In today’s fast-paced business world, companies must stay competitive by staying ahead of the curve. Business intelligence (BI) is a vital tool organizations use to make data-driven decisions that can improve their bottom line.
Business Intelligence Key Trends for 2024
Here are the important BI trends for 2024.
1. Augmented Analytics
“Augmented analytics” refers to incorporating AI and ML algorithms into business intelligence tools. This integration enables users to automate tasks like data preparation, analysis, and visualization, allowing them to focus on extracting insights from the data. Augmented analytics tools use natural language processing (NLP) and other AI technologies to simplify complex data queries and provide real-time actionable insights. By incorporating augmented analytics into BI tools, organizations can make faster, data-driven decisions and enhance their overall performance.
Example: Salesforce Einstein Analytics is an augmented analytics tool that provides businesses with real-time insights. It uses ML algorithms to help users identify patterns and trends in their data, enabling them to make informed decisions quickly.
2. Embedded BI
Embedded BI refers to integrating BI tools into other applications, such as CRM or ERP systems. By embedding BI tools into these systems, organizations can provide their employees easy access to real-time data insights, reducing the need to switch between multiple applications. Embedded BI is becoming increasingly popular as it eliminates the need for users to learn new software and reduces the risk of data silos.
Example: Tableau Embedded Analytics allows organizations to integrate BI tools into their applications and products, providing users with real-time insights from within the applications they use most frequently.
3. Collaborative BI
Collaborative BI involves sharing insights and data between different departments within an organization. Encouraging collaboration and knowledge sharing within a business can provide a more comprehensive understanding of its operations, resulting in improved decision-making. Collaborative BI tools enable teams to work together in real time, share insights, and identify patterns and trends that may have been missed otherwise.
Example: Microsoft Power BI is a collaborative BI tool that allows users to share insights and collaborate on real-time data analysis. It also provides a shared workspace where users can collaborate and share information.
4. Data Storytelling
Data storytelling is a technique used to convey complex data insights engagingly. It involves using data visualization tools to create visualizations and narratives that help users understand the data better. By presenting data compellingly and engagingly, businesses can increase user adoption of BI tools and improve the effectiveness of their data-driven decisions.
Example: Qlik Sense is a data storytelling tool that allows users to create compelling visualizations and narratives that help them understand complex data insights.
5. Cloud-Based BI
Cloud-based BI refers to using cloud computing to host and deliver BI applications. This approach offers several benefits, including increased scalability, flexibility, and accessibility. Cloud-based BI saves businesses from spending money on expensive hardware and software infrastructure, reducing ownership costs.
Example: Amazon Quick Sight is a cloud-based BI tool that allows businesses to create interactive visualizations and dashboards in minutes. It is fully managed by Amazon Web Services (AWS), providing users with the scalability and flexibility they need to meet their changing business needs.
6. Mobile BI
Mobile BI involves accessing and analyzing data insights through mobile devices. Businesses can enhance productivity and expedite decision-making by equipping employees with real-time data insights accessible through mobile devices. Mobile BI allows users to access crucial data from any location without being physically present in the office. This feature enhances flexibility and convenience for remote working. One example is SAP Analytics Cloud, a mobile BI tool that offers real-time data insights on mobile devices. This tool allows users to work from any location while reducing the necessity of accessing critical data from the office.
7. Natural Language Processing (NLP)
Using AI algorithms to analyze and interpret natural language lets users interact with data insights through voice and text-based commands.
Example: Yellowfin Signals utilizes NLP techniques to allow users to ask questions about their data using natural language. This provides immediate answers and insights.
8. Data Governance
Implementing policies, procedures, and technologies to ensure data accuracy, completeness, and consistency while also ensuring compliance with regulatory requirements.
Example: Informatica Data Governance provides businesses with a comprehensive set of tools and policies to manage their data assets effectively, enabling organizations to comply with data privacy and security regulations.
9. Predictive Analytics
We use statistical models and ML algorithms to analyze past data to predict future trends and outcomes.
Example: IBM Cognos Analytics, which provides businesses with the tools to create predictive models, allowing them to anticipate future trends and behaviors and make informed decisions accordingly.
10. Data Democratization
Allowing all employees, regardless of their technical abilities or job responsibilities, to access data can promote fairness in decision-making and give them the confidence to make informed decisions.
Example: Domo, which provides businesses with a self-service BI platform that allows employees to access and analyze data insights, regardless of their technical expertise, enabling them to make data-driven decisions.
Conclusion
In conclusion, business intelligence is an ever-evolving field constantly adapting to new technologies and business needs. As we move into 2024, we expect to see increased adoption of the following:
- Augmented analytics
- Embedded BI
- Collaborative BI
- Data storytelling
- Cloud-based BI
- Mobile BI
- Natural language processing
- Data governance
- Predictive analytics
- Data democratization.