Top 10 Business Intelligence Trends to Look Out for in 2023

Business intelligence (BI) combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations make more data-driven decisions. Hopefully, these BI trends will help prepare your company to meet a more hyper-scale, connected, and data-driven global market in the next few years. The BI landscape is evolving and the future of business intelligence is played now, with emerging trends to keep an eye on. Read on to see our top 10 business intelligence trends for 2023!

Artificial Intelligence: Artificial intelligence (AI) is the science aiming to make machines execute what is usually done by complex human intelligence. AI and machine learning are revolutionizing the way we interact with our analytics and data management. This concept is known as ethical AI and it aims to ensure that organizations use AI systems in a way that will not break the law.

Data Security: In the business intelligence ecosystem, we will see more defensive AI developments in the form of security. Already, we see the continuous development in proactive analytics lending to BI advanced neural networks that detect system anomalies before any issues happen.

Natural Language Processing (NLP): NLP bridges the gap between computers and humans by eliminating the need for any programming language. By integrating this capability with voice-activated digital assistants on mobile devices, software vendors make data discovery even more user-friendly. It outputs the essential takeaways of a data visualization in conversational language, facilitating quick insight interpretation.

Analytics-as-a-Service (AaaS): AaaS provides businesses with end-to-end big data analytics, from data collection to cleaning, organizing, and processing huge and disparate datasets through the internet and tailored fit to a business specification. Meanwhile, more companies are expected to rely on the AaaS business model if its predecessor, Software-as-a-Service, is of any indication, where users pay only when they use the service.

Data Literacy: Data literacy is key to increasing user adoption and maximizing the effectiveness of BI tools. Data literacy is important for all individuals, irrespective of their work profile and businesses. Data-driven business owners have to eliminate the data literacy gap between data analysts and non-technical users.

Data Visualization: Data visualization allows businesses to keep every relevant stakeholder engaged with the data by empowering them to analyze and manipulate the information intuitively and extract actionable insights. It requires understanding the relationship between data in the form of data preparation and guided advanced analytics.

Real-time Data & Analytics: Real-time access to data has become a norm in everyday life, not just for businesses. Moreover, implementing live dashboards will help companies to immediately access relevant information regarding their business and react if any potential issues arise.

Data Quality Management: With so much information being produced every second, using quality data when performing analysis has become a critical element. Essentially, data quality management ensures that companies can make the right data-driven decisions by using the correct data for analytical purposes.

Data Governance: Data governance ensures the quality of business assets through role-based access, authentication protocols, and auditing. When data is accurate, unique, and up-to-date, users trust the insights are reliable, boosting revenue and reputation.

Data Automation: Business intelligence topics wouldn’t be complete without data (analysis) automation. This new trend refers to the action in which businesses automate as many processes as possible by using multiple tools and technologies such as AI, machine learning, low-code, and no-code tools, among others.