search
top

Data Analytics and Predictive Analytics: A Comparison

Data analytics offers tools and techniques to produce outcomes to enhance business productivity and reduce risk.

 
 
 
 

Business organizations today are working in the midst of competitors dealing with uncertain market conditions and generating huge amount of data daily. This is a peculiar characteristic of data that it speaks a lot about the behavior of business. The data can be related to customers, visitors, business users, employees and stakeholders etc. It may be about consumer buying patterns, visitors showing interest in their business, possible leads, business activities pointing to growth or loss of the business, etc. This data, if churned properly, can help business people to find and analyze numerous interesting patterns. Subsequently, the capability to draw conclusions from data can drive them in right direction.

 
Data analytics plays an instrumental role in solving routine business problems. Data analytics tools can transform the way a business operates and deals with business obstacles. These tools have the ability to deal with all problems ranging in complexity thereby increasing efficiency of business processes. Data analytics provides valuable insights into business problems by converting threats into opportunities and weaknesses into vital strengths.

 
Many business people rely on predictive analytics models to understand the patterns in variety of transactional data for forecasting the future events. On the other hand, data analytics helps business to take well-informed and accurate decisions at right time. Let’s dive into the exploration of significant differences in similar looking terminologies Data Analytics and Predictive Analytics:

 

Data Analytics Predictive Analytics
It is a quantitative and qualitative technique to perform data inspection, cleaning and data transformation for drawing significant information from the dataset. It is a statistical modelling technique to understand behavior of current and historical trends detecting patterns in the dataset.
Its purpose is to guide in data driven decisions. Its purpose is to do future predictions.
Data is collected and pre-processed to obtain transformed structured data to derive insights. Historical data is used to develop predictive model to predict future events.
The steps involved are:
• Define the project
• Data Exploration
• Data Preparation
• Data Analysis
• Data Evaluation
• Deriving Insights
The steps involved are:
• Identify Problem
• Data Exploration
• Data Preparation
• Data Transformation
• Model Building
• Model Validation
• Model Deployment
• Monitor Results
• Control
The expected outcome depends on customer requirements. The expectation is a reliable predictive model generated from data in hand.
The industrial applications range from those related to decision making, like early warning system, fraud detection, customer satisfaction etc. It can be applied by those industries which intend to predict future events like sales forecasting, risk assessment, customer churn prediction etc.
From the business viewpoint, it is useful to make businesses more informed about the real-time scenario to take right business decisions. From the business viewpoint, it is useful to make business future ready by uncovering relationships in data not easily visible.

 
Looking broadly, both data analytics and predictive analytics, can be used as decision-making tools. Data analytics involves handling and processing data through statistical techniques while predictive analytics, a subset of Data analytics, involves complex statistical algorithms to generate future predictions. Both the tools can guide business people to take appropriate decisions based on the conclusions drawn from the data, but the choice of analytics tools depends on their requirements.

3 Responses to “Data Analytics and Predictive Analytics: A Comparison”

  1. Grishma says:

    I’m new to Data analytics. I was looking for this difference since long. Thanks for the post.

  2. Kavita says:

    Hey!! This is really good.

  3. Priya Shishir says:

    Great information!! Keep it up.

Leave a Reply

Your email address will not be published. Required fields are marked *

top