Analytics Model Types

Every day, businesses are creating around 2.5 quintillion bytes of data, making it increasingly difficult to make sense and get valuable information from this data. And while this data can reveal a lot about customer bases, users, and market patterns and trends, if not tamed and analyzed, this data is just useless. Therefore, for organizations to realize the full value of this big data, it has to be processed. This way, businesses can pull powerful insights from this stockpile of bits.

And thanks to artificial intelligence and machine learning, we can now do away with mundane spreadsheets as a tool to process data. Through the various AI and ML-enabled data analytics models, we can now transform the vast volumes of data into actionable insights that businesses can use to scale operational goals, increase savings, drive efficiency and comply with industry-specific requirements.

We can broadly classify data analytics into three distinct models:

  • Descriptive
  • Predictive
  • Prescriptive

Let’s examine each of these analytics models and their applications.

Descriptive Analytics. A Look Into What happened?

How can an organization or an industry understand what happened in the past to make decisions for the future? Well, through descriptive analytics.

Descriptive analytics is the gateway to the past. It helps us gain insights into what has happened. Descriptive analytics allows organizations to look at historical data and gain actionable insights that can be used to make decisions for “the now” and the future, upon further analysis.

For many businesses, descriptive analytics is at the core of their everyday processes. It is the basis for setting goals. For instance, descriptive analytics can be used to set goals for better customer experience. By looking at the number of tickets raised in the past and their resolutions, businesses can use ticketing trends to plan for the future.

Some everyday applications of descriptive analytics include:

  • Reporting of new trends and disruptive market changes
  • Tabulation of social metrics such as the number of tweets, followers gained over some time, or Facebook likes garnered on a post.
  • Summarizing past events such as customer retention, regional sales, or marketing campaigns success.

To enhance their decision-making capabilities businesses have to reduce the data further to allow them to make better future predictions. That’s where predictive analytics comes in.

Predictive Analytics takes Descriptive Data One Step Further

Using both new and historical data sets predictive analytics to help businesses model and forecast what might happen in the future. Using various data mining and statistical algorithms, we can leverage the power of AI and machine learning to analyze currently available data and model it to make predictions about future behaviors, trends, risks, and opportunities. The goal is to go beyond the data surface of “what has happened and why it has happened” and identify what will happen.

Predictive data analytics allows organizations to be prepared and become more proactive, and therefore make decisions based on data and not assumptions. It is a robust model that is being used by businesses to increase their competitiveness and protect their bottom line.

The predictive analytics process is a step-by-step process that requires analysts to:

  • Define project deliverables and business objectives
  • Collect historical and new transactional data
  • Analyze the data to identify useful information. This analysis can be through inspection, data cleaning, data transformation, and data modeling.
  • Use various statistical models to test and validate the assumptions.
  • Create accurate predictive models about the future.
  • Deploy the data to guide your day-to-data actions and decision-making processes.
  • Manage and monitor the model performance to ensure that you’re getting the expected results.

Instances Where Predictive Analytics Can be Used

  • Propel marketing campaigns and reach customer service objectives.
  • Improve operations by forecasting inventory and managing resources optimally.
  • Fraud detection such as false insurance claims or inaccurate credit applications
  • Risk management and assessment
  • Determine the best direct marketing strategies and identify the most appropriate channels.
  • Help in underwriting by predicting the chances of bankruptcy, default, or illness.
  • Health care: Use predictive analytics to determine health-related risk and make informed clinical support decisions.

Prescriptive Analytics: Developing Actionable Insights from Descriptive Data

Prescriptive analytics helps us to find the best course of action for a given situation. By studying interactions between the past, the present, and the possible future scenarios, prescriptive analytics can provide businesses with the decision-making power to take advantage of future opportunities while minimizing risks.

Using Artificial Intelligence (AI) and Machine Learning (ML), we can use prescriptive analytics to automatically process new data sets as they are available and provide the most viable decision options in a manner beyond any human capabilities.

When effectively used, it can help businesses avoid the immediate uncertainties resulting from changing conditions by providing them with fact-based best and worst-case scenarios. It can help organizations limit their risks, prevent fraud, fast-track business goals, increase operational efficiencies, and create more loyal customers.

Bringing It All Together

As you can see, different big data analytics models can help you add more sense to raw, complex data by leveraging AI and machine learning. When effectively done, descriptive, predictive, and prescriptive analytics can help businesses realize better efficiencies, allocate resources more wisely, and deliver superior customer success most cost-effectively. But ideally, if you wish to gain meaningful insights from predictive or even prescriptive analytics, you must start with descriptive analytics and then build up from there.

What Is Machine Learning?

Machine Learning
Machine Learning

Machine learning is Artificial Intelligence (AI) which enables a system to learn from data rather than through explicit programming.  Machine learning uses algorithms that iteratively learn from data to improve, describe data, and predict outcomes.  As the algorithms ingest training data to produce a more precise machine learning model. Once trained, the machine learning model, when provided data will generate predictions based on the data that taught the model.  Machine learning is a crucial ingredient for creating modern analytics models.