data analytics - emerging freelancer

Data analytics has quickly emerged as an important method that helps to provide relevant information and analyze business processes. This is important for organizations due to the fact that decision-making, data analysis, and trend identification can all be accomplished through a data-driven approach. Here I will compare and describe the five most popular types of data analytics that can be applied in various sectors.

Descriptive analytics is therefore the simplest type, it involves aggregating data and providing information about events that have occurred in the past. However, diagnostics take a step further in that they not only show outcomes but also the causes and reasons that lead to them. While predictive analytics forecasting is the process of estimating probabilities of past seen events and future unseen events, prescriptive analytics is a step further and provides actionable insights on how to achieve the desired outcomes.

Third is cognitive analytics which is a new approach that uses artificial intelligence, and machine learning to perform analysis of the big data and make decisions independent of human interventions. Every kind of data analysis type has its own strengths and application so organizations can make use of various approaches that can provide them with a complete perspective of the organization.

This piece will establish the various types of data analysis and how they can be employed in business so that readers who own businesses, those interested in analytics or are analysts themselves can benefit. Well, then let’s get our feet wet and go through all the wonderful opportunities concerning data analytics!

1. Descriptive Analytics

Descriptive analytics is the simplest type of analytics, and it involves summarizing historical information to present meaningful information about past events. It assists organizations to have better insight of the events that have taken place and to understand the pattern that recurs frequently and the occurrence that is rare. Descriptive analytics typically responded to questions such as “What happened?” or “What is the state of affairs at the moment?”

This kind of analysis employs different statistics, visualization techniques, and reportings such as dashboards and others to present the data in an acceptable and comprehensible manner. This provides businesses with information about their past performance, consumers, and market dynamics. According to historical data analysis, organizations are able to realize the patterns, trends, and relations within the variables, and be able to devise proper plans for the future.

Retail industry, finance, healthcare sectors, advertising, and marketing are some of the sectors that apply descriptive analytics. For example, in the retail industry, descriptive analytics may be used to study the retail sales, discover the best-selling products, and customers’ trends. It can be applied in the healthcare industry to assess the data of patients, the diseases’ trends, and healthcare solutions. In conclusion, descriptive analytics creates a strong base for organizations as it helps to work with the data and make business decisions.

2. Diagnostic Analytics

Diagnostic analytics takes the concept of descriptive analytics a notch higher by explaining why certain things happen. That is why it seeks to ask and answer the question; why did it occur? in an endeavor to analyze various historical data and come up with the causes of a given event. Detective analytics on the other hand assists organizations in identifying the fundamental issues that have led to certain outcomes or continued to present certain results so that corrective procedures can be employed or positive outcomes emulated.

This type of analytics refers to the process of applying statistical models, data mining methods, and machine learning algorithms in order to establish relationships and patterns. It can assist organizations in explaining why specific results were achieved and can offer information on their root causes. Diagnostic analytics is most effective in identifying problems, determining where exactly processes slow down and improving them when necessary.

For instance, in the finance departments of various organizations, diagnostic analytics can be applied to determine the reasons that lead to either profitability or otherwise. In manufacturing, it can be used for analyzing the production data and determining the possible reasons for certain defects or low productivity. Diagnostic analytics helps organizations to better understand their functioning and make the necessary changes toward improvement using the data.

3. Predictive Analytics

It is a form of data analytics that uses data collected in the past to forecast events that will occur in the future. Its purpose is to predict the probability of an occurrence by using past data to assess a chance of an event or condition occurring. Predictive analytics is the application of predictive techniques such as statistical modeling, data mining and machine learning to the development of models.

It is a form of analytics that helps organizations to make the right decisions regarding the future. This enables businesses in predicting certain occurrences, enhancing marketing strategies, and boosting their performance. The application of predictive analytics is present in areas of operation like finance, insurance, e-commerce, and healthcare.

For instance, in the insurance sector, predictive analytics is useful for evaluating the risk of the policyholders that will help in the determination of premiums to be charged. In e-commerce, they can be used in order to forecast client’s preferences and offer relevant goods. Predictive analysis enables an organization to be more anticipate in its market by allowing it to act before the completion.

4. Prescriptive Analytics

Prescriptive analytics is actually even more advanced than predictive analytics since it gives the possible actionable paths towards achieving specified goals. It serves the purpose of answering the question “What should be done?” through applying optimization algorithms; simulation models; and decision analysis techniques to systematically compare possible options and provide an optimal solution. Prescriptive analytics involves the tools used in providing a set of recommendations about what should be done by an organization depending on facts available.

This form of analytics examines a number of considerations such as constraints and objectives and the likely outcomes in coming up with a set of solutions that are in line with the objectives of the firm. It assists organizations and companies to assess various eventualities and decide on the proper course of action after modeling the possible consequences. Therefore, prescriptive analytics is very essential especially when making decisions that require more than one aspect to be looked at.

