- Business Analytics - Home
- Business Analytics Basics
- Business Analytics - What It Is?
- Business Analytics - History and Evolution
- Business Analytics - Key Concepts and Terminologies
- Business Analytics - Types of Data
- Business Analytics - Data Collection Methods
- Different Tools used for Data Cleaning
- Business Analytics - Data Cleaning Process
- Different Sources of Data for Data Analysis
- Business Analytics - Data Cleaning
- Business Analytics - Data Quality
- Descriptive Analytics
- Descriptive Analytics - Introduction
- How Does Descriptive Analytics Work?
- Descriptive Analytics - Challenges and Future in Data Analysis
- Descriptive Analytics Process
- Descriptive Analytics - Advantages and Disadvantages
- Descriptive Analytics - Applications
- Descriptive Analytics - Tools
- Descriptive Analytics - Data Visualization
- Descriptive Analytics - Importance of Data Visualization
- Descriptive Analytics - Data Visualization Techniques
- Descriptive Analytics - Data Visualization Tools
- Predictive Analytics
- Predictive Analytics - Introduction
- Statistical Methods & Machine Learning Techniques
- Prescriptive Analytics
- Prescriptive Analytics - Introduction
- Prescriptive Analytics - Optimization Techniques
Descriptive Analytics - Applications
Descriptive analytics is used in almost every domain of industry. Some of the key application areas of descriptive analytics are as follows −
Insurance
Analysing historical data to detect patterns and trends to assess risk and insurance premium policies. Insurance data analytics is obtaining and analysing insurance data to gain important and actionable insights. This technique allows insurance firms to make better-informed decisions. It is then analysed using advanced statistical software and insurance data analytics solutions.
Every insurer has a wealth of data. In today's competitive insurance market, utilizing your data analytical abilities is critical to success. Its primary goal is to find recurrent patterns and emerging trends in the data. This information can be used to determine pricing, assess risk, improve operational efficiency, and detect instances of fraudulent conduct in the insurance industry. Insurance companies derive insights from data using data analytics technologies and will most certainly have a competitive edge. It enables insurance companies to make informed decisions by analysing large amounts of data. It serves as a strategic tool, allowing insurance companies to operate with better intelligence and efficiency.
Financial Services
In current time, world of finance, professionals are continuously looking for more effective ways to process and interpret large amounts of data. Descriptive analytics has emerged as a key approach in this area. It is most widely used in the finance sector due to following some of the key reasons −
- Sales Trends Analysis − It assists in identifying trends in sales data, allowing finance professionals to better understand the elements that influence sales success.
- Customer Behavior Patterns − By studying customer data, businesses can acquire valuable insights into their purchasing patterns and preferences, which are essential to marketing and product development.
- Financial Performance Monitoring − Descriptive analytics provides a detailed analysis of financial performance over time, which aids in budgeting and financial planning.
Overall, it examines past transaction data to analyse customer spending patterns and identify potential areas for cost reduction or revenue generation.
Retail
Using descriptive analytics, retailers may organize their data to frame a business story. It works by combining raw data collected from different sources (POS terminals, inventory systems, OMS, ERPs, etc.) to produce useful insights into past and present performance. It includes analysing sales data from promotional campaigns to determine which offers and strategies were most effective in driving customer purchases.
Traditionally, analysts performed this manually in Excel by obtaining data from various sources, structuring it, charting it, and so on. Many of these data collection and reporting tasks can now be automated using business intelligence tools and integrations.
Simply, descriptive analytics uses data to describe "what" is going on in your organization. However, it offers nothing to answer like a "why" question until paired with other types of data analytics that reveal patterns and connections. Descriptive analytics can help retailers to learn more about their customers about buying patterns, their choices, their mindset etc; it helps to frame business strategies accordingly.
Furthermore, with descriptive analytics, experts can learn more about the elements that drive website traffic and what visitors do once they arrive at your site, allowing you to improve their user experience. Using descriptive analytics can help you gain a better knowledge of your target consumers' preferences, recognize patterns in their activity, and create content that speaks to them.
Healthcare
Healthcare analytics is a systematic application of data and statistical analysis to enhance healthcare delivery, outcomes, and management. It is a process of collecting, analysing, and interpreting health data from different sources, including electronic health records (EHRs), billing systems, insurance, medical imaging, genetic sequencing, payor records, pharmaceutical research, and others. Healthcare analytics enables firms to successfully detect patterns, trends, and correlations in their data. This method is critical for improving patient care, increasing operational efficiency, and boosting financial outcomes.
Descriptive analytics enhances patient outcomes in different ways; healthcare analytics facilitates to assessment of patient demographic information to better understand the requirements of varied groups. By studying patient admission and readmission data identify factors and patterns that contribute to better patient care and optimize resource allocation. A hospital may also apply descriptive analytics by analysing emergency department data to identify the source of wait times, serve and treat patients in a better way and open the facilities to resolve the issue. At the population level, descriptive analytics can detect patterns in disease outbreaks and compare different treatment mechanisms to help prevent the outbreak from spreading.
