Analytics has become a driving force behind business development and transformation. It helps provide organizations with the capabilities required to develop new and creative strategies that can improve customer experience, enable growth opportunities, and even provide new revenue streams. So, is data analytics and business analytics same?

Both data and business analytics help organizations make data-driven decisions. Oftentimes, the terms are used interchangeably despite the distinctive differences. Without further ado, let’s understand the difference between business analytics and data analytics.

What is Data Analytics?

Data analytics can be defined as the process of analyzing data and categorizing them. Data can be categorized by sorting, storing, cleansing, and identifying patterns, as well as interpreting insights using statistical techniques, big data processing, and technology. 

The goal of data analytics is to answer specific questions, discover new insights, and help companies make better, data-driven decisions. It is performed by using sophisticated tools like Python and Tableau.

One of the most popular forms of data analytics in the present time is machine learning. In ML, massive volumes of data are processed which uncovers patterns within the data to make intelligent predictions and produce insightful information. This can help in solving a particular business question or solve a specific business problem.

The insights gained from analyzing data can lead to decisions and actions that positively impact an organization’s performance. This is because you are relying on credible datasets to make decisions rather than relying on your gut instinct. 

Hence, engaging in data analytics enhances organizational or operational efficiencies and enables the formulation of strategies to capitalize on emerging business prospects.

Types of Data Analytics 

Data analytics can be typically categorized into four types:

  • Descriptive Analytics 

Descriptive analytics focuses on answering the “what.” It helps you to identify why something happened or is happening in your business. This form of analytics is the most common and summarizes and analyzes the historical trends in key performance indicators or KPI dashboards, data visualization, or reports. 

By using descriptive analytics, you can compare data between two specific periods for given metrics or KPIs. For example, you can use this analytics to measure month-over-month subscriber growth or year-over-year revenue growth. 

  • Diagnostic Analytics 

Diagnostic analytics focus on answering the “why.” It helps you understand the reason behind why a certain event or action has occurred. This in-depth approach requires you to develop hypotheses to understand the reason that may have caused the issue. This approach also includes exploring data to find patterns and relationships. 

For instance, a marketing manager may want to find out why the sales campaigns developed by their team are not performing well in a certain market. 

  • Predictive Analytics 

Predictive analytics focuses on answering “what will happen.” This type can be identified as an advanced data analytics approach that uses statistical models to identify patterns in data. This helps in forecasting trends or projecting the probability of the outcomes based on current and/or historical data. 

In the world of business, predictive analytics proves to be significantly useful in sales forecasting, fraud detection, and credit risk assessment, among others.

  • Prescriptive Analytics 
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Prescriptive analytics focuses on answering “what should we do.” The goal of this approach is to identify ways on what could be done to improve certain aspects of a business. The optimal course of action is to assess possible scenarios for a business outcome. 

This process requires the expertise of data scientists working with advanced modeling techniques. They use artificial intelligence (AI) and machine learning (ML) to make the information more accessible. 

What is Business Analytics?

Business analytics is a subdivision of business intelligence. It focuses on the big picture of how data can be utilized to improve weak areas in an existing strategy or procedure in a business. It also helps in adding value or optimizing costs in specific business processes. 

This analytics process uses various reporting or financial analysis tools, data visualization tools, and even data mining to improve specific business functions like sales and marketing.

A common example where business analytics is used is during the planning of sales strategies for an upcoming season. Here, business analytics is used to predict product demand so that stock can be optimized accordingly so that certain business goals can be met. Therefore, business analytics aims at creating solutions and solving existing challenges that are unique to a business. 

Business analytics usually stays at the forefront of the data pipeline as compared to data analytics which is more focused on the backend. Successful use of this analytics helps in obtaining data-driven insights to support business decision-making processes and implement practical changes throughout the organization.

Data Analytics vs Business Analytics

Now that we have understood the critical difference between data and business analytics, let’s understand the role of data and business analysts:

Role of Data Analyst

A data analyst gathers, cleans, analyzes, visualizes as well as presents existing data to help in making better business decisions. An effective data analyst uses data to answer questions and empower decision-makers to plot the best course of action. 

One of the most critical roles of a data analyst is to identify new sources of useful data. They also engage their time in understanding what questions and solutions business leaders are looking for. They gather useful data to get to the right answers.

Role of Business Analyst

A business analyst helps in identifying problems, opportunities, and solutions for their organizations. They form the link between the world of IT and business. They are responsible for planning and communicating goals and strategies to the relevant stakeholders in their organization. 

Also known as problem solvers, business analysts approach different situations and challenges by looking at them from different perspectives. Their ultimate goal is to develop solutions using the data gathered. 

