Types of Analytics: descriptive, predictive, prescriptive analytics

Types of Analytics: descriptive, predictive, prescriptive analytics

Last Update Made On August 1, 2019

The big data revolution has given birth to different kinds, types and stages of data analysis. Boardrooms across companies are buzzing around with data analytics - offering enterprise wide solutions for business success. However, what do these really mean to businesses? The key to companies successfully using Big Data, is by gaining the right information which delivers knowledge, that gives businesses the power to gain a competitive edge. The main goal of big data analytics is to help organizations make smarter decisions for better business outcomes.

Thomas Jefferson said – “Not all analytics are created equal.”

Types of Analytics -descriptive predictive prescriptive analytics

Big data analytics cannot be considered as a one-size-fits-all blanket strategy. In fact, what distinguishes a best data scientist or data analyst from others, is their ability to identify the kind of analytics that can be leveraged to benefit the business - at an optimum. The three dominant types of analytics –Descriptive, Predictive and Prescriptive analytics, are interrelated solutions helping companies make the most out of the big data that they have. Each of these analytic types offers a different insight. In this article we explore the three different types of analytics -Descriptive Analytics, Predictive Analytics and Prescriptive Analytics - to understand what each type of analytics delivers to improve on, an organization’s operational capabilities.

Get free access to 100+ ready-to-use Data Science code solutions - Click here

Understanding Predictive and Descriptive Analytics

A lioness hired a data scientist (fox) to help find her prey. The fox had access to a rich DataWarehouse, which consisted of data about the jungle, its creatures and events happening in the jungle.

On its first day, the fox presented lioness with a report summarizing where she found her prey in the last six months, which helped  the lioness decide where to go hunting next. This is an example of DescriptiveAnalytics.

Next, the fox estimated the probability of finding a given prey at a certain place and time, using advanced ML techniques. This is PredictiveAnalytics. Also, it identified routes in the jungle for the lioness to take to minimize her efforts in finding her prey. This is an example of Optimization.

Finally, based on above models, the fox got trenches dug at various points in the jungle so that the prey got caught automatically. This is Automation.

Descriptive Analytics -> Predictive Analytics / Optimization -> Automation. This is the AnalyticsLifeCycle.

Free access to solved use-cases with code can be found here (these are ready-to-use for your projects)

Work on Hands on Projects in Big Data and Hadoop

Types of Analytics

descriptive predictive prescriptive analytics

Big data analytics helps a business understand the requirements and preferences of a customer, so that businesses can increase their customer base and retain the existing ones with personalized and relevant offerings of their products or services. According to IDC, the big data and analytics industry is anticipated to grow at a CAGR of 26.4% reaching a value of $41.5 billion by end of 2018. The big data industry is growing at a rapid pace due to various applications like smart power grid management, sentiment analysis, fraud detection, personalized offerings, traffic management, etc. across myriad industries. After the organizations collect big data, the next important step is to get started with analytics. Many organizations do not know where to begin, what kind of analytics can nurture business growth and what these different types of analytics mean.


To help release your Data Science projects faster we have put together a library of solved code example. Click here to get free access.

What is Descriptive Analytics?

90% of organizations today use descriptive analytics which is the most basic form of analytics. The simplest way to define descriptive analytics is that, it answers the question “What has happened?”. This type of analytics, analyses the data coming in real-time and historical data for insights on how to approach the future. The main objective of descriptive analytics is to find out the reasons behind precious success or failure in the past. The ‘Past’ here, refers to any particular time in which an event had occurred and this could be a month ago or even just a minute ago. The vast majority of big data analytics used by organizations falls into the category of descriptive analytics.

A business learns from past behaviours to understand how they will impact future outcomes. Descriptive analytics is leveraged when a business needs to understand the overall performance of the company at an aggregate level and describe the various aspects.

Dr. Michael Wu, chief scientist of San Francisco-based Lithium Technologies describes descriptive analytics as -“The simplest class of analytics, one that allows you to condense big data into smaller, more useful nuggets of information.”

Descriptive analytics are based on standard aggregate functions in databases, which just require knowledge of basic school math. Most of the social analytics are descriptive analytics. They summarize certain groupings based on simple counts of some events. The number of followers, likes, posts, fans are mere event counters. These metrics are used for social analytics like average response time, average number of replies per post, %index, number of page views, etc. that are the outcome of basic arithmetic operations.

The best example to explain descriptive analytics are the results, that a business gets from the web server through Google Analytics tools. The outcomes help understand what actually happened in the past and validate if a promotional campaign was successful or not based on basic parameters like page views.

Anytime you are stuck on your project, use our code recipes for just-in-time troubleshooting (these are ready-to-use for your projects)

What is Predictive Analytics?

The subsequent step in data reduction is predictive analytics. Analysing past data patterns and trends can accurately inform a business about what could happen in the future. This helps in setting realistic goals for the business, effective planning and restraining expectations. Predictive analytics is used by businesses to study the data and ogle into the crystal ball to find answers to the question “What could happen in the future based on previous trends and patterns?”

Dr. Michael Wu, chief scientist of San Francisco-based Lithium Technologies said -"The purpose of predictive analytics is NOT to tell you what will happen in the future. It cannot do that. In fact, no analytics can do that. Predictive analytics can only forecast what might happen in the future, because all predictive analytics are probabilistic in nature."

