Tools and Techniques of Analytics

Definition and Description of Analytics

Across the globe, there is growing interest in data, and companies are looking for more effective methods and strategies to exploit this data. Today, the term analytics is widely used. According to Rose (2016), the term coincides with the introduction of Google Analytics, which was done on 14th November 2015. In research conducted by Google, there was an increase in the number of people searching for analytics by 500 percent after the introduction of Google Analytics. The growth can also be attributed to the rise in the number of articles and books with the title analytics — also, the increase in the number of marketing programs conducted by leading organizations such as IBM. The drastic growth also led to a proliferation in how the term analytics is used with the introduction of phrases such as health analytics and text analytics.

The term analytics can be used in three different ways, hence three different definitions. First, this term can be used as a synonym for metrics or analytics (Rose, 2016). For example, an e-commerce platform can have website analytics to check the number of visitors or clicks on the website. In the second definition, the term can be considered as a synonym for data science (Rose, 2016). This definition can be used in the healthcare industry to share vital information in real-time. The alerts are based on the analysis of medical data, which helps the workforce in making predictive decisions. For instance, analytics can be used to send reminders when a patient should have a lab test. Analytics can also be used generally to take quantitative approaches when making decisions in an organization (Rose, 2016). Organizations can use analytics to hire talents, come up with pricing strategies, and to manage their inventory to improve profits and efficiency.

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Tools and Techniques of Analytics

With the increasing importance and demand for data analytics around the globe, there has been an increase in the number of opportunities globally. It has become difficult to single, which is the best data analytics tool since open-source platforms are providing higher performance, are more user-friendly and more popular than paid platforms.

Software analytics tools

R Programming

R is one of the leading analytics tools used around the world for data modeling and statistics. R can be used to manipulate and present data in different ways (“TOP 10 DATA ANALYTICS TOOLS,” n.d.). It can be installed on machines that run on macOS, Windows, and Unix. User can easily install the different packages which come with the software depending on the requirements.

MS Excel

One of the most popular and basic tools in data analytics is Microsoft Excel. MS Excel is used in almost every industry. For individuals who are experts in other tools such as R, they still need to utilize this tool (“TOP 10 DATA ANALYTICS TOOLS”, n.d.). Organizations use Excel to analyze a client’s internal data. With Excel, users can complex tasks which are summarized in pivot tables depending on the requirements. In advanced cases, users can utilize Excel for business analytics through time grouping, creating DAX measures, and detecting automatic relationships.


SAS is another analytics tool which is a programming language and environment developed by SAS institute (“TOP 10 DATA ANALYTICS TOOLS”, n.d.). SAS can analyze data from various sources and is easily manageable and accessible. The application can be utilized in understanding customer intelligence and has different modules for marketing, social media, and web analytics. Organizations can use SAS to profile consumers, create prospects, predict customer behavior, manage and optimize their communications.


SQL is an acronym for Structured Query Language, which is pronounced “ess-que-el.” SQL is a programming language used to communicate with a database. SQL statements are used to perform different tasks such as adding, updating, retrieving, and deleting records (“SQLCourse – Lesson 1: What is SQL?”, n.d.). Organizations can use SQL for descriptive analytics. SQL statements can be used to measure the central tendency (mode, median, mean) to measure the variability of a data set.


SPSS is a statistical software developed by IBM, and it is used by organizations to solve business problems to make quality decisions (“IBM SPSS Statistics,” n.d.). SPSS provides visualization and statistical procedures to provide user-friendly, robust, and integrated system to help organizations understand their data to solve complex research and business problems.


NLP is an acronym for Natural Language Processing, and it is a branch of artificial intelligence. This branch deals with how humans and computers interact using natural language (Garbade, 2018). The objective of this statistical method is to read, decipher, understand, and convert human languages into valuable forms.

Statistical Analytical Tools

Descriptive Statistics

Descriptive statistics is considered as the simplest form of analytics where big data is reduced to small and meaningful information. For human beings, it is problematic to interpret and understand unprocessed data, but once it is converted to information, it is easier to comprehend (Mujawar & Joshi, 2015). Basically, descriptive analytics is used to illustrate a set of data. A good example is the interpretation of census data. Census data is big and cannot be easily understood by human beings, but after conversion data, legislators can decide on how to allocate resources equitably.

