Introduction
In the digital age, big data continues to be in the spotlight. And why wouldn’t it be? Data has the power to push any organization in the right direction. Through data, organizations learn to resolve main issues and address the future impact. Business Analytics
One extensive research highlights that by 2025, data generation will reach 463 exabytes per day on a global scale. In the grand scheme of things, perspective matters and this is where the difference between business analytics and data analytics becomes clear.
Keeping that in mind, let’s dive into the basic differences between business analytics and data analytics:
Business Analytics and Data Analytics
You can view big data through the lens of its two sub-fields famous as business analytics and data analytics. But oftentimes, the thin red line between business analytics and data analytics is blurred. Technically, business analytics and data analytics are part of big data and are used interchangeably.
Data analytics, in a typical sense, refers to data collection and manipulation to unearth valuable insights that organizations can use in order to make logical business decisions. And one of the main responsibilities of data analysts is to manage multiple data sources and related data storage.
Data storage can include databases, data warehouses, and data lakes that make it possible to conduct exploratory analysis. A closer look will help you realize that data analytics is not so different from other disciplines like applied statistics, machine learning, and data science.
On the other hand, business analytics is all about designing a solution that optimizes business processes through statistical analysis, predictive modeling, and data analytics. Today, data scientists perform complex data analytics. And business analytics works as a domain to the management team as it involves enterprise-level decision-making.
Now, let’s dive deeper into the differences between business analytics and data analytics:
Data Management
When it comes to data management, data analytics models the workflow in order to manipulate different data sets and determine data patterns and trends. This ultimately becomes highly informative to make intelligent and uniform business decisions.
Conversely, business analytics is about conducting a comparative analysis of structured data via data analytics. The idea is to benchmark this data and take a closer look at a business’ progress and how it can improve certain areas to drive growth. This data makes it easier to fine-tune business strategy and make future plans through predictive modeling.
Data Modeling
Data modeling allows organizations to check future insights and get the answers to specific questions. With advanced data analytics tools, data modeling has become a dynamic process. Data scientists take advantage of data models to predict trends as well as make adjustments in the models to find out the future direction of the business.
Whether it’s data analytics or business analytics, data modeling works in favor of both and helps data scientists predict future outcomes and data trends by analyzing essential KPI metrics. It takes a specific selection of methods, attributes, and variables to build a robust data model. Data modeling also involves data manipulation tactics such as data classification, regression, and clustering.
Data Quality
Data analytics extracts information that requires continuous interpretation. In most cases, this data comes with a lot of theoretical possibilities and statistical probabilities. But data analytics does not provide a business solution or consolidate insights. And this is where business analytics steps in to analyze presented data and look for innovative business solutions.
Data Focus
The focus of data analytics and business analytics is different but often overlaps. Data analytics revolves around collecting and manipulating business data through program code and SQL querying to showcase undiscovered operational correlations.
Business analytics starts where data analytics ends. It focuses on establishing key data patterns and trends and their correlation with BI information to get meaningful insights. Think of business analytics as a superset of advanced data analytics. In fact, business analytics makes the most out of data analytics to forecast through regression, neural networks, analysis, and decision trees.
Final Thoughts
In 2022, there is a multitude of data available to organizations that require strategic analysis to cater to the specific needs of the target audience. At this point in history, data has become the Holy Grail for organizations to innovate, retain customers, drive unprecedented growth, and maintain a competitive edge in the market.
As vast data generation becomes a norm, there is more need for bright and talented data analysts. It is imperative to realize and recognize that there is more than one type of analytics and there are unique platforms on the market that can offer one or a blend of type of analytic visualizations. But whether it’s data analytics, predictive analytics, diagnostic analytics, web analytics, business analytics, or prescriptive analytics, it’s all about pushing the boundaries that help industries evolve.
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Business Analytics
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- https://www.talend.com/resources/business-analytics-vs-data-analytics/
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- https://datasciencedegree.wisconsin.edu/blog/business-analyst-vs-data-analyst/
- https://www.selecthub.com/business-analytics/web-analytics-vs-business-analytics/
- https://www.educba.com/data-analytics-vs-business-analytics/
- https://www.simplilearn.com/business-analytics-vs-data-analytics-article
- https://www.analyticsfordecisions.com/data-analytics-vs-business-analytics/
