A Day in the Life of a Digital Analyst

A man looking at a computer screen

Virtually all industries use data to solve the most complex challenges facing businesses today. Whereas business leaders used to rely on historical data to predict what might happen in the future, the data analysts of today translate enormous data sets into real-time intelligence. Now more than ever, marketers can truly access the mind of consumers to provide the most valuable and efficient customer experience. Industry leaders can use data to develop new products, target new markets, and solve real-world problems.

Increases in available data drive a huge demand for competent data specialists. In fact, on LinkedIn’s list of needed skills in 2019, the majority of “hard skills” pertain to the field of data science and analytics. Also, the Bureau of Labor Statistics predicts that careers for data professionals will increase by 26 percent over the next 10 years, which is much faster than the national average of 5 percent for all professions and industries.

The demand for specialized skills in data science leaves many asking—what exactly is the role of a data analyst?

What is a data analyst?

Data analysts collect, organize, and interpret various data sets. Data analysis draws meaningful conclusions—using data science—that can be used to benefit a company in a variety of approaches. Data analysis and data science supply the fuel that allow business leaders create efficiencies, increase revenues, and solve complex business problems. While specified skills and functions of data analysis vary from industry to industry, data analysts should anticipate their primary day-to-day functions including: interpreting data into essential insights and visualizations, and communicating those insights and visualizations to business executives through reports and presentations.

The roles of data analytics and data science professionals has changed over the past decade, and experts say they’ll continue to change. There is some overlap in the role of a data scientist and data analyst, but they typically serve two purposes. While a data analyst carries the primary responsibility of collecting, organizing, interpreting, and disseminating insight, data scientists take it a step further by writing code to make predictions.

Thus, the importance of data science and data analytics is evident, and therefore careers have increased enormously. The large volume of data that is created every minute drives companies to hire skilled specialists to effectively manage increasingly large data sets that are otherwise confounding to the average viewer. One of the most interesting elements of a data analytics career is getting the opportunity to tackle complicated puzzles and find missing pieces of information which truly become an asset to an enterprise. Data analytics is an important component of storytelling, because, without it, businesses resort to using outdated statistics and guesswork to send messages to consumers. Data is a company’s door to advanced intelligence, and the field of data science and analytics is the opening key.

Almost every industry utilizes data science, including data analysis and data visualization. The traditional data professional was once only found in the field of computer science, but that is rapidly changing. For example, a digital marketing analyst—who helps companies gain a deeper understanding of target audiences—can work in a variety of industries, evaluating market conditions and predicting potential sales of a product or service. This means data professionals now must possess advanced communication and problem-solving skills in order to work across multiple divisions.

Data analyst skills and qualifications

Data analysts must possess advanced knowledge and digital skills to be successful, which is why many employers require data employees to possess a master’s degree. Data analytics is unique because data professionals must be both analytical and business savvy. While the “data analyst” title is telling of its role—requiring a long list of technical, analytical, and complex problem-solving skills—data analysts must also possess strong soft skills, such as remarkable communication. A data analyst’s ability to communicate—both organizationally and interpersonally—is a key requirement in translating data into a story that can be consumed by a target audience.

Below is a list of key technical and analytical skills every data analyst should possess:

Data mining: Discovering patterns, anomalies, and correlations in large data sets to forecast and predict specific outcomes.

Data modeling: Creating a model for the storage of data in a database.

Database management:: Organizing, monitoring, archiving, and protecting data within a database, which has become increasingly important with the growth and volume of business data.

Analytical reasoning:: Making smart decisions based on collected data.

Data visualization:: Representing data translations into the form of a chart, diagram, or picture.

Software and programming tools:: The following list includes some of the most essential software and programming tools used in data analytics:

  • SAS: Integrated software suite for advanced analytics, business intelligence, data management, and predictive analytics.
  • SQL: Relational database programming language, often used in conjunction with other programming languages.
  • R: Open-source programming language used to build statistical models.
  • Python: General-purpose programming language with some of the top AI and machine learning tools.
  • Stata: Statistical analysis tool used especially in the fields of economics, sociology, political science, and biomedicine.

Below is a list of soft skills every data analyst should possess:

Creativity: Having a genuine curiosity to solve complex puzzles, therefore driving the ability to be innovative and ask the right questions.

Persuasion: Telling compelling stories about your company’s goals and building positive business relationships to achieve collaborative partnerships.

Collaboration: Working across multiple disciplines to break down barriers that block the information needed to reach programmatic goals.

Communication: Clearly translating and presenting data findings and solutions.

Time management: Prioritizing and accurately dividing time and efforts in order to meet deadlines and tackle the most pertinent tasks.

