We are all aware that numerous analytics exist to handle data, but many firms do not know when to use which. Indeed, what distinguishes the finest data scientist or data analyst from others is their ability to discover the proper type of analytics that best matches the company goals in order to maximise outcomes. All sorts of analytics provide unique information.
Understanding and using various types of analytics offered value to a firm by allowing it to better its organizational-level operational skills. Analytics may make the most of their unstructured or structured data by selecting the proper types of data. Discover five different types of analytics in the table below:
1. Descriptive Analytics
Descriptive analytics assists in collecting the most value from data mining in order to construct and experience a business intelligence system that analyses real-time and historical data to generate insights for future approaches.
Creating financial or sales reports is an example of descriptive analytics.
2. Diagnostic Analytics
Diagnostic analytics is the second type of data analytics that assists businesses in resolving crucial difficulties by determining if something is happening, why it is happening, and what the fundamental cause is.
Diagnostic analytics is useful when a company using business intelligence dashboards wants to drill down into the data to uncover the reasons or issues affecting the company. Integrating diagnostic analytics with descriptive analytics assists firms in discovering data relationships and architecture in order to do a quick comparison and construct the most trustworthy data-based decision model.
👉 The HR department analysing the applicant’s data sets is an example of diagnostic analytics.
3. Predictive Analytics
Forecasting the future, predicting market trends, changes in customer behaviour, and competitor analysis to optimise and design cutting-edge strategies to maximise business outcomes, is always fascinating. Forecasting is fundamental to predictive analytics.
Businesses use insights from descriptive and diagnostic analytics, as well as other historical data sets, to develop a recommendation-based model using advanced statistical and machine learning algorithms.
👉 For example, by analysing a patient’s past health records and basic demographics, predictive analytical models in healthcare can determine whether or not a person is at risk of having a heart attack.
👉 Similarly, predictive analytics can be used to create a campaign based on consumer buying behaviour at various moments in the past.
👉 Predictive analytics examples include analysing product recommendation data sets to forecast the likelihood of various outcomes.
4. Prescriptive Analytics
Prescriptive analytics is the next step following predictive analytics, and it assists firms in developing prescriptions to solve business challenges based on data-derived criteria.
Big data is a black box, and predicting the most trustworthy inputs is always questionable, but it always reveals why those problems arose. This is where prescriptive analytics comes in. Prescriptive analytics provides firms with advice on all conceivable outcomes and results in actions that are likely to maximise company outputs.
Prescriptive analytics is a business optimization data analytics method.
👉 Marketing and business cycle reports are two examples of prescriptive analytics.
5. Cognitive Analytics
Cognitive analytics is the most advanced type of analytics, using a variety of cognitive technologies such as artificial intelligence, machine learning algorithms, deep learning models, and others to process information and draw conclusions from existing data and patterns.
These discoveries are then added to the knowledge base in preparation for future interferences, and the self-learning feedback loop mimics human thinking to make cognitive applications smarter and more effective over time.
👉 Processing enormous parallel/undistributed data (such as contact centre conversation records) to generate insights is an example of cognitive analytics.
