Integrating Business Intelligence with Data Analytics for Better Insights

In today’s tech-savvy world, businesses utilize data analytics, but many lack access to business intelligence (BI) technologies that would enable them to fully harness their data. Integrating business intelligence with data analytics helps businesses gain a competitive edge.

BI solutions are effective because they leverage the valuable insights provided by data analytics. However, BI technologies translate this data into a form that businesses of all sizes can understand and act on. BI provides all the tools and methods necessary to analyze business information, helping companies manage their operations in the past, present, and future. Data analytics assists in navigating crucial business data. This article will explain how companies can maximize the combination of data analytics and business intelligence.

What Is Business Intelligence?

Business intelligence (BI) is the result of combining various technological and strategic approaches. Integrating Business Intelligence helps companies gather, evaluate, and interpret data, enabling them to make better and more informed decisions.

What Is Data Analytics?

Data analytics refers to the process of analyzing data to identify trends and patterns, making predictions easier. It helps businesses grow, improve decision-making, and enhance processes. Data analytics encompasses various activities, including data analysis, data science, and data engineering.

How Do Data Analysis and BI Work Together?

Data analytics and BI focus on various aspects of the decision-making process, and they have complementary organizational roles.

• Integration: Through integrating business intelligence with data analysis capabilities, users can perform analysis on the same platform they use for visualization and reporting. The integration will narrow the gap between decision-making and technical data analysis.
• Foundation and Application: Data analysis generates insights through rigorous methods from the data. BI helps in the application of those insights for the organization’s improvement in strategic planning and operational processes.
• Scope of Use: An organization can apply data analysis in different contexts, such as policy development and scientific research, whereas BI may focus on improving business performance and operations.
• Tools and Techniques: Data analytics and BI use the same tools and techniques for data analysis, visualization, and manipulation. However, BI platform uses these tools by emphasizing dash boarding, interactive analysis, and reporting for non-technical users also.
• End Goal: The fundamental objective of data analytics is to extract knowledge and patterns from data while the BI aims to make use of this knowledge to drive business value, provide valuable insights, and support better decision-making at all organizational levels.

Data Integration Approaches In BI

Data integration approaches in BI are important for consolidating data from different sources to get valuable insights. Some common approaches are as follows:

• ETL (Extract, Transform, Load): One traditional approach involves extracting data from various sources, transforming it into a suitable format, and finally loading it into a data warehouse for analysis. This entire process is automated and uses data maps to facilitate completion. The data maps enable users to automate data flow efficiently.
• ELT (Extract, Load, Transform): In the ELT (Extract, Load, Transform) approach, data extraction is followed by loading the data into a data warehouse, after which the transformation occurs. ELT, also known as the pushdown optimization approach, is well-suited for modern data architectures. It is faster and ideal for data streaming rather than traditional batch processing.
• Data Virtualization: This approach is known for providing a unified data view from various sources without physically consolidating them. Data visualization enables real-time access, reducing the need for data replication. It is popular because it minimizes or eliminates the requirement for physical data movement.
• Real-Time Data Integration: This approach integrates data in near real-time or real-time, enabling immediate analysis and decision-making. It is commonly used in high-speed environments where timely information display is critical.

Each data integration approach has its own strengths and vulnerabilities, which may be selected based on the organization’s requirements, including speed, complexity, and data volume.

How Can BI Tools Make Data More Understandable and Actionable?

Through the packaging functionality of data analytics in an easy-to-use interface, non-technical employees can easily understand data insights. Here are some ways in which BI tools can help make data more understandable:

• Data Visualization in an Easy-To-Understand Format

One of the most challenging ways of data analytics is that all information that it produces should make sense. BI tools can make this easier by displaying data in a visual format, such as graphs, maps, and charts. 43% of respondents in a survey reported a high satisfaction with the improved data quality that accompanies BI solutions. The visual format gives a better understanding of the trends and patterns which may be hidden otherwise.

• Data Presentation In a User-friendly Context

Data analytics produces vast amounts of data, which may also have irrelevant information. By using BI tools, the data presentation can be done in such a way that the user understands it completely. A Bit tool can help to show sales data by using customer journey or marketing data by using buyer’s journey.

• Enhancing Security

Data security is one of the paramount considerations for any business that gathers and stores sensitive data. BI tools offer features that enhance data security, such as data encryption and user access controls. These features increase the value of both data analytics and BI for businesses of all sizes.

Benefits of Integrating Data Analytics for Business Intelligence

Data integration business intelligence provides various advantages that can fundamentally enhance operational efficiency and decision-making. Some advantages of integrating BI with data analytics are as follows:

• Customer Insights: By analyzing customer data, businesses gain insights into behaviors and preferences, leading to improved customer retention and satisfaction. According to a survey, sales planning and forecasts were the top BI solution areas (57%).
• Risk Management: Predictive analytics can identify risks and develop mitigation strategies to safeguard business interests.
• Competitive Advantage: By leveraging actionable insights from data, companies can gain an edge over competitors by innovating and anticipating market transformations.
• Improved Decision-Making: Data analytics provides valuable insights by identifying correlations, patterns, and trends, helping businesses make informed decisions.
• Operational Efficiency: Data analytics streamlines processes and optimizes resource allocation, increasing productivity for businesses.

Conclusion

Data-driven decision-making is essential for the success of any business. For this reason, integrating business intelligence and data analytics is crucial for obtaining better insights. The combination of both provides a competitive advantage and helps elevate your organization’s analytics.

Imagine IT is a leading organization with experienced IT consultants who specialize in delivering various data integration approaches in BI. They are also known for providing top-notch services such as cybersecurity, IT support, Microsoft 365, and co-managed IT services. To receive outstanding data integration services, contact us by calling 620-278-3600.