By 2023, over 33% of large-sized businesses will practice decision intelligence. Data analytics makes decision-making 5x faster for businesses. Self-service business intelligence is crucial for 60% of research and development departments. The global business intelligence market will grow to $33.3 billion by 2025. (


In today’s business world, data is the most valuable resource for businesses to help effective decision-making and apply efficiency in business processes. And nowadays, every organization is growing its data daily, and the organization must get some inside information from the existing data to understand customers better and increase sales. So here, business intelligence plays a key role in simplifying the problem using existing data and providing quick metrics using BI Tools to support better data analysis.

Several Expected Trends in the Field of Business Intelligence

These trends include:

  • Migrating Data to the Cloud
  • Self-service Analytics
  • Data-Ops
  • Real-Time Analytics
  • Streaming Analytics
  • Deploying ML models to the cloud and showcase using BI tools.
  • Augmented Analytics

Migrating Data to the Cloud

Data migration to the cloud refers to transferring and storing firm data from physical servers or on-premises infrastructure to cloud-based servers and storage systems.

Companies are moving to the cloud because data volumes and data sources are increasing with size and data access from hydrogenation databases, and managing these has become a challenging task for organizations.


  • Scalability: Easily alter data storage and processing capacities to meet changing needs
  • Cost-effectiveness: Lower initial investments and operational costs
  • Increased security: Put in place strong safeguards to protect data from breaches and unauthorized access

Use Cases:

For instance, suppose a retailer wishes to migrate his sales and inventory data from on-premises servers to a cloud-based platform like AWS or Microsoft Azure. By doing so, they may take advantage of the cloud’s scalability and cost-effectiveness, allowing them to handle expanding data quantities and analysis requirements successfully.

Best Data Migration Tools and DB

Self-Service Analytics

Self-service analytics empowers business users to independently interact, analyze, and explore data without relying on technical resources. It enables users to access and analyze data directly without the need for IT support or data analysts. This trend promotes greater data accuracy, resource optimization, cost efficiency, and precise decision-making. Users can generate insights and reports promptly, enabling faster and more agile decision-making processes.


  • Data Accuracy
  • Reduces dependency on technical resources for data analysis
  • Eliminates the need for manual involvement

Use Cases:

Sales teams can leverage self-service analytics tools to analyze customer data, sales performance, and market trends. They can gain insights into customer preferences, identify sales opportunities, and optimize sales strategies without relying on dedicated data analysts.

Best Self-Service Analytics Tools

  • Power BI, Domo & Looker & MicroStrategy 2020


Data-Ops, an organization-wide data management practice, focus on controlling data flow from source to value. In today’s data-driven environment, organizations face the challenge of managing an ever-increasing volume, velocity, and variety of data. Data-Ops seeks optimal data efficiency and value creation by ensuring scalability and consistency in data operations. It enables organizations to derive insights from data more effectively, improving decision-making and operational efficiency. Benefits:

  • Manage and process massive volumes of data efficiently
  • Ensure consistent and reliable data management practices across the organization
  • Maximize value creation and scalability of data work

Use Cases:

A financial institution implements Data-Ops principles to streamline its data management processes. By adopting automated data pipelines, standardized data quality checks, and collaboration among data engineers and analysts, they achieve faster and more reliable data processing, leading to improved decision-making and operational efficiency.

Best Data-Ops Tools:

Amazon Web Services, Snowflake, Apache Airflow & Databricks Lakehouse Platform & Datafold

Real-Time Analytics

Real-time analytics involves the processing and analysis of data as it is generated, enabling organizations to gain insights and make informed decisions in real time. This trend is particularly relevant in industries such as IoT and financial trading, where the speed of data analysis is critical. Real-time analytics helps businesses quickly to identify new trends and deliver a superior customer experience.


  • Gain timely and actionable insights for informed decision-making
  • Respond quickly to market changes and opportunities
  • Proactive decision-making: Identify trends and patterns in real-time for proactive actions

Use Cases:

A transportation company utilizes real-time analytics to monitor vehicle locations, predict traffic patterns, and optimize route planning. They can make real-time adjustments to improve efficiency, reduce fuel consumption, and enhance customer satisfaction by analyzing live data streams from GPS devices and traffic sensors.

