Traditionally, businesses have used conventional, intuition based decision making which involved lots of subjectivity but the explosion of data and an advent of analytics in recent times has led to a new era of data-driven decision making. Today, the use of analytics in strategic planning has become indispensable to gain competitive advantage and reach business goals. Organizations with readily available data coupled with efficient data processing ability are moving away from traditional methods to a more focused data based approach. In today’s data-driven environment, every industry is using analytics to generate actionable insights for informed decision making which help businesses to realize its revenue potential and achieve operational efficiencies.


Since a long time, analytics is being used in business but its use was limited due to non-availability of organized data and computational efficiency. However, analytics field has evolved over the years largely due to the introduction of computers, improved data storing capacity and development of new tools. Today, a large magnitude of data is getting generated every day and this enormous information is easily available to the organizations which have analytics capabilities to process and gain valuable information. Majority of the data is often available in raw form and not directly useful to the business but it can be processed using different types of analytics to generate actionable insights which can then be used to make sound business decisions.

Analytics is thus the science of examining & evaluating data using different tools & techniques to generate valuable insights. These insights help to unravel hidden patterns, understand historical trends and predict future outcomes. In simple terms, analytics is the scientific process of transforming data into insight for making informed decisions.

Broadly, there are four different types of analytics used to process the data and each one of them is unique and uses specific methods to generate different kinds of information. The first two types are used to generate insights to understand the business better but it's from the second two types that one can really get the foresight to drive business forward.


Descriptive Analytics:

Descriptive analytics is the simplest type of analytics where we process and examine raw data to summarize it into useful form of insights. It helps businesses to understand ‘what has happened’ during a certain time period. This type of analytics is used in most of the organizations very frequently and repetitively. Dashboards and MIS reports use descriptive form of analytics to derive insights from the data.

Diagnostic Analytics:

Diagnostic analytics is investigative in nature and helps business to understand the reasons behind “why something has happened”. All kinds of exploratory & confirmatory data analysis is used to understand the root causes of a problem. Business Intelligence & Data Visualization tools uses diagnostic analytics to generate insights. Segmentation & profiling is also another form of exploratory analytics.


Predictive Analytics:

Predictive analytics are probabilistic in nature and helps to predict “what might happen” in the future. It uses various data mining, statistical modelling and machine learning techniques to study recent and historical data to predict future events & outcomes and also explains drivers of the observed phenomenon. Propensity models like Churn, Cross Sell and Up Sell are the most common type of predictive analytics.

Prescriptive Analytics:

Prescriptive analytics is the type of analytics wherein we need to recommend an action, so the business can use this information and act accordingly. Prescriptive analytics helps us understand ‘what action one should take while foreseeing future events to achieve business objectives. Optimization and simulation is the best form of prescriptive analytics. Using scored results of predictive models to roll out specific target campaigns is also another form of prescriptive analytics.

Complexity of analytical methods and required skill sets increases as we move from descriptive towards prescriptive analytics but so is the value it creates to the business. Although, most of the organizations have been using descriptive analytics, its objective was very limited to understand their business better using insights from the available data. Lately, companies started adopting more complex type of analytics (predictive & prescriptive) primarily due to availability of powerful tools & techniques and its potential to forecast business outcomes for better planning.


Analytics offers immense value to organizations worldwide and across different domains where it has consistently delivered significant positive impact to businesses. The most significant benefits of implementing business analytics were identified as following:

  • Improved decision-making process based on data
  • Improved overall organizational performance
  • Better strategic planning
  • Better customer relationship management
  • Better resource utilization
  • Better risk management
  • Accomplish operational efficiencies

Today, analytics is used practically in every business domain like BFSI, Retail, FMCG, Manufacturing, Healthcare and E-Commerce where data is rich and readily available. However, the role is somewhat subdued in other sectors largely due to non-availability or lack of organized data. It should also be noted that many sectors that have built analytical capabilities, have organized data available which is getting updated regularly. Following are some of the domains and functions where use of analytics is predominant.

Some specific examples of extensive application of analytics across domains are described below:

  • The BFSI Industry was an early adopter for the use of analytics largely due to the necessity of fact-based decision making process and availability of processed data which gets updated every day. They use analytics for fraud detection, risk management, loss forecasting, portfolio optimization and channel optimization, etc. It is also used to segment customers on the basis of their credit behaviour, risk profiles, delinquency etc., and offer products that are customized for them. Majority of credit bureaus and lending firms deploy sophisticated analytics to understand credit worthiness of their customers.
  • Retail organizations generate and process enormous amount of data using analytics and manage their day to day operations to maintain inventory, optimize supply chain, assortment planning, SKU rationalization, improve store efficiency and profitability, managing customers and marketing effectiveness and offer optimization based on consumer preferences.
  • E-Commerce companies generates terabytes of data daily and it is managed using advanced analytics to interact with numerous customers and sellers very efficiently. They understand consumer behaviour based on their browsing and transaction history and build product based recommendation engines using analytics. They also manage demand and supply of different products with the help of analytics and use these insights for setting optimum prices of available products.
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  • n Healthcare service providers are also using analytics to process patient medical history for better decision making. Most other service sector industries also use analytics to understand consumer behaviour and devise appropriate marketing strategies. It is also used to build propensity models and use optimization and simulation techniques to manage customers and generate better revenues.
  • Human Resources (HR) Analytics is used for effective employee management. It allows organizations to identify workforce trends, forecast future staffing needs and improve employee satisfaction, etc.
  • Social media and web analytics are a relatively new domain but gathering momentum because of the public usage and reach of online platforms. The same is being used to understand the consumer behaviour and their sentiments towards certain products, brands, organizations etc. Recently, credit agencies have started using social media data to understand customer’s risk behaviour to make lending decisions.


Today, there is no doubt that data based decisions are helping every business to drive their strategic initiatives in the right direction to reach their goals. Organizations which have integrated analytics into their decision making process have recognized and appreciated its real potential. Yet, most of the organizations are using analytics on a much smaller scale, restricting its true value. However, time has come to embrace analytics across all levels thereby helping every single function to optimize their decision making process.


For a further conversation on this subject of Cedar View or how we may be able to help please email us Cedar at india@cedar-consulting.com

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Thomas Cook
GoldMan Sachs

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