Data Analytics & Business Analytics

Table of Contents

1. Introduction to Data Analytics

2. Introduction Business Analytics

3. Data Analytics Tools

4. Business Analytics Techniques

5. Data Analysis vs Business Analysis

1. Introduction to Data Analytics

Data Analytics is nothing but analyzing raw data and make conclusions based on those information.

Data Analytics can be approached in several ways

• Predictive Data Analytics

This is the most common approach of Data Analytics followed by businesses. Businesses identify what is going to happen in future using trends, correlations. Predictive Data Analyzing can be of two types - predictive modeling and statistical modeling.

• Prescriptive Data Analytics

This approach helps to predict outcomes and identify what should be done next. Data is pulled and roadmap is produced to identify what to do. For this, Machine Learning and Artificial Intelligence are combined to simulate various approaches for the expected outcome

• Diagnostic Data Analytics

As the name suggests, in this approach, businesses analyze data from the past. What was done is identified by examining the data, employing techniques like drill down, data mining, data discovery, and correlations.

• Descriptive Data Analytics

This is the most straight approach of Data Analytics. Data is parsed(broken down) and main features and characteristics of data is summarized. The findings are presented through reports, pivot tables, and visualizations like pie charts, histograms, line graphs, and box and whisker plots

2. Introduction to Business Analytics

Business Analytics is nothing but using data of past business performances to make better business decisions that helps in driving better business planning. Data is collected, processed, analyzed and presented to predict the trend, which in turn improves business performance and business strategy. Business Analytics helps businesses in many ways like solving problems, identifying opportunities, optimizing processes, increasing revenue, and improving effeciency.

Business Analytics can also be approached in four different ways

• Descriptive Business Analytics

In this approach, historical data is interpreted to identify how business trend and pattern will be

• Diagnostice Business Analytics

This approach focusses in identifying root cause of any business problem

• Predictive Business Analytics

This technique concentrates in using the data to predict/forecast future business outcomes

• Prescriptive Business Analytics

This approach uses various techniques to determine what action should be taken based on descriptive and

predictive analysis

3. Data Analytics Tools

• Power BI

Power BI is Microsoft's Data Analytics tool which is widely used. Started off as a straight forward DA tool, Power BI has evloved and catered advanced business needs. Now Power BI has Machine Learning(ML) capabilities. In addition to that, Power BI converts insight to action with the help of Microsoft Power Platforms. Power BI is available as a free desktop for individuals to learn the tool.

• Tableau

Tableau is a direct competitor for Power BI. It helps in providing data-driven business forecasts, strategies, and decisions. Tableau is better than Power BI as it supports vast data sets. Also visualizations can be customized without writing codes. Another advantage is that Tableau dashboards can be shared to people who do not habe Tableau. Tableau is free to use for individuals.

• QlikSense

QlikSense is another Data Analyzing tool which uses Machine Learning(ML) which helps users to understand data and use it effectively. One of its features is Augmented Analytics which suggests insights automatically. There is no free version of Qlik Sense but a free trial period is available for users to get started.

• ThoughtSpot

ThoughtSpot is not like other Data Analytics tools, as it understands and produces results by using natural language. We have to ask questions in natural language to get visualization of data. ThoughSpot is effective in analyzing data with complex questions even with huge data. ThoughtSpot is priced based on usage which makes it easier for organizations to pay. This makes ThoughtSpot one of the best Data Analytics tools.

• Oracle Analytics Cloud

Oracle Analytics Cloud uses latest data analytics technologies like Artificial Intelligence(AI), Machine Learning(ML), Natural Language Processing(NLP) to help business users, data engineers and data scientists to connect, prepare and enrich relevant data to evaluate predictions and make accurate quick decisions. OAC has several pricing models starting with a 30-day free trial.

4. Business Analytics Techniques

• Brainstorming

Brainstorming is one of the Business Analytics Techniques in which the company leaders come together to evaluate business needs, in order to prevent potential issues or developing solutions for existing issues.

• SWOT Analysis

SWOT is the acronym for Strengths, Weaknesses, Opportunities and Threats. In this technique, all these four factors are examined in relation to a business. This helps business in understanding how to improve their business with business analysis by identifying the threats.

• MOST Analysis

MOST is the acronym for Mission, Objective, Strategy and Tactics. As the name suggests, business should think about their mission, objectives as well as how to build strategies to meet business needs and preventing potential issues. This technique is used by businesses to align their operations with mission and values.

• Business Process Modeling

Business Process Modeling is a BA technique which helps organizations in visualizing how the business works and how to improve it. It uses text, symbols, diagrams to visualize roles, steps, and events in a process. Different techniques used are flowcharts, scripts, BPMN, UML which helps business in optimizing, automating, and monitoring workflows and processes.

• Use Case Modeling

Use Case Modeling is a technique that defines the points of operation and user interactions in a business. Use case diagrams are created with actors, use cases, set of relationships, and system boundary. User interface design can be added later for programmer's reference. Businesses use this technique

• User Stories

User stories technique focusses on aligning business needs and challenges based on user expectations and interactions. They are written based on user perspective and they are used to organize the direction of a development project.

5. Data Analytics vs Business Analytics

Data Analytics and Business Analytics both deal with data. However Data Analytics is about collection of data, organizing the data, and analyzing large data to predict, find insights and trends with the help of mathematical and programming skills. Whereas Business Analytics also deals with data, but to understand business perrformance, how to effectively do business and recommends actions and initiatives. Data Analytics is focussed on the backend, while Business Analytics is at the front of the data pipeline .