Master of Science in Business Analytics

The volume of data in the business world today and all the benefits organizations could gain from it have given way to two relatively new occupations—the data analyst and the data scientist. Although these roles overlap in some areas, they carry two different sets of responsibilities and functions surrounding Big Data. Data analysts—a profession in name going back decades—look through and make sense of the large amounts of structured and unstructured data available. Data scientists, meanwhile, occupy a higher-level role involving a greater deal of investigation and problem solving, often with the goal of developing or influencing products, processes, and services.

Due to the specialized skills required, the job market currently has a shortage of data analyst and data scientist professionals. In fact, because of the number of openings, future demand, and average salary, Glassdoor named data scientist the top job in the US in 2018. Examining this phenomenon even further, IBM’s “The Quant Crunch: How the Demand for Data Science Skills is Disrupting the Job Market” found that to anticipate this shift, advanced knowledge of analytics, artificial intelligence, algorithms, and machine learning keep professionals competitive, as does a solid background in math and software engineering. Beyond the technical components, communication skills, while perceived as secondary, play an integral part: data analyst and data scientist professionals frequently have to communicate their findings to management and other non-technical personnel in a clear, convincing way to influence business decisions.

What Do Data Scientists and Data Analysts Do?

Although data scientists and data analysts fulfill separate roles, the positions often work in tandem. In fact, through additional education and experience, a data analyst can be promoted to a data scientist role.

On a general level, both career paths involve a significant amount of data analysis in a business context, often utilizing the latest tools to sift through and draw insights from the unwieldy amount of information generated. At the same time, that information can be used to understand more about a company’s customer base or industry to develop new products or services or to adjust its business model for a better experience. Within this context, the data analysis professional must look past the numbers on the page and use their findings to tell a convincing story.

While professionals performed these calculations manually decades ago, the internet opened up the floodgates to the amount of data available on customers and business competitors, and its pace only continues to accelerate. Ignoring the wealth of information present can negatively impact business decisions, resulting in products or services that don’t resonate with a customer base. Long term, a business that fails to use all the data available ends up seeming out of touch and falls behind its competitors.

But, where do these findings come from? Big Data, in general, encompasses multiple sources. Structured data, as the name implies, is easily organized and sorted and comes from online-based services, products, and electronic devices. It includes data from Google Analytics about website traffic, company sales figures, GPS coordinates, and any information collected by a smartphone or fitness tracker.

Unstructured data, by contrast, is related to human input and isn’t as easily organized or categorized. This information comes from emails sent, customer reviews posted, social media activity, and images uploaded. It’s the fastest-growing segment of Big Data but, compared to its structured counterpart, isn’t as straightforward to manage. Businesses often use a system of keywords and metadata to make sense of it all.

Within this framework, data analysts and data scientists are responsible for or have a hand in:

• conducting consumer data research and analyzing its results;
• adjusting customer-based algorithm models to arrive at more specific findings;
• utilizing programming languages and software, like SQL, Tableau, and spreadsheets to comb through and draw out actionable insights and apply algorithms to large swaths of data;
• regularly conducting quantitative analysis to shape company processes and decisions;
• assisting with company financial reporting, including hitting key performance indicators (KPIs); and
• generating and maintaining analysis dashboards and creating other data visualization media to illustrate metrics, findings, and goals.

Data Analyst vs. Data Scientist

From this general foundation, the two career paths diverge. Data analysts, as the job title implies, spend a significant amount of time examining complex datasets and databases. In the process, they look for patterns and trends to compile data points and create charts and other visualization methods to illustrate what their findings indicate. It’s beneficial for such professionals to love numbers and have a background in statistics, and in today’s workplace, programming skills are of growing importance. As well, because practically all fields need data analysts, professionals ought to be familiar with industry-specific data points and trends.

A data scientist often builds upon this foundation. The patterns and trends curated by the data analyst influence the data scientist’s predictions or contribute to a solution, both of which help build future strategies. Outside of these broader responsibilities, a data scientist often seeks ways to improve data curation and analysis. As such, these professionals spend time creating processes for data modeling and develop unique algorithms for custom, fine-tuned analysis. Because their job responsibilities run in a technical direction, having a background in computer science benefits many interested in this role.