Descriptive analytics can improve healthcare by optimizing resource allocation and minimizing waste through analysis of equipment, supplies, and staff usage. By evaluating patterns in financial data, analysts can better understand the cost of providing services and uncover cost-cutting options. Improving patient care by aggregating and dispersing patient satisfaction survey results.
Energy
With the ever-growing market with more competitors, the coming of renewable and alternative energy, and changing consumer needs, the energy industry has to work hard to cope with these changes in the new era. Data analytics is therefore an essential tool to help the energy industry successfully transition through all the changes. There are already some data analytics applications in the energy industry, and new data-driven energy services are coming out to meet the needs of modern consumer demands.
Use of data analytics techniques to analyse and derive insights from data related to energy production, consumption, and distribution. This field has gained significance as the energy sector seeks to optimize operations, improve efficiency, and make informed decisions based on data-driven insights. Analysing historical energy consumption data to forecast future energy needs, optimize distribution, and identify potential areas for energy conservation. Overall, its results facilitate in the following ways −
- Analyse electricity usage data − Descriptive analytics works by collecting customer electricity usage data from households, such as electricity bills and meter data, and then analysing and better understanding the customer's behaviour in using electricity.
- Perform predictive − To maintain the electrical grid system and ensure that it runs efficiently at all times, we can utilize data analytics to do predictive maintenance based on data. Data analytics can constantly forecast what parts of a system or machine require maintenance or repair and when allowing fixes to be scheduled on time to avoid damage that could disrupt operations.
- Supervise network operation − Real-time data analytics enable us to see and monitor the performance of an electrical grid system, ensuring that it is operating at maximum capacity. Furthermore, if there is an issue or a malfunction in the system, it will be able to detect and resolve it instantly.
- Distributed energy resources − Distributed energy resources are small-scale power sources for the electrical system. It distributes electricity to electrical loads on the grid. DERs are small-scale power suppliers that are interconnected across the grid. To work properly, DERs must be adequately integrated into the broader grid.
- Smart Energy Platform − A digital energy management system for solar power trading. Its primary purpose is to facilitate energy trade between producers and consumers. We use data analytics apps and AI technology to estimate electricity demand and production capacity across all manufacturers. As a result, we can more accurately match buyers and sellers and analyze operational data to ensure optimal performance.
Life Sciences
Data analytics is essential for the life sciences industry, as it provides an essential understanding of data insights. It enables life science workers and researchers to harness the power of data for ongoing innovation and progress. Developing patient personas and analysing adherence data to predict the probability of non-adherence to treatment plans and design targeted interventions. Data analytics is critical for diverse therapeutics, personalized medicine, and gaining a deeper knowledge of complex healthcare and life science systems. Implementing data analytics in life sciences creates potential opportunities for improved healthcare services, more effective therapies, and better patient care.
Data analytics is essential for the life sciences industry, as it provides vital insights and data. It enables life science workers and researchers to harness the power of data for ongoing innovation and progress. Data analytics is critical for diverse therapeutics, personalized medicine, and gaining a deeper knowledge of complex healthcare and life science systems. Implementing data analytics in life sciences unleashes unlimited possibilities, leading to improved healthcare services, more effective therapies, and better patient care.
Some of the key application areas are −
- Genomics and genetics − Data analytics make it easier to identify disease-causing genes, conduct population genetics studies, and develop personalized medicine approaches.
- Drug discovery and development − Biological data analysis allows for the seamless identification of prospective therapeutic targets, safety, efficacy, and drug development procedures. It is crucial for high-throughput and virtual screening, as well as structure-based drug design.
- Clinical trials − Analysing patient data from clinical trials simplifies and streamlines the evaluation of safer and more effective drugs and therapies. It aids in patient categorization, prospective adversity identification, and trial efficiency.
- Epidemiology − Data analytics is used to examine disease patterns and trends in populations to better understand disease transmission and associated risk factors. It enables outbreak analysis, illness modelling, and public health planning.
- Imaging data analysis − Data analytics is changing medical imaging by allowing for faster and more accurate diagnosis of complex images. Image segmentation and pattern recognition enable radiologists to detect anomalies more precisely, resulting in better patient outcomes and more efficient treatment strategies.
Manufacturing
Descriptive analytics can refer to the study of time-series data collected during production to give decision-makers with a comprehensive view of performance and trends. Analysing production and quality data to identify factors that lead to production failures, improve process efficiency, and enhance product quality.
Data is collected in production from a variety of sources and processes, including production equipment sensors and monitoring systems, among others. These data insights enable process engineers to readily assess their process data and answer queries like −
- How is our production process doing?
- How frequently did this problem occur?
- What's a main problem?
- Can I track deviations from good behaviour?
- What is expected to occur next?