Here are the different responsibilities of data and business analysts:

Responsibilities of Data Analyst Responsibilities of Business Analyst
Work with business leaders and stakeholders to define a problem or business need Evaluate a company’s current functions and IT structures
Identify and source data Review processes and interview team members to identify areas of improvement
Clean and prepare data for analysis Identifies and defines specific business requirements 
Analyze data to identify patterns and trends Present findings and recommend relevant measures to management and other key stakeholders
Visualize data so that it is easier to understand Create visuals and financial models to support business decisions
Present data in a compelling and storytelling format Train and coach staff in new business systems
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Process of Data Analytics

The data analytics and business analytics differences can also be understood in their different processes. The process of data analytics starts with a business objective in mind followed by the collection of data using modern data analytics tools which also help in integrating, managing, and its analysis followed by the sharing of any findings. 

The two major steps in the data analytics process are:

Before the process of data analysis starts, raw data needs to be transformed into clean and business-ready information. This is done through data integration where data from multiple sources are brought together to provide complete, accurate, and updated information. 

The first step includes data replication where data is copied from the master sources to one or more locations. This process helps in faster data access and forms a data backup. After this, data ingestion takes place where data is moved from a variety of sources to a storage location such as a data lake or a data warehouse.

Data ingestion can be streamlined in batches or real-time like the previous process. Here, data is also cleaned and standardized, making it ready to be used in data analytics tools. Lastly, data management is upheld through proper governance. 

A governed data catalog profiles and documents every source of data. It helps in defining who in an organization can take which action on which data. These policies and standards allow users to easily find, prepare, use, and share trusted datasets on their own, without relying on IT.

  • Data Analysis

The second step in the data analytics process is data analysis. After raw data has been transformed into accessible information, the analysis phase begins. Here, the goal is to answer specific questions along with discovering new questions and unanticipated insights by exploring the data. Through data analysis, analysts identify meaningful patterns, relations, and trends using a  variety of tools and processes. 

The foremost process is self-service analytics which enables users to easily analyze data without the need to write code. Here, a platform using an associative engine allows users to explore data freely in any direction, recalculate analytics, and highlight data relationships with every click. 

Augmented analytics is another process that uses AI and ML to enhance human intuition with suggested insights and analyses, automation of tasks, search, and natural language interaction, along with advanced analytics calculations. Conversational analytics is powered by AI and lets users use natural language to text, chat, and use voice within analytics apps to explore data and discover useful insights. 

You can perform thorough data analysis using data visualization and dashboards. By using graphs, charts, and maps, you can present data in an easier format. Dashboards help it easy for data exploration. Another method is reporting and alerting through which stakeholders can remain informed. 

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Embedded analytics are also useful as they help different stakeholders quickly access data and insights in the workflows, thereby impacting swift decision-making. Lastly, mobile analytics allows users to share their analysis and collaborate with other stakeholders on any device.  

Process of Business Analytics

The popularity of business analytics as a tool is because it can be applied to any industry where data is captured and accessible. The data can be used to improve the organization’s capability to improve their service and gain insights into online and digital information. 

The seven stages in the process of business analytics are:

  • Define Business Needs

In the first stage, the requirement of the business is identified. For instance, it can be an improvement on an existing goal or solving a business problem. Here, the goal is broken down into smaller goals and analysts identify available data, and how to use it.

  • Explore the Data

This stage focuses on cleaning the data, identifying missing data, removing duplicate data, and transforming combinations of variables to form new variables. After data cleaning, analysts try to make sense of the data by using different techniques like scatter plots. They also look for general patterns and actionable insights that can be analyzed to achieve the business goal. 

  • Analyze the Data

Statistical analysis methods such as correlation analysis and hypothesis testing are used to identify the factors related to the target variable. In this stage, data is cut, sliced, and decided for different comparisons and to gain adequate insights. 

  • Prediction 

Business analysts model the data using predictive techniques such as decision trees or logistic regression, among others. These techniques help in uncovering insights patterns, relationships, and hidden evidence of the most influential variables. 

The analysis also compares the predictive value with the actual value and selects the best-performing model based on accuracy and outcomes. 

  • Optimize and Solution Assessment

One of the most critical stages in the process of business analytics is where different scenarios and outcomes are considered. Based on these, the most ideal solution is determined based on the model with the lowest error which is meeting management targets and the organization’s strategic goal. 

  • Decision-Making and Outcome Evaluation

The analyst decides in this stage and takes action based on the derived insights. After an appropriate period after the action is implemented, the outcome of the action is measured.

  • Update System as per Result of the Decision 

In the final stage, the outcome of the decision and action along with the new insights derived from the model are recorded and updated in the database. This process helps in identifying the effectiveness of the decision and action and understanding the return on investment. The result helps in the process of future decision-making and the identification of new insights. 

Conclusion 

Thus, business analytics and data analytics differences can be identified with respect to the different stages along with the roles and responsibilities performed by respective analysts. Both processes are crucial for business and can help in identifying insightful information that can improve business performance and decision-making.