Top 10 Business Intelligence & Analytics Trends for 2024

Digital transformation of businesses expedited the adoption of business intelligence (BI) and data analytics technologies. The global business intelligence market is currently valued at $29.42 billion, and by 2030, it is expected to reach $54.27 billion with a CAGR of 9.1%. The increasing number of business intelligence tools customized for each sector encourages businesses to adopt these tools quickly.

Not surprisingly, organizations are unaware of using 97% of gathered business data. When data analytics is used, new insights can be uncovered from these data. The BI tools can consume these data and encourage innovative business transformation.

As new technologies evolve, we will explore the top business intelligence trends for 2024 in this blog. 

Significance of Business Intelligence 

Globally, the BI adoption rate is about 26%. Organizations increasingly adopt BI as the firm size increases. The key driver for using BI tools is establishing a data-driven culture. Many businesses face technology challenges in establishing data governance. Some of the key benefits that BI tools can offer are:

• Better data quality
• Valuable business insights
• Reduced business costs
• Increased revenue
• Faster decision making 
• Quicker analysis, reporting, and planning
• Improved customer satisfaction 
• Better employee satisfaction 

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The crucial step to implementing business intelligence is to work on data analytics. Structured and unstructured data from multiple data sources must be integrated into one platform. Data analytics begin with ETL – Extract, Transform, and Load, where data is gathered from multiple sources, standardized, and loaded to a single platform. When data is available, the BI tools can generate reports that can help in making the following types of business decisions:

• Tactical decisions – To improve day-to-day operations
• Strategic decisions – For significant expenditure
• Operational decisions – For managers to optimize daily and hourly processes
• Financial decisions – To track major KPIs, predict financial outcomes, monitor revenues and expenses, and uncover cost-savings
• Risk management decisions – To mitigate business risks, including operational, supply chain, compliance risks, etc. 
• Competitive decisions – To create business strategies to win over competitors 

Features of BI and Analytics Solutions

Data analytics and business intelligence solutions are essential to automate data operations. Even with digital transformation, over 88% of data employees use spreadsheets and spend 60% of their time on manual processes. E-commerce companies actively use BI tools to clean up and explore data for predictive modeling and report generation. Some of the features that BI and analytics solutions offer are:

Reporting

BI technology allows businesses to customize report generation. You can also set up a scheduling system to automatically generate and send reports. This is useful for monthly, quarterly, and annual performance analysis. Machine-readable style reports allow data exploration using rows, pivots, filters, etc. 

Advanced Analytics

Regression analysis of BI tools allows you to create and analyze links between dependent and independent variables. OLAP relations are useful in exploring data from multiple sources. For leaders, it makes it easy to compare performance over locations, customer sectors, time, product groups, etc. Self-service BI tools allow department managers to take over advanced analytics instead of relying on data science engineers.

Data Visualization

Spreadsheets that managers use lack visualization capabilities. BI tools allow you to explore data from multiple perspectives and angles with visualization. Graphs, bar charts, pie charts, etc, offer major facts and statistics in a visually appealing manner. Advanced BI tools also provide deep visualization customization useful in analyzing trends. 

Location Intelligence

BI tools include intelligence maps that can visualize location data in multiple ways. Real-time data analytics about the geographic locations of customers will benefit businesses to predict consumer behavior. They can upgrade their sales and marketing strategies accordingly. 

Customized Alerts

Many BI tools have built-in automation and alert features. These tools can send customized alerts to all stakeholders to ensure timely performance. It can help managers to stay on top of daily, weekly, and monthly reports. The teams can easily schedule meetings and appointments without delays. 

Data Management

Data analytics involve automating blending, cleansing, examining, and analyzing data for investigation. Advanced automation uncovers data characteristics that were not originally visible. Efficient data analytics will help in establishing data governance and management principles. 

Predictive Analysis

In the highly competitive business landscape, executives must stay on top of general and customer trends. To optimize stock and inventory management, supply chain professionals need demand forecasts. BI tools equip businesses with predictive analytics to predict demands, threats, and opportunities. It is also useful in risk mitigation and business growth decisions. 

