At present, most organizations rely heavily on data to make critical business decisions.
However, mere data collection is not enough. Without valuable insights, data is just a bunch of numbers that do not contribute much to your decision-making process. This is where analytics solutions come into the picture. They provide a way to make sense of data and draw meaningful conclusions from it. But with a plethora of analytics solutions available in the market, it can be overwhelming to determine which one suits your business needs.
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4 Types of BI
In this article, we will discuss the different types of analytics and how they can benefit your business.
Descriptive Analytics: A Look at the Past
Descriptive analytics refers to the use of data to provide insights into what has happened in the past. It is primarily focused on providing context and helping stakeholders interpret information. Data visualizations like graphs, charts, reports, and dashboards are used to represent the data statistically.
In healthcare, for example, if a sudden surge in the number of patients being admitted to the casualty department occurs, descriptive analytics would provide real-time data that includes relevant statistics, such as the date of occurrence, volume, and patient details. This can help medical professionals get a better understanding of the situation and take appropriate measures to address it. It would also help to identify longer terms trends, such as an increase in intakes on a Friday night.
Diagnostic Analytics: Understanding the Cause
Diagnostic analytics takes the descriptive data a step further to provide a deeper analysis of why something happened in the past. It helps in root cause analysis and uses processes like data discovery, data mining, and drill down and drill through.
Using the healthcare example mentioned earlier, diagnostic analytics would explore the data to make correlations and identify potential causes. For instance, by analyzing the symptoms of the patients, it could determine there is an increase in the number of trauma injuries admitted to casualty on a weekend, and that this may be caused by alcohol abuse.
Predictive Analytics: Anticipating the Future
Predictive analytics takes historical data and uses machine learning algorithms to identify patterns and trends. This information is then used to make predictions about what is most likely to happen in the future. Analyzing patterns in the data can identify potential future outcomes and provide insights into how to prepare for them.
For example, using predictive analytics, healthcare professionals can forecast a surge in patients being admitted to casualty on weekends. Based on the patterns in the data, they can determine that additional staff will be required over weekends, particularly in the late evenings.
Prescriptive Analytics: Taking Action
Prescriptive analytics takes predictive data to the next level by suggesting various courses of action that can be taken to affect future outcomes. It outlines the potential implications of each action and provides insights into which one is most likely to yield the desired outcome.
Returning to our healthcare example, prescriptive analytics would suggest that the hospital increase the number of staff on hand to adequately treat the influx of patients. This can help ensure that the hospital is well-prepared to handle any emergencies that may arise in the future.
Conclusion
In summary, each type of analytics plays a crucial role in making informed business decisions. Descriptive and diagnostic analytics help to understand what happened in the past and why it happened, while predictive and prescriptive analytics help to anticipate what is likely to happen in the future and suggest actions that can be taken to affect those outcomes. A combination of these analytics can provide a holistic view of your business, enabling you to make smarter decisions and stay ahead of the competition.
Read our definition of business intelligence to know the true meaning of Business Intelligence.
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