For instance, in supply chain management, prescriptive analytics can employ data and models to decide the most effective approaches for maintaining inventory, and determining production and distribution plans for better supply delivery to consumers at lesser cost. In healthcare, it can be used for identifying the best approaches in treatment provided the available data on the patient as well as medical data on the ailment. This involves creating a reference model that provides recommendations for decision making and achieving organizational goals.

5. Cognitive Analytics

There is another field called cognitive analytics where use artificial intelligence and machine learning techniques to make decision using unstructured data. Cognitive analytics are different from other types of analytics as they are aimed at analyzing natural language as well as other items of unstructured information such as images.

Cognitive analytics incorporates techniques like natural language processing, image recognition and sentiment analysis to derive insights from disparate sources of information. It also allows companies to obtain information from social media feeds, customer feedback, and articles on the internet. Cognitive analytics can be applied to map customers’ feelings, find tendencies, and perform decision-making operations.

For instance, in the financial sector, cognitive analytics can be applied to articles and social media accounts to identify trends in the market and, therefore, make correct investment decisions. In sales, it can be used in analyzing customers’ feedback and sentiments for the improvement of service provision. Cognitive analytics brings new opportunities for the implementation of new decision-making and gaining insights from big unstructured data.

Real-World Examples of Data Analytics Applications

To further illustrate the practical applications of data analytics, let’s explore some real-world examples:

  1. Retail: A retail company can use descriptive analytics to analyze sales data and identify the most popular products, best-selling locations, and peak sales periods. Diagnostic analytics can help identify the factors that contribute to low sales in certain regions or product categories. Predictive analytics can be used to forecast demand and optimize inventory levels. Prescriptive analytics can suggest the best pricing strategy or promotional activities to maximize profitability.
  2. Healthcare: In healthcare, descriptive analytics can be used to analyze patient data and identify disease patterns, risk factors, and treatment outcomes. Diagnostic analytics can help identify the factors that contribute to readmissions or complications. Predictive analytics can be used to predict patient outcomes or identify patients at high risk of certain diseases. Prescriptive analytics can optimize treatment plans based on patient data and medical guidelines.
  3. Finance: In finance, descriptive analytics can be used to analyze financial data and identify trends, anomalies, or patterns of fraud. Diagnostic analytics can help identify the factors that contribute to financial losses or underperformance. Predictive analytics can be used to assess creditworthiness, predict market trends, or detect anomalies in trading activities. Prescriptive analytics can optimize investment portfolios or recommend trading strategies.
  4. Manufacturing: In manufacturing, descriptive analytics can be used to analyze production data and identify bottlenecks, defects, or quality issues. Diagnostic analytics can help identify the factors that contribute to low productivity or high defect rates. Predictive analytics can be used to forecast demand, optimize production schedules, or predict machine failures. Prescriptive analytics can optimize production processes, minimize costs, or improve product quality.

These examples demonstrate how data analytics can be applied in various industries to gain insights, make informed decisions, and optimize business operations. Organizations can leverage different types of data analytics based on their specific needs and goals.

Choosing the Right Type of Data Analytics for Your Business

Deciding on the right type of data analytics should involve identifying what you hope to achieve with data analysis, what data you have access to, and your abilities in performing analyses. However, all types of analytics provide different methods and insights, and the decision should be based on the type of business and issues that have to be addressed.

Descriptive analytics is an excellent initial approach to visualize your past performances and patterns. Thus, it forms a good basis for further evaluations and assists in decision-making by determining past effectiveness. Diagnostic analytics is ideal to use when you wish to pursue a deeper understanding of why specific results were achieved or if there are problems that need addressing.

Predictive analytics will come in handy if you need to make some forecast about future events, forecast the tendencies, behavior of the customers and make proper adjustments to the further steps. Prescriptive analytics can be used where one needs to make a decision on the best course of action given the different objectives and their corresponding constraints.

Cognitive analytics is appropriate when you are interested in variable data sources, for instance, social media platforms, customer feedback, or articles. It assists in extracting information from text or image and make independent decisions based on it using natural language processing or computer vision respectively.

Finally, it is mandatory to underline that organizations may use various types of analytics simultaneously to obtain comprehensive perceptions of their performance. For instance, a retail firm might employ descriptive analytics to examine past trends in sales, diagnostic analytics to determine why sales are low, predictive analytics to consider expected trends in the future, and prescriptive analytics to determine the optimal approach to pricing.