Real-Time Mobile Data Access

As companies work with large data quantities, access to real-time data is critical. New-age customers are highly demanding, and they need quick and effective services. The BI tools can use historical data for predictive analytics. They can also leverage real-time data for automated tracking and monitoring of business services. 

Top Business Intelligence Trends 

Data analytics technology is continuously evolving, bringing new benefits for business intelligence. BI and analytics can dramatically change the business landscape, especially with ongoing global digital transformation. Some of the business intelligence trends to look forward to in 2024 are:

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1. AI & ML Integration 

Artificial intelligence (AI) and machine learning (ML) are some of the latest trends in business intelligence. Businesses will use advanced tools for forecasting, predictions, and decision-making. Automating analysis with advanced technologies will uncover patterns and predict trends. Businesses will be empowered to forecast the future from historical data. Amazon has already employed AI-powered algorithms to minimize product and customer gaps. The manufacturing industry increasingly uses AI predictive maintenance to derive more value from their assets. 

Adaptive AI systems are useful in creating decision-making frameworks that adapt to real-world changes. It will enable executives to make more informed decisions.  

2. Augmented Analytics

Augmented analytics looks at data from a new perspective. AI automation helps with data preparation and discovery. The insights from deep data analytics will improve data understanding and democratize BI by making it available and accessible for everyone. Popular BI tools like Power BI, Tableau, etc, already use augmented analytics with intuitive interfaces and smart visualizations. Businesses can get automated recommendations, reducing the time to make decisions. 

3. Real-Time Analytics

The increasing adoption of IoT has demanded the need for instant real-time insights. Timely updates will improve the coordination of business operations scattered across borders. Businesses can implement a dynamic monitoring system with real-time analytics to track progress and make instant quick decisions.

4. Data Governance and Security 

As data consumption and usage increases, enhancing data security and governance is important. Organizations look for CompTIA Data+ professionals who can help them make sense of gathered data. Without integrated data security, organizations can lose millions of dollars due to cyber threats and data breaches within a few minutes. A continuous threat exposure management (CTEM) approach will enable organizations to reduce security breaches by two-thirds. 

5. Natural Language Processing

Natural language processing (NLP), which is a subset of AI, is already in use everywhere. People talk to their smartphones and chat with Autobots to fulfill their needs. Organizations that expand their investment in NLP technologies can upgrade their business decisions from unstructured data sources like social media posts, feedback forms, etc. It will also help executives to get visual responses and analytic insights simply by typing their queries. 

6. Multi-Cloud Management

As businesses consume vast amounts of data, the need for storage space also increases. Enterprises leverage cloud services to store, manage, and analyze data. The leading cloud platforms, such as AWS, GCP, Azure, etc, enable businesses to integrate AI and scale up rapidly without any issues. Hybrid cloud solutions harness the power of multiple cloud systems. By 2027, 70% of global enterprises will adopt Industry Cloud Platforms (ICPs) to accelerate their business operations.  

7. Advanced-Data Visualization 

Business intelligence is not about digging deeper into spreadsheets. The BI tools utilize complex data to present insights visually to make it easy for everyone to understand. Collaboration between the BI tools will be improved to enhance data visualization. This will allow multiple businesses to share and exchange their data to work together and innovate. The ease of use offered by data visualization tools makes it one of the prominent business analytics trends.

8. Predictive and Prescriptive Analytics

Data analytics scope will extend far beyond in the upcoming years. Businesses will move on to predictive and prescriptive analytics. Predictive analytics will help businesses take a peek into their future probabilities, which will help them in making growth decisions. Prescriptive analytics will enable leaders to take action to address pressing issues in the organizations. Intelligent analytics use artificial neural networks (ANN) and autoregressive integrated moving averages (ARIMA) to make accurate predictions. 

9. Demand for Data Literacy

As organizations continue to leverage data for every aspect of running the business, employers need their employees to acquire data literacy. Professionals can get data analytics certifications after completing the Data Analytics & Engineering Bootcamp that CCSLA offers. In the future, organizations can expect the workforce to have advanced data science knowledge to work with AI models and BI tools.  

