What Does a Data Analytics Consultant Do?

Data analytics is useful in nearly every enterprise, from farming techniques to online fashion retail. Wherever large sets of numerical information accrue, a data analyst can figure out how to mine them for useful insights.

Data analytics consultants use data sets and models “to draw meaningful insights and solve problems,” according to the Northeastern University graduate programs blog. As a data analytics consultant, you might work in:

• A consulting firm
• A corporate setting
• Pajamas (if you’re self-employed)

With a little experience, education, and know-how, you can earn one of the 2.3 million jobs that seek candidates skilled in analytics. You might be required to put in long hours and solve complex problems, but the compensation should make the effort worthwhile: the average salary for analytics consultants is $96,616, which is about $40,000 higher than the average for all workers.

Wondering what else you need to know about becoming a data analytics consultant? In this article on what does a data analytics consultant do, we discuss:

• What does a data analytics consultant do?
• Education requirements for becoming a data analytics consultant
• Certifications needed for a career in data analytics consulting
• Should data analyst consultants go to graduate school?

What does a data analytics consultant do?

The requirements may vary based on individual jobs, but being a data analytics consultant generally means immersing yourself in numbers and then translating your findings for the people who need to use them. To put this in geekier terms: if data analytics is The Matrix, you are Laurence Fishburne, and you need to bring your colleagues (Keanu Reeves) in. Note that data science—though a closely related profession—is different. Where data analysts focus primarily on data sets and tangible guidance, data scientists focus more on creating models and speculative projections.

What skills do data analytics consultants need?

There are so many skills a data analyst can have that you’re probably not going to have every one, especially starting out. Keep in mind that certain skills will get you further than others.

Top skills that every data analyst consultant should have include:

• Data Visualization
  • Essential for presenting data insights in an understandable and impactful way.
  • Helps in identifying patterns, trends, and outliers through visual representation.
  • Tools like Tableau, Power BI, and matplotlib (in Python) are commonly used.
• Microsoft Excel
  • Fundamental tool for data manipulation, analysis, and visualization.
  • Widely used for its versatility and ease of use in handling data.
  • Includes features like pivot tables, charts, and data analysis toolpak.
• R (alternatively Python) Programming Knowledge
  • Crucial for statistical analysis, machine learning, and data manipulation.
  • Python is known for its extensive libraries like pandas, NumPy, and scikit-learn.
  • R is preferred for its powerful statistical packages and visualization capabilities.
• SQL
  • Essential for querying and managing relational databases.
  • Used to extract, manipulate, and analyze data stored in databases.
  • Important for integrating data from different sources and ensuring data integrity.
• ChatGPT
  • Can assist in generating natural language summaries of data insights.
  • Useful for automating data reporting and creating interactive data assistants.
  • Enhances the ability to communicate findings effectively and efficiently.
• Prompt Engineering
  • Key skill for effectively utilizing AI models like ChatGPT.
  • Involves crafting prompts that elicit the most relevant and accurate responses.
  • Important for developing custom AI solutions tailored to specific data analysis needs.

The good news is your expertise transfers from one field to another. While analysts sometimes specialize in a specific field, you don’t have to. If you know how to work numbers really well, it won’t matter if you are crunching them for a large trading firm or an NBA basketball team.

That said, it can be beneficial to find a niche and become an expert. According to 5 Tips for Wharton MBAs Interested in a Career in Data & Analytics, specialization provides not only a “solid understanding of the core principles” of your industry of choice but also a familiarity with “the atmosphere, work/life balance, and recruiting norms” of that industry.

Depending on the way your career advances, it might be useful to acquire familiarity with other skills and programs, including:

• Amazon Web Services (AWS)
  • Provides scalable cloud computing services for data storage, processing, and analysis.
  • Tools like Amazon S3, Redshift, and EMR facilitate big data management and analysis.
  • Enhances data analytics capabilities with flexible and cost-effective infrastructure.
• Database Management
  • Involves designing, implementing, and maintaining databases to store and organize data.
  • Ensures data integrity, security, and efficient retrieval.
  • Utilizes database management systems (DBMS) like MySQL, Oracle, and PostgreSQL.
• Data Mining/Data Warehouse
  • Data mining involves extracting useful information from large datasets.
  • Data warehouses store large volumes of structured data for analysis and reporting.
  • Facilitates historical data analysis, trend identification, and decision-making support.
• Hadoop
  • An open-source framework for distributed storage and processing of large datasets.
  • Enables handling of big data through its Hadoop Distributed File System (HDFS) and MapReduce programming model.
  • Essential for scalable and efficient data analysis across multiple servers.
• LaTeX
  • A document preparation system used for creating high-quality technical and scientific documents.
  • Commonly used for writing research papers, reports, and presentations in data analysis.
  • Provides precise control over document formatting and mathematical typesetting.
• Machine Learning
  • Involves developing algorithms that can learn from and make predictions on data.
  • Crucial for building predictive models, classification, regression, and clustering.
  • Utilizes libraries and frameworks like TensorFlow, scikit-learn, and PyTorch.
• Matlab
  • A programming and numeric computing platform used for data analysis, visualization, and algorithm development.
  • Widely used in engineering, scientific research, and applied mathematics.
  • Offers extensive toolboxes for statistical analysis, machine learning, and signal processing.
• Statistical Analysis
  • Involves collecting, organizing, analyzing, and interpreting data to make informed decisions.
  • Utilizes statistical tests, models, and software like SPSS, SAS, and R.
  • Essential for hypothesis testing, trend analysis, and deriving insights from data.
• Tableau
  • A powerful data visualization tool used for creating interactive and shareable dashboards.
  • Enables users to connect to various data sources, analyze data, and present insights visually.
  • Simplifies complex data into understandable visual representations for decision-making.

