Types of Analytics: descriptive, predictive, prescriptive analytics

Types of Analytics: descriptive, predictive, prescriptive analytics


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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, which 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. Big data analytics cannot be considered as a one-size-fits-all blanket strategy. In fact, what distinguishes the best data scientist or data analyst from others, is their ability to identify the different types 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.

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

Types of Analytics -descriptive predictive prescriptive analytics

Types of 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 the analytics mean. Let's explore the different types of analytics and the value they bring in to any business -

  • Descriptive Analytics

  • Predictive Analytics

  • Prescriptive Analytics

  • Diagnostic Analytics

 

descriptive predictive prescriptive analytics

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 behaviors 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.”

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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, the 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 is 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.

It is the simplest form of analytics. It describes or summarises the existing data using existing business intelligence tools. Therefore, it becomes easier to understand what is going on or what has happened. The main techniques used here are data mining and data aggregation.

Descriptive analytics involves using descriptive statistics such as arithmetic operations on existing data. These operations make raw data understandable to investors, shareholders, and managers. Thus, the clarity of data can help individuals as well as industries in analyzing key areas.

Predictive Analytics

The subsequent step in data reduction is predictive analytics. Analyzing 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 behavior datasets and web server data to get real insights through predictive analytics. Companies can predict business growth in the 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 the 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 capitalize 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.

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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 
  4. Forecasting- What if the existing trends continue?
  5. Monte-Carlo Simulation – What could happen?
  6. Pattern Identification and Alerts –When should 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 behavior, forecasting inventory levels, predicting what products customers are likely to purchase together so that they can offer personalized recommendations, predicting the number of sales at the end of the quarter or year. The best example where predictive analytics finds great application is in producing the credit score. A credit score helps financial institutions decide the probability of a customer paying credit bills on time.

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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 maximize 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 helps understand how to achieve the best outcome and identify data uncertainties to make better decisions.

Simulating the future, under various sets of assumptions, allows scenario analysis - which when combined with different optimization techniques, allows prescriptive analysis to be performed. The 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 is 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 the 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.

Diagnostic Analytics

Analytics performed on the internal data to understand the “why” behind what happened is referred to as diagnostic analytics. This kind of analytics is used by businesses to get an in-depth insight into a given problem provided they have enough data at their disposal. Diagnostic analytics helps identify anomalies and determine casual relationships in data. For example, eCommerce giants like Amazon can drill the sales and gross profit down to various product categories like Amazon Echo to find out why they missed on their overall profit margins. Diagnostic analytics also find applications in healthcare for identifying the influence of medications on a specific patient segment with other filters like diagnoses and prescribed medication.

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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 the 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 descriptive analytics.

Next, the fox estimated the probability of finding a given prey at a certain place and time, using advanced ML techniques. This is predictive analytics. 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 the 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.

As an 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.

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