Challenges and Considerations in Implementing Data Analytics

Implementing data analytics in an organization can be a complex process that comes with its own set of challenges and considerations. Here are a few key factors to keep in mind:

  1. Data Quality: Data quality is essential for accurate and reliable analytics. Organizations need to ensure that their data is clean, complete, and consistent. Data cleansing and pre-processing techniques may be required to remove duplicates, handle missing values, and correct errors.
  2. Data Integration: Data analytics often requires integrating data from multiple sources. Organizations need to have a robust data integration strategy in place to ensure seamless access to all relevant data. This may involve data consolidation, data warehousing, or the use of data integration tools.
  3. Analytical Skills: Data analytics requires a certain level of analytical skills and expertise. Organizations need to invest in training their employees or hiring data analysts who have the necessary knowledge and skills to perform data analytics effectively.
  4. Data Privacy and Security: Data analytics involves handling sensitive and confidential data. Organizations need to ensure that appropriate measures are in place to protect data privacy and security. This may involve implementing data encryption, access controls, and data anonymization techniques.
  5. Infrastructure and Tools: Data analytics requires the right infrastructure and tools to process and analyze large volumes of data. Organizations need to invest in scalable and efficient hardware, software, and cloud computing resources to support their analytics initiatives.
  6. Ethical Considerations: Data analytics raises ethical considerations, especially when it comes to handling personal data and making automated decisions. Organizations need to ensure that their analytics processes are transparent, fair, and comply with relevant data protection regulations.
  7. Continuous Improvement: Data analytics is an ongoing process that requires continuous improvement and refinement. Organizations need to continuously monitor and evaluate their analytics initiatives to ensure that they are delivering the expected value and making a positive impact on business outcomes.

By considering these challenges and taking appropriate measures, organizations can overcome hurdles and successfully implement data analytics to drive insights and improve decision-making.

Tools and Technologies for Data Analytics

To implement data analytics effectively, organizations need to leverage a wide range of tools and technologies. Here are some commonly used tools and technologies for different types of data analytics:

  1. Descriptive Analytics: For descriptive analytics, organizations can use tools such as Microsoft Excel, Tableau, or Power BI to summarize and visualize data. These tools provide a user-friendly interface for data exploration, data visualization, and dashboard creation.
  2. Diagnostic Analytics: Diagnostic analytics often requires advanced statistical techniques and data mining algorithms. Tools such as R, Python, or SAS can be used for statistical analysis and modeling. These tools provide a wide range of statistical functions and libraries for data exploration and hypothesis testing.
  3. Predictive Analytics: Predictive analytics requires more advanced techniques such as machine learning algorithms. Tools such as Python, R, or TensorFlow can be used for predictive modeling and analysis. These tools provide a wide range of machine learning algorithms and libraries for building predictive models.
  4. Prescriptive Analytics: Prescriptive analytics often involves optimization algorithms and simulation models. Tools such as IBM CPLEX, Gurobi, or AnyLogic can be used for optimization and simulation. These tools provide advanced algorithms and modeling capabilities for solving complex optimization problems.
  5. Cognitive Analytics: Cognitive analytics requires natural language processing, image recognition, or sentiment analysis capabilities. Tools such as IBM Watson, Google Cloud Natural Language, or Microsoft Azure Cognitive Services can be used for cognitive analytics. These tools provide APIs and libraries for processing unstructured data and extracting meaning from textual or visual sources.

It is important for organizations to select the right tools and technologies based on their specific requirements, available resources, and budget. It is also crucial to have a skilled team or external support who can effectively utilize these tools and technologies to derive meaningful insights from data.


Data analytics is an effective approach that allows organizations to gain knowledge, make correct decisions, and improve their efficiency. In this article, we explored the five major types of data analytics: Descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, and cognitive analytics.

Descriptive analytics gives a view of what has happened in the past and is used to understand past occurrences. Diagnostic analytics takes it a notch higher by trying to explain why some things happened in the first place. Thus, predictive analytics involves using past data to forecast future occurrences. Prescriptive analytics focuses on advising the right course of action taking into consideration various objectives and constraints. Cognitive analytics is the combination of artificial intelligence and machine learning for automatic decision making on text based data.

Different approaches of data analytics all come with their benefits and it is possible for an organization to use more than one technique to analyze their operations. Thus, organizations are capable of leveraging data analytics and achieving success in their industries by choosing the proper type of analytics, taking into account challenges and considerations, and utilizing relevant tools and technologies. Therefore, wake up to the power of data analytics and let your data tell you more than you ever thought possible.

At Emerging Freelancer, we provide highly rated data analysis services that will help you to run a successful business. We take care of converting raw data into helpful information for your business by using the most advanced tools and methods available. From data graphical representation, business intelligence solutions, to engaging in data consulting services for an organization, our services are customized, letting you stand out in this analytics demanded era. Unleash the full potential of your business with Emerging Freelancer and learn more about the advantages of using data.

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