Hypothesis testing

In analytics, hypothesis testing involves testing a postulation concerning a population parameter. Analysts can utilize different procedures depending on the type of data and requirements. In hypothesis testing, the analyst utilizes sample data to test if a hypothesis is plausible. Next, they test the hypothesis by examining or measuring the sample data. Two different hypotheses can be tested in a random data sample: the alternative hypothesis and the null hypothesis. The null hypothesis suggests that there is no statistical significance between two sets of data, while the alternative hypothesis suggests that there is a statistical significance(Hayes, 2019).

The Chi-Square Test

The Chi-square is applied in analytics to test the relationship between categorical variables. This statistical tool has a null hypothesis, which means that in a population, there is no relationship between categorical variables (“Using Chi-Square Statistic in Research – Statistics Solutions,” 2019). The Chi-square is mainly applied in bivariate tables, also known as crosstabulations, to evaluate the Test of Independence. In crosstabulations, the distributions of two categorical variables are simultaneously presented, and their intersections are shown in the table cells. The Test of Independence checks if there is a connection between the two variables by comparing the expected and observed patterns if the values are independent.


Correlation is one of the fundamentals of data analysis, and it is a vital tool for analysts. Correlation is the relationship between two or more things. Correlations help data analysts in investigating the causes of a certain occurrence, making predictions, and defining trends (Ghosh, 2018). There are two types of data involved in correlations: bivariate data and univariate data. Univariate data has a single variable, and it easy to set up and work with. In this type of data, analysts use a central tendency to check representative data, dispersion to enquire the deviations, skewness to check the size and shape of the distribution, and kurtosis to measure concentration. Bivariate data involves studying two variables at the same time. For instance, a researcher can check the relationship between blood pressure and age.

There are four types of correlation. One is the positive correlation where a positive change in one variable causes a positive change in the other variable. In negative correlations, a positive change in one variable leads to a negative change in the other variable. Where there is no clear-cut trend between two variables, it is known as zero correlation. The final correlation is known as spurious correlation, and in this case, there is a third variable that influences the correlation.

Multiple regression

Multiple regression is mainly used in predictive analytics. Analysts use this technique to create and learning models based on the variables. From the model, they can easily predict the value of a response variable where only the dependent variables are known (Bonner, 2019). A good example is when investors want to predict how well a stock will do based on the available information. The objective of using multiple regression is to model a linear relationship between the dependent and independent variables.

One-Way and Two-Way Analysis of Variance

Analysis of Variance (ANOVA) is mainly used in cases where there are datasets. Analysts use this technique to compare three or more groups of data. There are two types of ANOVA, namely, One-Way ANOVA and Two-Way ANOVA.

In a sample, one-way ANOVA is used to compare the Variance in the group means based on one independent variable. This technique is based on hypothesis meaning that it checks data for multiple mutually exclusive theories (Mackenzie, 2018). For instance, a researcher will want to know if human beings eat more in the cold season or in the hot season. In this instance, the independent variable is the season. When using ANOVA, the independent variable is categorized into groups. The researcher can check the weight of individuals in four different months, hence four groups.

The two-way ANOVA is also a test based on hypothesis, but in this case, the sample is defined in two ways. The results are then put into two categorical groups (Mackenzie, 2018). Using the previous example, the researcher would rephrase the question to consider the season and gender.

There are several differences between one-way and two-way ANOVA. The one-way ANOVA is mainly used to conduct equality testing between three or more means in a sample. On the other hand, the two-way is used to check the interrelationship between a dependent variable and two independent variables. Secondly, while there is one independent variable or factor in one-way ANOVA, there are two independent variables in two-way. Two-way ANOVA is used to compare many groups of two factors while one-way the independent variable or factor must have three or more categorical groups. The one-way ANOVA technique satisfies two principles of design of experiments, which include randomization and replication while two-way meets the three requirements, which include local control, randomization, and replication.

