Data is everywhere and is the epicenter of technological space. From tech giants to start-ups, data plays a pivotal role in making informed decisions. And in this data-driven world, two terms, Business Intelligence (BI) and Data Analytics are interchangeably used in this context. In this blog we will explore Business Intelligence vs Data Analytics.
BI and Data Analytics serve different functions within an organization, but they are related. BI provides a foundation for data analysis while Data Analytics applies various techniques to extract insights from the data.
Such Data-driven insights provide a broader view of business performance and immensely encourage businesses to drive growth in today’s competitive business environment. Now let’s dive deeper into BI and Data Analytics.
Business Intelligence (BI)
Initially called ‘Descriptive Analytics’ the term ‘Business Intelligence’ (BI) was first used in the mid 1860s and BI is a set of technological processes used for analyzing, gathering and managing raw and unprocessed data of an organization.
In the sense, BI examines the data to understand the market trends and drive insights. Data-driven insights help an organization to improve business strategic decisions and find new revenue and business opportunities besides spotting market gaps.
Benefits of Business Intelligence
BI assists in discovering issues, optimizing operations, increasing performance and identifying areas for future growth. Here a some of the benefits of BI at glance.
- Prevention of financial harm: BI can detect potential business problems and save them from financial harm.
- Build responsibility: Individuals who are at leadership level can make the most of BI by uncovering relevant areas of their roles. This will build individual responsibility.
- Performance Metrics: BI solutions that provide performance metrics and monitor progress of the organization’s goals.
- Make data accessible: BI software has user-friendly interfaces and role-based dashboards making data accessible to everyone including individuals from non-technical backgrounds.
Data Analytics
Data Analytics in simple words means analyzing large sets of data and creating frameworks needed to be stored for current and future needs of a business. The goal is to make accurate and dynamic forecasts and recommendations. There are four predominant types of Data Analytics, and they are:
- Predictive Data Analytics: This is the most used type in Data Analytics and used for identifying trends and correlations in the business.
- Prescriptive Data Analytics: It is a combination of artificial intelligence (AI) and big data to help predict outcomes and identify what actions to take.
- Diagnostic Data Analytics: It is a process of examining data to understand the cause and effect.
- Descriptive Data Analytics: It forms the spine of reporting and even BI cannot function with Descriptive Data Analytics.
Benefits of Data Analytics
Increase productivity:
Data Analytics can detect areas for waste reduction, better inventory control and supply chain optimization. This will increase the overall productivity of a business or organization.
Mitigate Risk:
Every business goes an extra mile and takes that risk. Data Analytics understand the risk factor and suggest preventive measures that would be immensely helpful for organizations to handle unwanted breakdowns.
Enhance marketing and sales
Data Analytics, capable of refining marketing strategies with data-driven insights and allocating resources in the most effective strategies to maximize investments.
Faster development and innovation
With customer feedback and emerging technologies, Data Analytics will speed up product development and encourage innovation, which is the need of the hour.
Business Intelligence vs Data Analytics
While data forms the backbone of Business Intelligence and Data Analytics, there are many differences. That draws us to Business Intelligence versus Data Analytics.
| Feature | Business Intelligence | Data Analytics |
| Techniques | Role-based dashboards, Reports | Statistical analysis, Machine Learning, Predictive Modeling |
| Focus | Historical data and monitoring business performance | Discovering insights in large complex data |
| Purpose | Support operations and strategic planning | Identify opportunities for business improvement |
| Time period | Strategic long-term | Tactical and short-term |
| Data type | Structured from database | Unstructured from resources |
| Goal | Improve business performance and efficiency | Drive better business decisions |
How does Business Intelligence work?
BI works by gathering data and presenting it in a meaningful way for businesses to consume and make strategic decisions. The process can be broken down into several steps and they are
Data Collections: BI tools do the data collection from various sources such as databases and social media platforms. This data is typically stored in data warehouses used for reporting analysis.
Data Integration: The collected data needs to be integrated with a single source of truth for validation. This ensures the accuracy and consistent of data.
Data Analysis: By analyzing the data, insights and trends can be uncovered by creating reports and dashboards using techniques such as Machine Learning (ML).
Data Presentation: Here the data is presented in the form of charts or graphs for business users to easily access and understand the data.
Actionable Insights: The presented data is nothing, but data-driven insights and they help in making actionable decisions such as product development, marketing campaigns and others.
How does Data Analytics work?
Data Analytics involves a series of steps to collect and interpret data. The process is a multi-step one, and they are:
Data Collections: Though the first step is like BI, here the data is collected from spreadsheets, customer transactions, website clicks, social media activity and others. The data is then stored and analyzed.
Data Cleansing: The data undergoes cleaning process to remove inconsistencies, and the techniques include removing duplicate entries and correcting errors.
Data Analysis: Using statistical and computing techniques such as Machine Learning, the data is analyzed, and insights are extracted.
Data Visualization: Post analysis, data is visualized into graphs and other visual aids making them easier to communicate and use for identifying patterns.
Data Interpretation: Here the data will be interpreted into data-driven insights for businesses to improve their operations and service.
Examples of Data Analytics
Since Data Analytics helps in optimizing operations, reducing costs and improving customer satisfaction, it can be used in sectors like retail, healthcare, manufacturing, logistics and finance.
In the retail industry, Data Analytics takes customer preference into consideration and optimizes campaigns, offers and pricing. This strategy might increase sales. While in healthcare, Data analytics tends to estimate the medical records of the patients and suggest medication. In healthcare, predictive (data) analytics method may be used.
Quality is paramount in the manufacturing sector and Data Analytics ensures industrial equipment is managed well using sensor data. Here too predictive analytics might be used to spot quality issues at early stages.
Conclusion
It’s not merely about Business Intelligence versus Data Analytics as both are essential tools to leverage the power of data for businesses. In practice, the specific needs tend the businesses to often use the combination of BI and Data Analytics to gain holistic view of their operations.
Understanding the differences between both is up to the businesses and they should use the right tools to extract maximum value from their data and achieve the goals.
