recovery plans, ensuring they can retrieve the data in case of hardware failure or cyberattack.

Database managers also ensure that their organization’s database is secure. How? They develop and implement security protocols, monitor access and protect sensitive information to prevent data breaches and loss of data.

DBA’s career opportunities are broad. Almost every sector, including government and healthcare, relies on databases for storing and accessing critical information. This means they need a DBA to maintain and protect their databases, giving these professionals job opportunities in almost all industries.

5. Data analyst

A data analyst processes statistical analyses on large datasets. They discover how to use data to answer questions and solve problems. They also use software tools to interpret complex data and draw a conclusion from the analysis to present straightforwardly that non-tech business stakeholders can understand.

Data analysts can work in various settings. For instance, a data analyst working in a tech company can work with product teams to understand user behavior, identify key metrics and recommend product improvements based on their analysis.

On the other hand, data analysts working in a financial service firm might use data to detect fraudulent activities and assess their firm’s financial risks. They might also create models to predict economic trends and inform investment strategies.

Essential education and training needed for a career in big data

Individuals looking to start a career in big data must have a solid foundation in mathematics, statistics, data analysis and computer science. Some of the necessary education and training they need include:

1. Formal education

A bachelor’s degree in mathematics, computer science, data science or statistics is often a baseline requirement. These programs mainly cover essential concepts, like algorithms, data structures, probability and statistical methods.

2. Graduate degree

A master’s degree or Ph.D. in a related field also provides more advanced knowledge, especially if the individual is dealing with complex data for big companies.

3. Coding skills

Big data enthusiasts must be proficient in programming languages, like Python, Java, or R. The data science community commonly uses Python, particularly due to its simplicity and the numerous available data analysis libraries, such as Pandas and NumPy. Other programming languages include SQL, Scala, Julia and MATLAB, which help analysts query, process, perform tasks in parallelism and visualize data.

4. Online certifications and courses

Numerous online platforms offer courses and certifications in big data technologies. Websites, like Coursera, Udemy and edX, host courses from universities and companies, like Google and IBM. Certifications can provide a structured learning path and demonstrate your skills to potential employers.

5. Real-world experience

Nothing beats practical experience in the technological world. Enroll for internships, co-op programs and entry-level roles providing hands-on real-world data experience. You can also work on your projects or compete on platforms, like Kaggle.

Skills and experience needed to ace big data

Growing a career in big data requires a blend of technical, analytical and soft skills. Here’s a breakdown of the critical skills and experience you need besides technical skills:

1. Analytical skills

Big data enthusiasts must have a strong foundation in statistics and mathematics. These subjects are critical to understanding data and building effective models. They must also be able to approach complex problems methodically, applying analytical thinking to propose solutions.

This means that these technologists must have solid problem-solving skills. Besides this, they must also sharpen their competence in interpreting data and drawing conclusions, helping them to make spot-on data-driven recommendations.

2. Soft skills

It’s popularly believed that data professionals are stand-alone individuals. However, this is untrue. These professionals also need certain soft skills, like communication and teamwork. Communication helps them explain complex data to non-technical stakeholders in a straightforward manner that won’t confuse non-tech individuals or frustrate the professional.

Many big data projects involve cross-functional teams, including data scientists, software engineers and big data engineers, and it’s crucial to communicate with your team to ensure easy and effective collaboration.

One extra skill every data professional must have is curiosity. Engage your drive to explore data, ask questions, and seek new methods or technology. Sometimes, curiosity is all you need to improve your capabilities.

3. Networking

Networking is critical when building and growing any career, including a job in big data. Here are some strategies for effective networking in this field:

● Attend industry conferences and workshops.
These events offer opportunities to meet industry professionals. You can learn the latest trends in big data, showcase your knowledge and catch potential employers’ eyes.

● Join online communities and forums.
Online platforms, like Stack Overflow, GitHub and Kaggle, are hubs where data professionals ask questions, share knowledge and collaborate on projects. Participating actively in these communities can help you connect with like-minded individuals.

● Professional networking platforms
LinkedIn is an essential tool for networking. You can meet and connect with professionals in the field, join big data-related groups and participate in discussions. Regularly updating your profile and sharing content related to big data can also attract networking opportunities.

● Enroll in educational programs.
Enrolling in big data courses or degree programs can provide networking opportunities with instructors and fellow students. This program can also lead to study groups, project collaborations and job opportunities.


A career in big data promises engaging work and significant rewards, intellectually and financially. The world is increasingly moving toward data-driven decision-making, opening opportunities to big data enthusiasts alongside long-term relevance and stability.