Best Real-Time Analytics Tools:

Google Cloud DataFlow, Amazon Kinesis & Apache Kafka & Azure Stream Analytics & Apache NIFI, etc

Streaming Analytics

Streaming analytics focuses on processing and analyzing data continuously rather than in batches. This trend benefits data sources that generate data in small increments and require immediate analysis. Streaming analytics enables organizations to gain dynamic insights, detect patterns, and make real-time decisions. It finds applications in various domains, including social media monitoring, fraud detection, and IoT device management.


  • Real-time processing: Analyze and act on data streams as they occur
  • Immediate insights: Quickly identify and respond to emerging trends and issues
  • Continuous monitoring: Detect anomalies and make rapid data-driven decisions

Use Cases:

An e-commerce platform uses streaming analytics to monitor customer behavior in real-time. By analyzing the stream of customer clicks, purchases, and interactions with the website, they can deliver personalized recommendations, detect fraud in real-time, and optimize the user experience.

Best Streaming Analytics Tools:

Apache Kafka, Apache Spark & Memgraph & Stream Analytics by IBM

Deploying ML Models to the Cloud and Showcasing using BI Tools

Deploying ML models to the cloud and showcasing them using Business Intelligence (BI) tools is an innovative approach combining machine learning with data visualization and analysis. This trend involves leveraging cloud infrastructure to host and deploy machine learning models, allowing organizations to harness the predictive capabilities of these models in real-world scenarios. By integrating ML models with BI tools, businesses can unlock valuable insights from their data and make data-driven decisions more effectively. Benefits:

  • Efficient model deployment: Leverage cloud resources for seamless model deployment
  • Enhanced model utilization: Utilize BI tools to showcase model insights effectively
  • Streamlined workflow: Combine ML models and BI tools for end-to-end analytics

Use Cases:

Customer Segmentation: ML models can analyze customer data to identify distinct segments based on preferences, behavior, and demographics. By integrating these models with BI tools, businesses can visualize and understand customer segments more effectively, enabling targeted marketing campaigns and personalized customer experiences.

Best ML Tools:

Google Cloud ML Engine, TensorFlow & PyTorch & Amazon Machine Learning

Augmented Analytics

Augmented Analytics is a transformative trend in the field of Business Intelligence (BI) that combines artificial intelligence (AI) and machine learning (ML) algorithms with traditional analytics tools. It aims to enhance and automate the process of data preparation, analysis, and visualization, enabling organizations to derive meaningful insights and make data-driven decisions more efficiently.


  • Automated insights: Extract valuable insights from vast data sets quickly
  • Enhanced decision-making: Make data-driven decisions with advanced analytics capabilities
  • Improved data strategies: Optimize operations and identify growth opportunities through analytics

Use Cases:

Sales Performance Analysis: Augmented Analytics can analyze sales data, customer behavior, and market trends to provide insights into sales performance. It can identify factors influencing sales, recommend strategies, and predict future trends.

Customer Sentiment Analysis: We can use it to analyze customer feedback, social media data, and other unstructured data sources to determine customer sentiment and preferences. This enables organizations to understand customer needs, improve products or services, and enhance customer satisfaction.

Best Augmented Analytics Tools:

Microsoft Power BI, Tableau & Oracle Analytics Cloud & DOMO


In summary, these trends in business intelligence demonstrate the growing importance of leveraging advanced technologies to extract actionable insights from data. By embracing these trends, organizations can enhance their decision-making processes, improve operational efficiency, and stay ahead in today’s competitive business landscape.

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About the Author

Kishor Gund

Kishor Gund

Solution Architect - Integration & Analytics-Analytics

He has 15+ years of rich experience in designing, developing, and maintaining large data warehouse business applications such as data migration, integration, and conversion and testing from inception to implementation in a timely manner using ETL/ELT approach in Agile model. And Expertise in cloud data warehousing and re-engineering the Enterprise Data warehouse in different environments like Oracle, Teradata & snowflake with multiple Terabytes of size and various levels of complexity using tools - Snowflake , OCI/OAC, OBIA etc.

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