Business Intelligence vs. Data Analytics in Health Care

As organizations today handle more data, it is important for them to effectively manage and analyze the information they come in contact with. By doing this, they can generate key insights that apply when making critical business decisions. Specifically in health care, providers rely on business intelligence and data analytics to locate ways to optimize patient care and ensure the quality of care given is always high. Leveraging the power of these disciplines to analyze data can lead to many benefits for health care organizations, such as improving the efficiency of care, detecting illnesses sooner, minimizing costs for patients, and improving other critical areas of health care services and delivery.

Business intelligence and data analytics are both key disciplines organizations today use to handle their data and generate key insights to aid in making critical business decisions. While these two terms focus on achieving similar outcomes, it is important to understand their differentiators and how to utilize each to tackle challenging business cases. Specific types of differences to learn about include their purpose, scope, complexity, granularity, and more. Also, know what kinds of jobs utilize these disciplines in the health care industry today to see if you want to pursue a health care analytics career. 

What is business intelligence?

Business intelligence is the process of transforming data, generating actionable insights through analysis, and building reports and dashboards to present important takeaways. It enables a company to become data-driven, collecting and analyzing data from each sector of its business to locate opportunities and find ways to improve in the future. 

You commonly use data visualization tools to create charts and graphs that showcase trends and insights that can be included on dashboards and reports. Management can then leverage the information presented from this analysis to make important decisions about what strategies they deploy and what trends to pay attention to. The end goal of business intelligence is to create digestible, easy-to-understand reports to present to key stakeholders that showcase actionable insights and market trends.

What is data analytics?

Data analytics is a discipline that organizations leverage to gather, organize, and analyze the unstructured data they encounter to generate key insights. In data analytics, you use statistics, programming, and other technical and quantitative skills to find patterns and trends in your data that can lead to better business decisions about future situations. 

A large part of data analytics focuses on predicting future outcomes and discovering what future trends may be relevant. Effectively using data analytics in your organization allows you to stay on top of market trends and have an advantage against competitors in your industry. 

Different forms of data analytics exist within the overall discipline, each focusing on driving specific outcomes for organizations. Four of the main types of data analysis are:

• Descriptive data analytics: This form of data analytics focuses on transforming raw, unstructured data into a presentable format that stakeholders can easily digest. Descriptive analysis looks at data from the past to find key insights and provide context for broad trends in your organization.

• Diagnostic data analytics: Diagnostics goes beyond descriptive data analytics and examines why a trend or change occurred. It is a way to analyze the root cause and find the reasons behind a potential problem.

• Predictive data analytics: Predictive data analytics aims to utilize past information and data to project what may happen in the future. Machine learning algorithms and advanced statistical models are used to help predict future outcomes. These forecasting models become more powerful and accurate with large sets of available data. 

• Prescriptive data analytics: This form of analytics focuses on enhancing the decision-making process for users. Optimization analysis is one common example of prescriptive data analytics. Organizations use prescriptive analytics to help define the steps they need to take to reach a certain goal. 

How do health care professionals rely on data?

Health care professionals rely on data and use business intelligence and data analytics to extract key information, perform analysis, and generate reports and visualizations showcasing important metrics and outcomes. Compared to other industries, health care organizations have taken longer to adopt business intelligence and data analytics to enhance their work, but these disciplines are acting as a catalyst in transforming the industry for the future. 

A study released by MarketsandMarkets states that the projected growth rate for the US health care analytics market is over 25 percent each year from 2022 to 2027 [1]. Effectively utilizing data in health care enables you to improve the management of key processes and procedures and enhance your ability to make difficult decisions. The main uses of business intelligence and data analytics in health care fall into two different buckets: discovery and delivery.

Discovery benefits, also called efficacy benefits, relate to disease detection, comparative effectiveness, and information sharing through storing data in centralized databases, among other things. An example of this is the chronic disease database, which promotes an ideal learning health system (LHS) where data gathered from every patient contributes to a database. Organizations that contribute to the database benefit from leveraging its information to improve the care for their patients and allow them to promote knowledge sharing across different teams. 

Care delivery benefits include optimizing the flow of patients receiving care, using dashboards and charts to track key metrics related to the care received by a patient, and disaster planning. Dashboards and control charts in health care have applications in tracking operational metrics within an office or hospital and monitoring critical recovery metrics like remission rate. Various other ways that health care professionals rely on data include:

• Minimizing the cost of treatments

• Improving key processes, such as inventory management

• Managing the revenue cycle

• Locating preventative care opportunities

• Monitoring patient preferences

• Reducing wait times for patients

• Enabling earlier disease detection

Business intelligence vs. data analytics: Differentiators 

While it is common to hear the terms business intelligence and data analytics used interchangeably, it is important to recognize their differentiators and some of the key factors behind each discipline. 

Scope 

The scope of data analytics is broader than that of business intelligence. Data analytics incorporates finding valuable insights through a wide range of methods, such as statistics, visualizations, and other exploratory methods. Business intelligence usually analyzes an organization's performance for a specific situation to find trends and information that help stakeholders make informed choices. 

Techniques

Business intelligence techniques include using data visualization tools, reports, and dashboards to organize and present the analysis results. Within data analytics, techniques commonly used for analysis are machine learning, data mining, prediction analysis, and programming. These techniques tend to be less beginner-friendly than business intelligence tools. 

Purpose 

In general, the main purpose behind business intelligence and data analytics differ. Business intelligence aims to generate insights from information and data that aid in making business decisions within an organization. For data analytics, the main purpose is to discover key trends and patterns within data that inform predictions about future outcomes.