Aside from these points:

• Data analyst positions may involve a greater degree of customer involvement, particularly for tracking what customers want or look for in a business. A data scientist, meanwhile, looks at these trends and organizes data to offer a solution or provide input toward new products and services meant to retain and keep customers interested.
• Data scientists tend to spend more time creating data visualizations, often beginning with dashboards and compiling data into presentations. Data analysts may need to do this from time to time, but these roles are more research and math heavy.
• In the process of doing research and compiling data, a data analyst often examines one or a handful of sources—for instance, all data related to company sales. Data scientists’ work, on the other hand, encompasses multiple, disparate sources and requires synthesis of this information.
• Between both roles, many data scientist positions center around machine learning and involve predictive modeling and building statistical models with this technology. Although data analysts also need to understand how machine learning works, professionals spend more time using SQL and spreadsheets.

Data Analyst Career Path

The need for data analysts goes back to the introduction of Microsoft Excel in the 1980s. Prior to this point, humans had to calculate and compile all research by hand, an action that could be mired in seemingly minor but statistically visible errors. Technology carved out this role’s modern-day trajectory somewhat, although responsibilities have generally stayed the same. To understand and implement tools for data gathering, management, compilation, and analysis, these professionals often need:

• a background in mathematics and statistics;
• to understand data mining and data warehouse technologies, data modeling, statistical analysis, database management, and data reporting; and
• experience using R, SAS, SQL, and other software and programming languages.

On a day-to-day basis, a data analyst professional will:

• write SQL queries and apply statistical analysis principles to find insights or answer business questions;
• mine data and analyze its findings to identify patterns or look for quality issues;
• create or add new metrics reflective of current business developments;
• continue to map and compile data from one or more sources, often for reporting purposes;
• work with engineering professionals to compile or gain access to more data; and
• be involved in reporting, often with the intent of supporting or improving business decisions.

Data Scientist Career Path

While interpreting data is key, data scientists have more responsibilities that often entail coding and mathematical modeling. Professionals often have advanced degrees or certifications that support their expertise in programming languages and machine learning.

Unlike data analyst positions, demand for data scientists is relatively new and correlates with the amount, variety, and detail of data the internet ushered in, and much of the surge has occurred over the past 10 years. Coinciding with this shift are the number and quality of technologies available to analyze this data. Businesses know that this information now composes the core of decision making, but the analysis and visualization involved require a specialized skill set.

Today’s professionals get to this point through a solid foundation in math, statistics, and computer science and some experience in the business world. On the job, data scientists:

• spend time cleaning and de-cluttering data through multiple means;
• look for patterns while building models and algorithms for better organization and examining the data;
• develop visualizations that influence product development and other business efforts;
• work with data analysts and engineers to gather these insights and routinely communicate their findings to management;
• act as thought leaders within their company and industry by communicating the value of data and the latest analysis tools;
• are always in the process of questioning or investigating something to improve its design, experience, or value and generate more revenue;
• never accept the status quo and routinely seek to create better analysis models;
• know how to glean insights from seemingly dissimilar data and conduct A/B testing to support their findings; and
• know how to tell a story based on the data they gather.

Key Skills for Data Analyst and Data Scientist Jobs

Both careers require specialized skill sets:

• ideally a science, technology, engineering, or math (STEM) undergraduate degree, although more companies are now requiring advanced degrees;
• a background involving mathematical modeling, database management, programming, and predictive analysis;
• experience using SQL/CQL, R, and Python;
• an understanding of data mining, machine learning, and the latest data analysis technologies; and
• advanced Excel skills.

As well, soft skills can’t be overstated, and it’s recommended that candidates hone their written and verbal communication, creativity, problem-solving, and team-work abilities.

Added to these standard requirements for data analysts, data scientists should ideally:

• have an undergraduate degree or experience in computer science;
• understand linear model and regression, boosting, trees, text mining, and social network analysis;
• know standard machine learning techniques, like decision tree learning, clustering, and artificial neural networks;
• know how to create data architectures, manipulate data sets, and develop statistical models;
• have a solid foundation in statistical concepts;
• be well-versed in tools like Google Analytics, AdWords, Coremetrics, and Facebook Insights, as well as Hadoop, Hive, and Map/Reduce; and
• have experience and a portfolio of presentations created through Periscope, Google Dashboards, ggplot, Business Objects, and similar tools.