Data visualization or representation techniques

Visuals are important when it comes to interpreting data, and data visualization play a major role in interpretation. Most of the information consumed by individuals comes in the form of visuals, such as TV commercials. Data visualization is the use of elements such as maps, graphs, and charts to represent data and information. For decision-makers, these techniques help in displaying complex data in a visual layout.

The most common forms of data visualization are graphs and charts. Although the two may sound the same, there is a difference. Analysts use charts to present information in the form of tables, diagrams, and graphs, while graphs are used to show the mathematical relationship between two sets of data (Blaettler, 2018). Simply put, not all charts are graphs, but all the graphs are charts.

There are different types of graphs. One is a bar graph, and it is used to compare discrete sets of data. This type of graph is mainly used in cases where one set of data influences another set of data (Blaettler, 2018). The other type is known as a line graph, and it used to show the changes in a group. For example, a line graph can be used to show how the temperature has changed over time. The third type of graph is known as a pie chart, and it is used to depict parts of a whole. For instance, it can be used to show relationships in a closed population.

There are other types of graphs, but pie charts, line and bar graphs are the most common.

Use of analytics in healthcare

Healthcare around the world is undergoing tremendous transformation. The transformation is mainly due to the available opportunities and out of necessity. The need for change is due to its inefficiencies and unsustainability compared to the past. The opportunities coms from the continuous advancement of technology and how it is affecting the healthcare industry. With advanced technology, medical personnel is using an intelligent system and devices in making decisions in the management and administration of healthcare.

In healthcare quality improvement

Before engaging in a discussion regarding healthcare analytics, it is important to understand the concept of healthcare quality. In healthcare, analytics is mainly used to improve the effectiveness, efficiency, and safety of healthcare delivery (Strome & Liefer, 2013). Some of the emerging challenges facing the healthcare industry include overworked employees, long waiting lists for patients, and dissatisfied patients. The application of analytics in healthcare seeks to eliminate or reduce these challenges. That way, the quality of healthcare is improved.

Healthcare companies are using technology-based solutions to treat their patients and manage their operations. Consequently, these organizations are collecting large volumes of data, which can only be valuable if it is utilized (Strome & Liefer, 2013). Most organizations in the healthcare industry are becoming more data-centered; in that, they are using the available data to make better decisions. Some of the decisions made include how many health practitioners to employ depending on patient waiting time.

The application of analytics compared to the previously used dashboards and reports is that it uses statistical, mathematical, and graphical tools to help in understanding quality issues in an organization and assist in identifying possible solutions (Strome & Liefer, 2013). The application of analytics helps organizations in understanding why certain issues are occurring and create a relationship between issues. In advanced analytics tools, analysts can get predictions with the right data and models. Organizations which use data mining and regression methods, they can highlight the relationship between different factor that may be affecting their performance.

One of the goals of using analytics in healthcare is to improve efficiency. The utilization of analytics helps in improving workflow and processes in healthcare. To guarantee safe, affordable, effective, and efficient patient care, it must start with ensuring that there are minimal or no barriers to quality and ensuring that waste is reduced or eliminated (Strome & Liefer, 2013). Consequently, it is essential for quality assurance teams in healthcare to identify which areas require improvement and how it should be done. Quality assurance teams use analytics to get the detailed information regarding workflow and processes in the provision and management of healthcare.

In healthcare performance improvement

Healthcare companies around the world are looking to improve their performance and quality of service delivery. Improving healthcare performance requires continuous and combined efforts of all stakeholders who include the patients and their families, the management, suppliers, researchers, and planners to ensure better personal development, better system performance, and patient satisfaction (Khalifa & Zabani, 2016). The performance of a healthcare organization can be measured using different measurable attributes such as equity, timeliness, accessibility, availability, efficiency, effectiveness, and safety.