10. Embedded Analytics

Business applications are expected to undergo a major transformation with embedded analytics. There will be no need for separate BI tools when these features are integrated into the business applications. This will enable all the stakeholders to use actionable insights in every step. Advanced BI features and a visual dashboard will enable managers to make quick decisions.  

11. Democratized Generative AI

Generative AI adoption increased massively after the launch of ChatGPT. As the number of pre-trained models increases, the availability of open-source and cloud computing technologies makes generative AI accessible worldwide. Garner predicts that over 80% of enterprises will use GenAI APIs by 2026. Business users can use these GenAI tools to access vast amounts of applications easily. It will enable enterprises to democratize skills and knowledge. 

12. AI Trust, Risk, and Security Management

Businesses must take robust measures to implement Trust, Risk, and Security Management (TRiSM) to secure data and operations. Democratized access to AI further emphasizes the need for effective TRiSM implementation. This will implement guardrails for the AI models to control risks. It can help organizations increase decision-making accuracy by 80%.

13. AI-Augmented Software Development

Advanced BI tools require a vast amount of coding. Software and data science engineers have to write millions of lines of code to create a comprehensive BI application. The competitiveness in the BI tools market will increase when new tools are introduced. AI-assisted software engineering technology will enable software and data science engineers to focus more on the design and composition of business applications. At the same time, the AI partner can help with coding and testing. 

14. Intelligent and Adaptive Business Applications

Business intelligence applications will become adaptive in the future as these tools gain inherent intelligence. These tools will become capable of adapting to respond autonomously and appropriately to uncover new information. 26% of CEOs reported talent shortage as the most damaging risk of their organization. Adaptive BI tools will help organizations prioritize their workforce and create enterprise-specific strategies rapidly. 

15. Augmented-Connected Workforce

Another trend driving the importance of intelligence applications and analytics is the growing need for developing an augmented connected workforce (ACWF) strategy. The BI tools of the future will focus more on people assets to enhance workforce experience. These tools will help organizations expand their skills by enabling their people to deliver their own skills. ACWF technology will also impact the business positively and push the business in the growth path. By 2027, 25% of CIOs will start using ACWF initiatives. 

16. Selling to Machine Customers

Machine customers are non-human customers who can complete sales and exchange payments. By 2030, businesses are expected to receive trillions in revenues through these customers. Businesses must upgrade their BI and data analytics usage to meet the demands of machine customers who work on pure logic and display distinct patterns. Strategic decisions will be required when these machine customers cannot be won over by taking them to business lunches. 

17. Sustainability Adoption 

In the upcoming years, businesses focusing on sustainability initiatives will have better growth prospects. The BI tools should also improve environmental, social, and governance (ESG) outcomes. An organization’s energy dependence will increase when using technologies such as IoT, AI, cryptocurrency, and cloud computing. The leaders must explore their business data deeply to create sustainable, circular, and efficient energy solutions. 

18. Data Economy

Leveraging big data and deriving insights from unstructured data will result in the growth of the data economy. Businesses can expand their revenue streams by monetizing data. AI-powered BI tools can identify unforeseen patterns and derive more value from data. Companies like John Deere have already pioneered the data-selling model. The company uses its data from farm equipment sensors to create insights that are sold back to farmers to increase their productivity. Democratization of AI will enable small-scale businesses to leverage this trend. 

Conclusion 

In the future, the scope for improvements in the BI and analytics field is massive. Data is not just an asset that needs to be organized. Organizations will start using data as an integral driver for strategic advantage. Businesses that update their data-driven strategies to implement BI and big data analytics will thrive in tomorrow’s digital world. Understanding business intelligence future trends will enable businesses to stay competitive. 
CCS Learning Academy offers multiple data science courses to improve data literacy. You can improve your employability and stay on top of the latest BI trends when you complete certification programs like CompTIA Data+. Regardless of your industry, you can become a data analytics and BI expert with Data Analytics & Engineering Bootcamps and advance your career.

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