Industries Needing Data Analyst and Data Scientist Professionals

As perhaps the most prominent example, retail—and specifically e-commerce—utilizes data science through multiple operations. This action may be as mundane as displaying other recommended items when you’re on a product page or after you’ve purchased something or sending emails that highlight items you’ve already visited or may be interested in. As well, these algorithms often populate the home page with listings of products related to past searches, increasing the odds you’ll click on something, explore the site, and purchase an item. In the case of sites where users regularly purchase the same product, an algorithm may send out a reminder at an estimated interval based on past habits for re-purchasing or stocking up, or you’ll be flagged whenever that particular item goes on sale. Based on research from McKinsey Global Institute, harnessing Big Data has helped retailers increase their profit margins by as much as 60 percent.

Nevertheless, retail represents one of several paths data analysts and data scientists could find themselves on:

• In medicine, Big Data is changing the way diagnoses are made and how accurate they are; has assisted in research for finding a cure; and is used to identify hot spots to halt the spread of a virus. The 2014 Ebola outbreak in West Africa is one instance where Big Data helped scientists track the disease’s spread and identify vulnerable populations.
• For farming and the food industry, Big Data has further revolutionized how food is grown, stored, and delivered, helping reduce the amount of food wasted and streamlining supply chain operations in the process.
• Business strategies further hinge on Big Data, particularly where it comes to improving efficiency, developing company processes, tracking and managing inventory, reducing the number of production errors, and cultivating customer loyalty.
• The finance industry, as an offshoot, further utilizes Big Data to oversee and manage accounts, predict trends, and keep track of transactions.
• Government also makes use of Big Data to identify trends that then form decisions and influence policies and to track the satisfaction of recent changes.
• In media, marketing, and telecommunications, social media and the data extracted from it have changed online advertising, created a channel for identifying customer satisfaction, and added more variables for updating website or service experiences; as well, the amount of data available allows these services to easily identify errors and revise products according to customer requests.

Types of Data Analyst and Data Scientist Careers

Depending upon their industry and responsibilities, data analysts and scientists hold a spectrum of job titles:

• Data Analyst: Professionals analyze, manipulate, and transform large datasets to gain insights and make business predictions.
• Statistician: A more mathematical role, statisticians compile, interpret, and analyze statistical data and formulas, often with the intent to shape business decisions or contribute to research.
• Data Engineer: Utilizing a range of programming skills and analysis tools, data engineers often clean the available data for processing, gathering, and storing information.
• Quantitative Analyst: Closely involved in financial analysis and risk prediction, quantitative analysts use statistical methodology and machine learning to answer business questions, solve financial problems, and predict markets or industry trends.
• Business Analyst: Outside of finance, business analysts examine company data across multiple departments to make recommendations and predictions and impact company strategy.
• Systems Analyst: Professionals are responsible for identifying organizational problems and conducting the research to find a solution, utilizing programming, statistical techniques, and data analysis to examine information and draw conclusions.
• Marketing Analyst: Especially where campaigns and digital products are concerned, marketing analysts routinely examine and look for trends in sales and marketing data, using a mix of modern tools like Google Analytics and traditional statistical techniques.
• Operations Analyst: Focusing on improving internal processes, operations analysts seek to streamline how a company runs, examining and visualizing data regarding systems, products, and services and pinpointing areas of improvement.
• Data Mining Specialist: Utilizing a mix of research, data analysis, and modeling techniques, professionals assess data to identify patterns and relationships to predict future industry trends and influence a company’s direction.
• Data Scientist: Often focusing on unstructured data, data scientists analyze the wealth and variety of information a company has available, creating their own formulas or conducting investigations to find unique, actionable insights that then affect key business decisions.
• Business Intelligence (BI) Developer: Professionals often have a top-level role in which they create company strategies and conduct the research to support their findings and orchestrate its future structure.
• Data Architect: Data architects ensure that the systems built for analyzing and examining information are operating and performing optimally and delivering the results other data analysis professionals need to draw effective conclusions.
• Machine Learning Specialist: Because algorithms can always be refined, machine learning specialists continue to find ways to improve existing technologies, particularly in relation to the rate of Big Data’s growth.

Start Your Data Analyst or Data Scientist Career

If you’ve been in an analytical or programming role and are seeking to take the next step, get on the path to a career as a data analyst or data scientist with the Wake Forest University School of Business’s on-ground Master of Science in Business Analytics degree. To learn more about this program, request additional information today.