One of the most common issues in healthcare facilities is the crowding of patients in emergency rooms, which leads to complications and serious consequences. Analytics can be used to solve such issues by creating strategies and solutions which help in decreasing the number of patients in the emergency room, hence improving performance (Khalifa & Zabani, 2016). Crowding of patients in the emergency room is mainly due to understaffing, inefficient staff members, and a small number of treatment areas. Analytics can be used to determine the number of staff who should be in the emergency room at a given time. Also, organizations can use analytics to determine how many treatment areas should be used for emergencies at a given time.

In healthcare clinical decision making

To make better clinical decisions, analytics helps in linking the available information about individual patients or populations to an existing database of health knowledge to reduce errors and adverse events and optimize care (“Improving Clinical Decision Support with Data Analytics,” n.d.). For example, when a healthcare provider is issuing drugs to a patient, it checks if the patient is using other drugs that can interact and affect the patient negatively. Also, it can alert the caregiver if the patient has an allergy to certain drugs. Advanced systems have additional features that use the available data sets to generate new trends, which can help healthcare facilities in creating better treatment plans and help them when working on difficult cases.

Around the world, healthcare facilities are under pressure to reduce their costs. Due to this pressure, organizations are investing heavily in an effective analytics infrastructure (“Improving Clinical Decision Support with Data Analytics,” n.d.). The powerful tools acquired help organizations in determining vital information such as the total cost of care, the level of care provided to patients in and out of the facility, and which procedures were conducted. From this data, organizations can determine which procedures are not effective and which ones they can take risks on. For example, a facility can be good at total knee replacements while their services for brain surgery are more expensive than the regional and national average. In such a case, the organization may decide to refer a brain surgery patient to another hospital to reduce the patient’s expenses.

In healthcare administrative decision making

Around the world, there is constant pressure for healthcare organizations to reduce their costs and improve productivity. Some of the solutions adopted by healthcare facilities such as HIE, HER, and EMR are collecting tons of numbers and data. Organizations can use various analytics tools to assist them in making decisions.

The application of analytics has changed how businesses approach decision making. In the past, decisions were mainly based on gut feelings or institutions (“How Data Analytics can help in Decision Making in Healthcare,” n.d.). Such decisions are not good since they are not based on reason or the available information. Nonetheless, the growth of data analytics tools and technologies has helped organizations to store, analyze, and transform large amounts of data with ease. From the analyzed data, the management can make informed decisions that are based on facts.

It is believed that companies which use data analytics to base their decision give them a competitive edge. Today, organizations believe that making quality decisions depends on the quality of information available (“How Data Analytics can help in Decision Making in Healthcare,” n.d.). Hence, most medical facilities that base their decisions on data analytics provide better services, efficiently and safely. For private hospitals, it can give them a competitive edge compared to other hospitals, hence higher revenues. For example, one hospital can use data analytics to hire more staff to reduce congestion. This hospital will hire the right number and not more or less than what is required. On the other hand, a hospital that hires based on intuition or gut feelings can end up overstaffed or understaffed. This mistake can lead to increased expenses and lower customer satisfaction.

In healthcare fraud detection

There is an increased number of fraud cases in the healthcare industry, and it is a burden to society. Consequently, healthcare fraud detection has become increasingly essential. Fraud cases in healthcare are not obvious, making them difficult to detect (“Healthcare Fraud Detection – Analytic and Data Mining Techniques,” n.d.). Some examples of fraud cases include billing for services not provided, lab technicians conducting unnecessary tests which are expensive, multiple billing when selling drugs or providing services, and healthcare facilities charging more than other organizations. These costs affect the patient, the hospital, and other entities such as insurance companies.

However, organizations can use analytics to detect fraud cases. This detection involves using detective investigation and account auditing (“Healthcare Fraud Detection – Analytic and Data Mining Techniques,” n.d.). After analyzing data, an organization can identify suspicious cardholders. Companies can also use this method to monitor their providers. For example, there is a sudden increase in the number of patient visits to the neural department. Sudden changes can be red flags.

In public health

Most cities use preventive policies such as ensuring clean water supplies and providing vaccines to stop a crisis before it starts. It can be difficult for public health officers to predict when the next crisis will occur. However, with analytics, they can predict public health challenges and stop them before they start (Bhatt et al., 2014). By using predictive analytics, public health officers can predict people who are more likely to get a chronic disease, have birth complications, and determine when an outbreak is more likely to occur. Based on this information, they can take the right steps, such as checking which food establishments are at risk of violating laws and providing prenatal treatment to reduce birth complications.

