Data Analytics Skill Sets to Have in Today’s Data Decision Support Environment

Healthcare organizations are facing tremendous obstacles in the current healthcare climate, with staff shortages, burnout of medical professionals, and hospital admissions at an all-time high. All of these factors, alongside rising healthcare costs, are requiring many organizations to make numerous difficult decisions.

In order for senior leadership to make informed decisions, critical data must be obtained, analyzed, and displayed in a format that is accurate and trustworthy. How does senior leadership get the data necessary to make decisions, and who is the responsible party? A data analyst in health informatics and information management can fulfill this role.

According to an article from Columbia Engineering, “It’s no hyperbole to say that modern society runs on data. Humanity generates an incredible two and a half quintillion bytes of data daily with no signs of slowing down.” But what is data analytics, and why is it important to healthcare?

Masters in Data Science defines data analytics as the “process of analyzing raw data to find trends and answer questions, and the definition of data analytics captures its broad scope of the field.” There are four primary types of data analytics: descriptive, diagnostic, predictive, and prescriptive. Each type has a different goal and a different place in the data analysis process. This article will detail the skill set needed to work in data analytics, as well as how selected data analytics impact healthcare organizations and procedures to ensure data integrity.

General Skills

Working with data analytics requires some general skill sets. Basic and advanced mathematics and computer science concepts are foundational. Also important, if difficult to teach, are non-technical soft skills, also called interpersonal skills—communications, problem-solving, critical thinking, teamwork, and project management. These are abilities that relate to how you work and interact with other people, and they help someone working with data analytics to use their technical skills and knowledge effectively and efficiently.

Data analysts do not simply deal with numbers, design programs, or conduct analyses. More often, this work requires critical thinking beyond raw data, such as understanding the types of data in the electronic health record, identifying patterns and trends, and interpreting and communicating the results to others. Teamwork is an important component to working in the healthcare industry in the current complex environment. These tasks are not usually completed by individuals but almost always by teams. An effective team player will contribute their own technical skill sets while collaborating with others to achieve the organization’s goals

Data Integrity Skills

Because so much data is now available in healthcare, it has become increasingly important to ensure the integrity of the data. The Harvard Business School describes data integrity as the “accuracy, completeness, and quality of data as it’s maintained over time and across formats.” AHIMA defines data quality and integrity as “the extent to which healthcare data are complete, accurate, consistent, and timely throughout its lifecycle including collection, application (including aggregation), warehousing and analysis.”

While data integrity is related to the privacy and security of the data, it’s not the same. Organizations must protect data from external and internal threats to ensure that it is kept private and confidential, while ensuring that the quality and integrity of the data are intact. Some of the common threats to data integrity include: human error, such as deleting a row of data in a spreadsheet by accident; inconsistencies across format, such as having inaccurate references in a spreadsheet cell; collection error, such as inaccurate data collection or data lacking information; and cybersecurity or internal privacy breaches, such as information breaches due to hacking by internal or external individuals. Data integrity is essential for several reasons: 1) to ensure that data is recoverable, searchable, and traceable; and 2) to protect the validity and accuracy of the data in order to increase its ability to be reusable and maintainable.

Database-Related Skills

Databases, relational or non-relational, serve the function of the collection of organized information for better and easier accessing, managing, and updating the data. A relational database is a collection of tables, and some commonly used relational databases include Microsoft SQL Server, Oracle Database, MySQL, and IBM DB2. Non-relational databases, such as NoSQL and JSON, do not use tables as the storage format and can quickly process all types of data, including unstructured data such as free text in the patient medical record. Patient clinical, demographic, and financial information can be collected, stored, and maintained in a database in a healthcare organization. These databases are very important to healthcare organizations and provide valuable information for various purposes (e.g., communicating information related to patient care, providing decision-making support, serving administrative planning, assessing quality improvement, facilitating financial management, and conducting research activities). The skill sets in database-related concepts and their applications can best benefit good database design and management with easily retrievable information, system modification, and data maintenance.

Data Analytics Skills

According to several studies performed by Business Higher Education Forum, two of the most desired skills for data analysts in 2022 are SQL and Microsoft Excel. The significance of SQL (structured query language) is paramount, as it is the beginning point of data analysis. Senior leaders within organizations need data analysts who can compose an SQL query to explore a data set. SQL is the most popular language for data analysis. In order to complete this technical task, one must understand the why and how of executing an SQL query. This requires parameters to be set by the data analysts that will yield raw data applicable to the management dilemma facing the decision-makers. Once the raw data has been extrapolated from the data source, the data is then exported to Microsoft Excel. Microsoft Excel is cited as the second most critical skill necessary for a successful career in data analysis, per the studies conducted by Business Higher Education forum and cited by Northeastern University. Some additional data analytics applications include R, Python, SPSS, and SAS.

Data Application/Visualization Skills

Microsoft Excel allows the user to manipulate the data, scrub non-usable or invalid responses, and place the data into a readable format with great ease. Once the data has been exported into Microsoft Excel, there are numerous statistical options available to the analyst for pivot tables, data visualization charts, graphs, and regression analysis outlining the numerical relationship between data elements. Data visualization helps to tell stories by organizing data into a form that is easier to understand and highlights trends and deviations. Visualizing the data enables decision-makers to interrelate the data to find better insights. It reduces the complexity of complicated data and enables generates faster decision-making. Google Charts, Tableau, Infogram, and Qlik Sense are tools for visualization. They all yield a detailed and easily readable graphic display that can be presented to boardrooms and senior leaders.

Transforming Data

With the abundance of data in healthcare, it is vital to consider the current data decision support environment and the skill sets needed to transform data into useful information. The skills discussed in this article are necessary to ensure the data can yield the information organizations needs to make good business decisions.

Lesley Clack (lclack@fgcu.edu) is an associate professor and department chair of health sciences at Florida Gulf Coast University.

Shannon H. Houser (shouser@uab.edu) is a professor of health services administration at the University of Alabama at Birmingham.

Michelle Martin (mmartin@latech.edu) is an associate professor of health informatics and information management at Louisiana Tech University.

Joanna Ward (jward@latech.edu) is an assistant professor of health informatics and information management at Louisiana Tech University.