Data analyst duties and responsibilities

Using analytics and visualization, data analysts are often expected to be one step ahead of everyone else. The data analytics opportunities and nearly endless, so there are a few paths a data analyst may follow in the job market. The industry and level of analysis needed will play a part in a data analyst’s duties and responsibilities. The Bureau of Labor Statistics indicates data analysts may work in a wide variety of fields, including finance, publishing, science and technology, management, manufacturing, trade, government, and more. Also, depending on the size and goals of the company, some may need more advanced analytics, while others only need basic analytics.

Operations research analysts have the following duties:

  • Gather and organize data from a variety of sources.
  • Identify and solve business and logistics problems.
  • Work with other employees across different divisions to gather the most accurate quantitative and qualitative information to solve a problem.
  • Determine the best way to tackle a problem and create a framework that is relevant and usable
  • Develop the most practical and profitable solutions using various forms of analysis
  • Create reports, give presentations, and hold meetings with key stakeholders and decision-makers to recommend a specific course of action.

Similarly, market research analysts have the following duties:

  • Analyze marketing and sales trends using various analytical tools and methods.
  • Using predictive analytics, measure the likelihood that marketing programs and strategies will be effective.
  • Create a framework for collecting qualitative and quantitative data that can be organized and evaluated to improve marketing strategies.
  • Stay informed and aware of market conditions, competition, consumer behaviors.
  • Use statistical software and programming to analyze data
  • Using data visualization, create tables, graphs, and reports that will translate complex data into understandable content.

Educational requirements to become a data analyst

To become a successful data analyst, you must have the right education and training. A bachelor’s degree in a data science field is helpful, but not required. Many data analysts begin a career in other areas, such as business, marketing, and economics, typically holding a bachelor’s degree in a field relating to those industries. Someone with a background in a non-technology field may need to pursue additional education to supplement their knowledge and skills in data analytics.

Online education has increased the number of options for furthering your education, including bootcamps, professional certification modules, and other vocational programs. However, a growing number of companies are requiring that data professionals carry a master’s degree, even if you already possess a bachelor’s degree in a technical field.

Your education may be contingent upon the level and type of data analytics you are interested in pursuing. The four types of data analytics are descriptive, diagnostic, predictive, and prescriptive:

Descriptive analytics (What happened?)

As one of the most basic pieces of intelligence a company generates, descriptive analytics is the preliminary stage of data processing where a data analyst gathers and summarizes a set of historical data. This data is used to create an accurate account of what actions or transactions took place within a business. It also builds a platform for better understanding why this historical events occurred.

Diagnostic analytics (Why did this happen?)

A more advanced form of research and analysis, diagnostic analytics analyzes a company’s accurate historical account of its business operations and seeks to determine why certain things occurred. A data analyst will practice data mining to drill-down on the data and discover patterns and correlations. This allows a company to determine its strengths and weaknesses.

Predictive analytics (What might happen in the future?)

With the goal of either repeating or changing history, predictive analytics is a form of data analytics that anticipates the occurrence of future events. A predictive data analyst may identify the likelihood of future outcomes to determine whether a mistake will occur again. Predictive analytics can also be used to predict the success of a marketing campaign.

Prescriptive analytics (What action should we take?)

The final phase in analytics after determining what will happen and when it will happen, prescriptive analytics also determines why it might happen and what action should take place to ensure the desired outcome is achieved. Prescriptive analytics closes the loop for descriptive, diagnostic, and predictive analytics and creates decision options for business executives.

Data analytics roles that focus primarily on basic descriptive analytics may not require extensive experience or an advanced program in data analytics. However, if you wish to pursue advanced analytics, such as diagnostic, predictive, and prescriptive analytics, you may consider a master’s degree in programs that are heavily weighted in science and mathematics, such as quantitative economics or data analytics and visualization. While it’s possible for these skills to be learned with on-the-job training or an analytics bootcamp, a master’s program will provide the necessary educational foundation to continue growing and ultimately be the one who is driving business growth for companies around the world.

Become the data analyst hiring managers are seeking

The Katz School of Science and Health at Yeshiva University (YU) offers an online MS in Data Analytics and Visualization. With online courses at your literal fingertips, you’ll receive advanced analytics training and gain specialized skills that are sparse in today’s workforce.

The program offers the following exciting and challenging courses:

  • Analytics Programming
  • Visual Design and Storytelling
  • Computational Math and Statistics
  • Structured Data Management
  • Data Science
  • Data Product Design
  • Data Driven Organizations
  • AI Studio
  • Information Architectures

The Katz school delivers a world-class education and connects students with thought-leaders and employers. Reach out today to learn more about YU’s online MS in Data Analytics and Visualization program.

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