Cognos Predictive Analytics: IBM Data Analytics

IBM Cognos Analytics is a business intelligence tool for handling and analyzing data. Cognos BI tool includes self-service features to share, explore, and prepare data for its users. Cognos Analytics IBM includes exploratory, descriptive, and predictive analytics techniques. This is known as numeric intelligence. To analyze data, Cognos Analytics uses statistical testing. 

This guide will help you understand predictive analytics, its techniques, types, benefits, and much more as they to Cognos Analytics.  

Defining Predictive Analytics  

Predictive analytics is a branch of advanced analytics that uses historical data along with machine learning, data mining, and statistical modeling techniques to make predictions about future events. Organizations use predictive analytics to understand patterns in their data to recognize potential opportunities and risks. Predictive analytics techniques are typically linked with data science and big data. 

Today, businesses are flooded with data from log files to animations, videos, and images which are stored in disparate data repositories. To help companies derive valuable insights from this data, data analysts use machine learning and deep learning algorithms to make predictions about future outcomes. Neural networks, decision trees, and linear and logistic regression models are some of the statistical techniques. Some of these models use initial predictive learnings to generate additional predictive insights. 

Predictive Analytics Models  

IBM Cognos Predictive Analytics models are developed to use historical data, identify patterns, understand trends, and utilize that data to conduct future trends. Some of the popular types or models of predictive analytics are: 

1. Classification models:

This category falls under supervised machine learning. It classifies data depending on historical data explaining relationships under a given dataset. For instance, the classification model is often applied by companies to classify prospects or customers for segmentation objectives. This model can also be used for answering true or false, or yes or no, to answer queries with binary outputs, credit risk evaluation, and fraud detection. This includes predictive analytics techniques such as:

• Decision trees
• Logistic regression
• Neural networks
• Random forest
• Naïve Bayes

2. Clustering models:

This category falls under unsupervised learning. They divide data depending on similar attributes. For instance, a commercializing company can use a clustering model to segregate users into similar groups depending on common features and design marketing techniques for every group. A few basic clustering algorithms are:

• K-means clustering
• Hierarchical clustering
• Density-based spatial clustering of applications with noise (DBSCAN)
• Mean-shift clustering
• Expectation-maximization (EM) clustering using Gaussian Mixture Models (GMM)

3. Time series models:

It uses several data inputs at a particular time frequency like yearly, monthly, weekly, daily, and so on. It is crucial to plot the dependent variable over time to evaluate the data for cyclical, trends, and seasonal activities that represents the requirement for particular model type and transformations. For instance, a call center often uses this model to analyze how many calls they may receive every hour at various times of the day. Frequently used time series models are:

• Autoregressive (AR)
• Moving average (MA)
• ARIMA model
• ARMA model

What are the Benefits of Predictive Modelling? 

A company that knows what to expect depending upon past patterns usually receives benefits in managing marketing campaigns, workforce, inventories, and a few other operational aspects.

Some IBM benefits are listed below.

• Improved decision-making: Effective business management requires informed decision-making. When analyzing growth or adding new products to their line, business owners must weigh the potential outcome against the inherent risks. Using predictive analytics tools, businesses can gain valuable insights that can aid in the decision-making process and offer a competitive edge. 

• Scalability: Automate data engineering and data science tasks. Test, train, and deploy techniques seamlessly around several company applications. Expand fundamental data science abilities across multi-cloud and hybrid environments.  

• Simplicity: It helps to manage the entire data science lifecycle. Standardize deployment and development processes. Design a specific model for security and data governance across the company.  

• Security: Every company should consider maintaining data security. Predictive analytics along with automation enhances security. Specific patterns linked with unusual end-user behavior can trigger security mechanisms.

IBM Cognos Predictive Analytics Solutions  

• Data science platform: IBM Watson® Studio assist in operationalizing AI by offering tools to prepare data and design models at will using visual modeling and open-source code.  

• Statistical analysis software: IBM® SPSS® Statistics is purposed to resolve research and business concerns through geospatial analysis, ad hoc analysis, predictive analysis, and hypothesis testing.  
  
• Visual modeling tool: The IBM SPSS Modeler assists you to tap into modern applications and data assets through complete algorithms and models which are prepared for prompt application.  
  
• Decision optimization solutions: IBM decision optimization solutions optimize events by providing prescriptive analytics features to augment predictive insights by machine learning models.  

Predictive Analytics Use Cases  

Predictive analytics techniques are used across several industries for solving different business-related concerns. Discussed below are a few areas where predictive analytics can be applied: 

• Banking: Financial companies use quantitative tools and machine learning to detect fraud and predict credit risk. A company that excels in fixed-income asset-management activities can use predictive analytics to understand dynamic market transformation along with static market restrictions. This application of predictive analytics enables customized services for customers and reduces risk.  
  
• Healthcare: It is used to identify and handle the care of chronically ill patients, and to track infections like sepsis. This model provides impressive outcomes such as the analyzing of a high rate of survival.  
  
• Human resources: The HR department uses this model to match prospective job applicants, increase employee engagement, and reduce employee turnover. The integration of qualitative and quantitative data enables companies to decrease their cost of recruitment and enhance employee satisfaction, which is specifically useful when label markets are volatile.  
  
• Supply chain: Predictive analytics help companies to make inventory management more efficient, supporting them to meet demand while reducing stock. Also, allowing businesses to analyze the cost and return on their product over time.  
  
• Marketing and sales: The marketing and sales team are known to Cognos BI tools to comprehend historical sales performance; predictive analytics allows businesses to be more active in a manner that can help them engage with their customers around the user journey. The marketing department can use predictive analysis for cross-selling techniques, and this majorly supports itself via a suggesting engine on a brand’s website. 

Conclusion 

IBM Predictive Analytics helps your company with data-driven actions in aspects critical to revenue and profitability. It will help you to identify risks, seize new opportunities, and implement effective strategies. Companies using Predictive Analytics can see future events and stay ahead of market competition. 

WRITTEN BY

Team Eela

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