Types of Analysis

Mahmoud El Fahl 3/24/2021
Analysis of data is a vital part of running a successful business and have a holistic view of the market, and forecast what might happen in the future to make a company competes efficiently within that market

There are four types ( Descriptive, Diagnostic, Predictive, Prescriptive  Analytics)  of data analysis that are in use across all industries

While we separate these into categories, they are all linked together and 
build upon each other. As you begin moving from the simplest type of  analytics to more complex, the degree of difficulty and resources required increases. At the same time, the level of added insight and value also increases

According to Gartner, the number of companies using prescriptive analytics tools may increase from ~10% in 2016 to over 35% by the end of 2020


Type 1 &2 : Descriptive and Diagnostic Analytic  (Business Intelligent)  

Answers the Question: What happened and why

Primary Tools: Data Aggregation

Data Pyramid: Data & Information

Limitation and Using

Descriptive analytics allow us to analysis the data coming in real-time or historical data to learn from past behaviors, and understand how they might influence future outcomes

Most business functions in your company are already using descriptive analytics in the form of recurring or custom reports. With analytics tools, you can dive deeper into the past and retrieve meaningful results. You can also conduct Diagnostic Analytics which focuses on understanding why something happened by using drill down and the root-cause of a problem

Example 

Total stock in inventory, average dollars spent per customer, year-over-yea change in sales and How much of a given product you sold over a certain time-period

 

Type 3: Predictive Analytics (Machine Learning) 

Answers the Question: What will happen 

Primary Tools: ML, statistical models and data mining 

Data Pyramid: Knowledge

Limitations and Using

We use Predictive Analytics any time to know something about the future, or fill in the information that you do not have, or provide estimates about the likelihood of a future outcome

It is important to remember that no statistical algorithm can “predict” the future with 100% certainty. Companies use these statistics to forecast what might happen in the future. This is because the foundation of predictive analytics is based on probabilities

Example

  • Results may be coarse-grained (e.g., expected industry growth or raw material pricing)
  • Company-centric (e.g., sales or revenue or profit growth)
  • Operational (e.g., expected changes in demand by product line)
  • Customer behavior: purchasing patterns to identifying trends in sales activities, predicting what items customers will purchase together
  • Forecasting inventory levels based upon a myriad of variables
  • Produce a credit score in the financial services to determine the probability of customers making future credit payments on time

Type 4: Prescriptive Analytics (Decision Science)

Answers the Question: What can we make it happen

Primary Tools: Stochastic Optimization, simulation and heuristics

Data Pyramid : Wisdom

Limitations and using

Prescriptive analytics is an advanced analytics concept based on Stochastic optimization that helps understand how to achieve the best outcome and identify data uncertainties to make better decisions  

Prescriptive analytics attempts to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, prescriptive analytics predicts not only what will happen, but also why it will happen, providing recommendations regarding actions that will take advantage of the predictions  

Prescriptive analytics apply a range of option constraints to maximize or minimize your objective, reduce decision-making risk, Free up time for higher-value efforts such as performing scenario analysis or considering larger Strategic questions   

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 

Example

    • Large scale organizations : scheduling the inventory in the supply chain, optimizing production, etc. to optimize customer experience

    • Business decision questions: In which order should you produce what products? In which manufacturing facilities? On what product lines? In what quantities

    • Business Constraints: Minimum production of a given product, required manufacturing time and cost of a particular machine, Raw material inventory Finished goods inventory capacity

    • To maximize or minimize your objectives: Total product costs

For summarizing this article, Here are some Pictures

Data Pyramid 

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