What is Data Analytics?




Data Analytics

  1. Data analytics is a science that uses raw data to identify patterns, answer questions, and draw conclusions. 
  2. It uses various techniques to convert raw data into a form that allows organizations to analyze important metrics. 
  3. Data analytics can help businesses understand what has happened in the past and provide a base to track trends.


Types of Data Analytics

Data analytics is a broad field. There are four primary types of data analytics: 

  1. Descriptive Analytics (What happened)
  2. Diagnostic Analytics (Why something happened)
  3. Predictive Analytics (What's going to happen)
  4. Prescriptive Analytics (What we should do about it)

Each type has a different goal and place in the data analysis process.



1. Descriptive analytics

  1. Descriptive analytics helps answer the question "What happened?"
  2. For a business, this can be used to describe outcomes to stakeholders.
  3. By developing key performance indicators (KPIs), descriptive analysis strategies can help track successes or failures.
  4. For example, metrics such as return on investment (ROI) are often used. 
  5. Specialized metrics can also be developed to track performance specific to an industry. 
  6. This process requires the collection of relevant data, data processing, analysis, and visualization. 
  7. Together, this can provide essential insight into past performance.


2. Diagnostic analytics

  1. Diagnostic analytics helps answer questions about why things happened. 
  2. These techniques supplement basic descriptive analytics. 
  3. They take the findings from descriptive analytics and dig deeper to find the causes behind trends and outcomes. 
  4. Key performance indicators are further investigated to discover why they improved or worsened. 

Diagnostic analysis could look like the following steps:

  1. Identify anomalies in the data. These may be unexpected changes in a metric or a particular market.
  2. Collect data related to these anomalies.
  3. Implement statistical techniques to find relationships and trends that explain the anomalies.


3. Predictive analytics

  1. Predictive analytics helps answer questions about what will happen in the future. 
  2. These techniques use historical data to identify trends and determine if they are likely to reoccur or change. 
  3. Predictive analytical tools and techniques include a variety of statistical and machine learning techniques, such as neural networks, decision trees, and regression.


4. Prescriptive analytics

  1. Prescriptive analytics helps answer questions about what should be done. 
  2. Data-driven decisions can be made using insights from predictive analytics, even in the face of uncertainty. 
  3. Prescriptive analytics techniques rely on machine learning strategies to find patterns in large datasets.


Conclusion

  1. These types of data analytics provide the insight that businesses need to make effective and efficient decisions. 
  2. Used in combination, they provide a well-rounded understanding of a company’s needs and opportunities.



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