Education requirements for becoming a data analytics consultant

Most data analytics consultants pursue undergraduate study “in mathematics and statistics, or they supplement a non-quantitative background by learning the tools needed to make decisions with numbers,” according to Northeastern University.

Nearly all STEM degrees are useful for aspiring data analysts. Common options include:

• Computer Science: Provides a strong foundation in algorithms, data structures, and software development, which are crucial for data manipulation and analysis. Skills in programming and understanding of computational theory are essential for implementing complex data analytics solutions.
• Information Technology: Focuses on the use and management of technology in organizations, including database management, network security, and system administration. This knowledge is valuable for setting up and maintaining the infrastructure needed for data storage and analysis.
• Management Information Systems: Combines business and technology education, emphasizing the design and management of information systems. This degree helps consultants understand how to integrate data analytics into business processes and make data-driven decisions to improve organizational efficiency.
• Mathematics: Provides a deep understanding of statistical methods, probability, and mathematical modeling. These skills are critical for developing and applying quantitative techniques in data analysis, ensuring accurate and meaningful insights.
• Programming: Equips individuals with the ability to write and understand code, which is essential for data extraction, manipulation, and analysis. Proficiency in languages like Python, R, and SQL is necessary for implementing data analytics algorithms and automating tasks.

Hands-on experience is often more important than the classes you take, however. “In academia, data is clean, and takeaways are apparent,” says Greg Caiola, in his article for Wharton. “In the real world, data is messy and takeaways are opaque. The real skill is learning how to thrive in that complicated environment.”

Caiola recommends finding a problem that you care about and solving it with data–he uses the example of fantasy football drafts. Internships can also be quite helpful in establishing a career in data. To find one, use resources like:

• Career services at your school
• Job search platforms like Glassdoor, LinkedIn, and Indeed
• The career section of websites for companies you want to work for
• Personal relations, such as professors, friends, and even family

You might even approach a local business to set up a partnership. There are many ways to gain experience with data analytics. Most of them happen outside of the classroom.

Certifications needed for a career in data analytics consulting

Though certainly not necessary for working in the field, a data analytics certification or two might help advance your career. Some of the best certifications for data analysts are:

• Certified Analytics Professional (CAP)__: This well-regarded certification tells employers you have advanced knowledge of the principles of data analytics. The CAP needs to be renewed every three years through continuing education.
• Cloudera Certified Associate (CCA) Data Analyst: This 120-minute exam tests the skills needed to prepare, structure, and analyze data. It is designed for people familiar with SQL.
• Microsoft Certified Solutions Expert (MCSE): Data Management and Analytics: According to the Microsoft website, earning this certification allows you to “demonstrate your broad skill sets in SQL administration, building enterprise-scale data solutions, and leveraging business intelligence data–both on-premises and in cloud environments.”

You might also opt to earn a certificate from a college or university. The Harvard University Extension program offers a Data Analytics Certificate to applicants who have a basic understanding of R. The program takes a year-and-a-half to complete and costs around $11,000.

Too expensive? The Massachusetts Institute of Technology open courseware program is free. The program is geared toward motivated self-starters (two words that often appear on a job description) who want to start learning data analytics right now.

When it comes time to look for work, demonstrated experience will most impress employers; certifications are one way to show you’ve put in your hours and have the necessary chops. With proven work experience and an impressive track record, many employers will want to hire you, with or without certifications).

Should data analyst consultants go to graduate school?

Again, the answer to this question depends on what you want out of your career as well as other personal and professional circumstances. Spending between a year and five years in a master’s program (depending on the school and whether you are full- or part-time) and potentially more than $100,000 might not make sense, especially if you have a family and full-time job. Even with flexible online programs, you still need to put in the work, which can be daunting.

Still, if you are looking to move your career into upper-level management, a graduate degree could be helpful. In some cases, your employer might be willing to help cover part (or even all) of the cost of a graduate degree.

If you do decide to earn a master’s degree, you will need to choose between a Master of Science (MS) and a Master of Business Administration (MBA). The main difference between these programs is an MS digs deeper into programming, software applications, and advanced mathematics, while an MBA is a broader, more management-focused program. An MS generally takes less time to complete.

The degree you choose depends entirely on your situation, but some examples of analytics degrees from esteemed institutions include:

• Master of Science in Applied Analytics from Columbia University
• Master of Science in Data Analytics from Fordham University
• Master of Professional Studies in Analytics from Northeastern University
• Data Analytics MBA through the Wharton school at the University of Pennsylvania

There is quite a bit of mobility in the data industry, especially for those who possess a graduate degree. Some other sectors that those with an MBA or MS in data are qualified to work in include:

• Business development
• Data science
• Entrepreneurship
• Information technology
• Operations
• Program and project management

(Updated on June 12, 2024)

Questions or feedback? Email editor@noodle.com