Time frame

Data analytics operates under a longer time frame to analyze past information and generate forecasts about future opportunities and strategies. Business intelligence focuses on a shorter timeline to provide real-time data and insights to stakeholders to help inform their decision-making. 

Types of data 

Data analytics works with complex data sets ingested from various sources that can sometimes be unstructured. The techniques and tools used in data analysis allow you to handle this type of data. Business intelligence, however, commonly performs analysis from data stored in a database from its organization. These data sets are often already organized, cleansed, and structured before business intelligence begins.  

Complexity

As you can see from the types of techniques and data relevant to each discipline, data analytics tends to be more complex, requiring a specific set of skills and knowledge of techniques, such as programming. While some training may be necessary to start with business intelligence, this discipline is more accessible and requires less specialized skills. 

Data volume

Business intelligence works with smaller, more manageable data sets compared to data analytics. However, both disciplines have ties to big data applications so that the data volume may vary depending on the specific situation. Data analytics frequently handles large quantities of data and requires data storage and scaling knowledge. 

Granularity

Business intelligence and the associated dashboards created through this type of analysis feature more high-level information derived from an organization's data. Data analytics investigates data sets further to extract unobvious insights and gain in-depth knowledge about relevant trends. 

Careers in health care using business intelligence and data analytics

Employers in health care today are actively looking for professionals with a data analysis skill set and a strong understanding of business intelligence fundamentals. A few of the top in-demand health care careers related to business intelligence and data analytics include:

Clinical data analyst

Average annual US salary: $88,330 [2]

Job demand growth: 35 percent from 2022 to 2032 [3]

Clinical data analysts focus on verifying and validating the information discovered from trials and experiments in a clinical setting. They utilize data analysis and business intelligence to build databases, manage data sets to maintain accuracy, and report the takeaways from their analysis of the data to key stakeholders to provide insights that improve the quality of health care options.

Population health data analyst

Average annual US salary: $82,517 [4]

Job demand growth: 35 percent from 2022 to 2032 [3]

As a population health data analyst, you leverage data analysis skills to analyze vast data sets to discover new business solutions for your organization in health care. You commonly collect your data or interpret studies to build your data sets, develop algorithms or predictive models, discover processes to improve health data sets and ensure the performance and accuracy of your data model are optimized to deliver valuable insights. 

Data scientist

Average annual US salary: $120,588 [5]

Job demand growth: 35 percent from 2022 to 2032 [3]

Specifically in the health care industry, data scientists utilize techniques in data mining, artificial intelligence, machine learning, and other disciplines to find valuable insights within health care data sets. The analysis you complete as a data scientist in health care aims to enhance the care patients receive, bolster self-tracking capabilities for patients, contribute to the research and treatment options of illnesses, and improve the efficiency of key processes throughout your organization overall. 

Health information management analyst

Average annual US salary: $80,647 [6]

Job demand growth: 10 percent from 2022 to 2032 [7]

As a health information management analyst, you collect and analyze diverse data sources related to health. You are responsible for gathering data to build dashboards and reports that showcase your findings to key stakeholders. Other tasks you may complete in this role include researching new data collection techniques, incorporating automation into your analysis, and serving as a resource for your organization to train other employees about data collection.

Health care business intelligence analyst

Average annual US salary: $88,359 [8]

Job demand growth: 10 percent from 2022 to 2032 [7]

Health care business intelligence analysts leverage the capabilities of business intelligence software to work with data sets and create data visualizations to showcase key insights. The information you work with in this position can relate to clinical, financial, operational, and even other types of data ingested by your organization. Your work improves patient care, locates critical trends related to the spread of diseases or demographic information, and provides valuable context to management to improve overall decision-making. 

Health care business intelligence developer

Average annual US salary: $86,473 [9]

Job demand growth: 10 percent from 2022 to 2032 [7]

Your goals in this position align with those of a health care business intelligence analyst; however, you focus on creating and maintaining critical business intelligence infrastructure used by your organization to manage data. The systems and tools you handle in a developer role include data models, various reporting services, dashboards, and graphs and charts built from your data. 

Common tools used in business intelligence and data analytics

Business intelligence and data analytics professionals utilize specific tools to complete their duties. Data analytics requires a strong knowledge of analytical techniques and data-related technical abilities. You need to know statistics, data mining, and even some programming languages in this discipline. Common tools used in data analytics include SQL, Python, and R.

Business intelligence demands a slightly different skill set that is less technical and more tailored to visualizing data and showcasing the critical information derived from analysis. In a business intelligence role, you commonly build charts, graphs, and other data visualizations to develop dashboards and other reports to present key insights to your organization. Common business intelligence tools to know are Microsoft Excel and popular business intelligence software, like Microsoft Power BI and Tableau. 

Getting started on Coursera

To learn more about business intelligence, data analytics, and the common tools and software utilized by professionals in these disciplines, completing a course or receiving a relevant Professional Certificate is a great place to start. On Coursera, you can enroll in some of the top courses in the world. 

Check out the Google Business Intelligence Professional Certificate. This certificate course series features three courses that allow you to earn a career credential demonstrating your business intelligence expertise. Through this program, you can learn in-demand skills required to land a business intelligence role today, such as data modeling, reporting, dashboarding, and visualization. 

If you are interested in pursuing a career path more related to data analytics, check out the Microsoft Power BI Data Analyst Professional Certificate. This certificate program features the ability for you to learn the most in-demand business intelligence skills to prepare you to attain a job in this field within five months. You can learn how to connect data sources, transform your data, and how to prepare Excel data for analysis.