In the pharmaceutical industry and discoveries

Most pharmaceutical companies have always depended on empirical data to understand the efficiency of medicine, test theories, and come up with patterns.

Pharmaceutical companies are using analytics to accelerate drug discovery and development. The application of analytics helps organizations in searching large sets of patterns, clinical trials, and publications hence helping in fast drug discovery by helping researchers to analyze previous test results (“6 Ways Pharmaceutical Companies are Using Data Analytics to Drive Innovation & Value”, 2015). Based on such analysis, researchers get to understand which methods or techniques yield the best results when developing a drug.

Companies in the pharmaceutical industry can use data analytics to improve the efficiency of clinical trials. Running a clinical trial is expensive and consumes a lot of time. Hence, it is important for a company to select the correct mix of patients in each trial (“6 Ways Pharmaceutical Companies are Using Data Analytics to Drive Innovation & Value”, 2015). Companies can use analytics to select the best group of patients to participate in a trial based on their history and demographics. That way, companies can get the best group and reduce their costs of running trials.

In human genomic data analysis and personalized medicine

For many years, scientists have studied and mapped the human genome, and one of the most successful cases was the Human Genome Project, which was done in the early 2000s. Nonetheless, to clearly understand how the human genome works, scientists required more resources and studies.

With the advancement of technology, scientists have utilized analytics to study the human genome closely. Healthcare organizations can use genomic information from patients to create personalized strategies for making therapeutic and diagnostic decisions. Through analytics, researchers can uncover unknown correlations, hidden patterns, and get additional information by analyzing large data sets. That way, scientists can easily figure out how complex genetic effects lead to complex diseases and how they can be mitigated.

Role of data quality in healthcare analytics

The demand for reliable and accurate data has increased in the healthcare industry. The data is created at the source by pharmacies, nursing groups, hospitals, and physician groups as they conduct their businesses (Dooling, n.d.). The data is then shared after conversion to secondary data with other entities such as governments, insurance companies, consumers, and public health agencies. These entities utilize the data differently, which includes making financial, administrative, and clinical decisions. Consequently, it is important to ensure that the data collected and shared with these entities is accurate and reliable.

To ensure data integrity and reliability, it is important to ensure that at the source, the data collected is complete and accurate. It can be achieved through the authentication of all users, correcting double entries, especially when billing, information governance through policies, and ensuring continuous audits (Dooling, n.d.). To maintain and achieve a high level of compliance in the healthcare industry, it is important to ensure that the staff is educated, and the establishment of procedures and policies of data entry.


The growth of technology has introduced a new term, analytics. Analytics involves discovering, interpreting, and communicating meaningful data patterns to improve a company’s efficiency, safety, and revenue. There is a wide range of software analytical tools that can be used in data analytics: R, MS Excel, SAS, SQL, SPSS, and NLP. These tools are either open-source or proprietary. Additionally, there are various statistical analytical tools that can be utilized in data analytics. These tools include descriptive statistics, probability variation, hypothesis testing, the Chi-Square test, correlations, multiple regression, and the analysis of Variance. These tools help in analyzing the data and generating meaningful reports which can be used in decision making. After analysis, analysts can use data visualization tools such as graphs and charts to display their results and deliver a message.

Analytics can be applied in various ways in the healthcare industry. Analytics can be used to improve quality and performance in healthcare through the collection and analysis of large data sets to make vital decisions in a facility. Also, it can be used to make better clinical decisions such as improving how surgeries are conducted and, in return, helps in improving healthcare. Companies in the pharmaceutical industry can use analytics to develop and improve drugs and in selecting trial groups. That way, they can save time and money.

Most of the data collected in healthcare facilities are converted to secondary data, which is shared with other entities such as governments and insurance companies. Consequently, it is important to ensure that the data is quality through policies, education, and system authentication. That way, these entities can make better decisions that can improve healthcare.


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