Organizations collect contextual data and relate it with other customer user behaviour datasets and web server data to get real insights through predictive analytics. Companies can predict business growth in future if they keep things as they are. Predictive analytics provides better recommendations and more future looking answers to questions that cannot be answered by BI.

Predictive analytics helps predict the likelihood of a future outcome by using various statistical and machine learning algorithms but the accuracy of predictions is not 100%, as it is based on probabilities. To make predictions, algorithms take data and fill in the missing data with best possible guesses. This data is pooled with historical data present in the CRM systems, POS Systems, ERP and HR systems to look for data patterns and identify relationships among various variables in the dataset. Organizations should capitalise on hiring a group of data scientists in 2016 who can develop statistical and machine learning algorithms to leverage predictive analytics and design an effective business strategy.

Predictive analytics can be further categorized as –

  1. Predictive Modelling –What will happen next, if ?
  2. Root Cause Analysis-Why this actually happened?
  3. Data Mining- Identifying correlated data (click here to get sample use-cases with code).
  4. Forecasting- What if the existing trends continue?
  5. Monte-Carlo Simulation – What could happen?
  6. Pattern Identification and Alerts –When should an action be invoked to correct a process.

Sentiment analysis is the most common kind of predictive analytics. The learning model takes input in the form of plain text and the output of the model is a sentiment score that helps determine whether the sentiment is positive, negative or neutral.

Organizations like Walmart, Amazon and other retailers leverage predictive analytics to identify trends in sales based on purchase patterns of customers, forecasting customer behaviour, forecasting inventory levels, predicting what products customers are likely to purchase together so that they can offer personalized recommendations, predicting the amount of sales at the end of the quarter or year. The best example where predictive analytics find great application is in producing the credit score. Credit score helps financial institutions decide the probability of a customer paying credit bills on time.

Free access to solved use-cases with code can be found here (these are ready-to-use for your projects)

What is Prescriptive Analytics?

Big data might not be a reliable crystal ball for predicting the exact winning lottery numbers but it definitely can highlight the problems and help a business understand why those problems occurred. Businesses can use the data-backed and data-found factors to create prescriptions for the business problems, that lead to realizations and observations.

Prescriptive analytics is the next step of predictive analytics that adds the spice of manipulating the future. Prescriptive analytics advises on possible outcomes and results in actions that are likely to maximise key business metrics. It basically uses simulation and optimization to ask “What should a business do?” 

Prescriptive analytics is an advanced analytics concept based on –

  • Optimization that helps achieve the best outcomes.
  • Stochastic optimization that helps understand how to achieve the best outcome and identify data uncertainties to make better decisions.

Simulating the future, under various set of assumptions, allows scenario analysis - which when combined with different optimization techniques, allows prescriptive analysis to be performed. Prescriptive analysis explores several possible actions and suggests actions depending on the results of descriptive and predictive analytics of a given dataset.

Prescriptive analytics is a combination of data,  and various business rules. The data for prescriptive analytics can be both internal (within the organization) and external (like social media data).Business rules are preferences, best practices, boundaries and other constraints. Mathematical models include natural language processing, machine learning, statistics, operations research, etc.

Prescriptive analytics are comparatively complex in nature and many companies are not yet using them in day-to-day business activities, as it becomes difficult to manage. Prescriptive analytics if implemented properly can have a major impact on business growth. Large scale organizations use prescriptive analytics for scheduling the inventory in the supply chain, optimizing production, etc. to optimize customer experience.

Aurora Health Care system saved $6 million annually by using prescriptive analytics to reduce re-admission rates by 10%. Prescriptive analytics can be used in healthcare to enhance drug development, finding the right patients for clinical trials, etc.

As increasing number of organizations realize that big data is a competitive advantage and they should ensure that they choose the right kind of data analytics solutions to increase ROI, reduce operational costs and enhance service quality.



Click here to get free access to 100+ ready-to-use
Data Science code soluions




Work on hands on projects on Big Data and Hadoop with Industry Professionals

Relevant Projects

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

Tough engineering choices with large datasets in Hive Part - 1
Explore hive usage efficiently in this hadoop hive project using various file formats such as JSON, CSV, ORC, AVRO and compare their relative performances

Yelp Data Processing using Spark and Hive Part 2
In this spark project, we will continue building the data warehouse from the previous project Yelp Data Processing Using Spark And Hive Part 1 and will do further data processing to develop diverse data products.

Airline Dataset Analysis using Hadoop, Hive, Pig and Impala
Hadoop Project- Perform basic big data analysis on airline dataset using big data tools -Pig, Hive and Impala.

Design a Hadoop Architecture
Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.

Online Hadoop Projects -Solving small file problem in Hadoop
In this hadoop project, we are going to be continuing the series on data engineering by discussing and implementing various ways to solve the hadoop small file problem.

Analysing Big Data with Twitter Sentiments using Spark Streaming
In this big data spark project, we will do Twitter sentiment analysis using spark streaming on the incoming streaming data.

Real-Time Log Processing using Spark Streaming Architecture
In this Spark project, we are going to bring processing to the speed layer of the lambda architecture which opens up capabilities to monitor application real time performance, measure real time comfort with applications and real time alert in case of security

Data processing with Spark SQL
In this Apache Spark SQL project, we will go through provisioning data for retrieval using Spark SQL.

Real-time Auto Tracking with Spark-Redis
Spark Project - Discuss real-time monitoring of taxis in a city. The real-time data streaming will be simulated using Flume. The ingestion will be done using Spark Streaming.