What is composable analytics?

Why is composable analytics useful?

Composable analytics offers numerous benefits that make it a good option for organizations looking to optimize decision-making processes. Composable analytics is useful for the following reasons:

• Breaking down data silos. Instead of being limited to a specific data silo, composable analytics supports the combination of data from various sources, potentially uncovering new insights previously hidden in isolated systems.
• Increased flexibility and agility. Organizations can quickly adapt to changing business needs by assembling and reassembling analytics components.
• Customization. Composable analytics enables the creation of customized analytics applications that address specific business requirements, rather than being constrained by off-the-shelf products that are limited by one set of vendor capabilities.
• Faster insights. The composable analytics approach combines diverse data sources and tools to generate insights more quickly than traditional BI systems can with more restricted data stacks.
• Improved integration. Composable analytics eases the integration of multiple data sources and technologies, providing organizations with a broader view of the data landscape.
• Wider access to BI and analytics data. Composable analytics lets more users across the organization access and work with BI data through low-code and no-code options. This expands analytics adoption beyond BI and data specialists and supports self-service BI initiatives as well as related data democratization efforts.
• Cost-effectiveness. Modular components can be reused, potentially reducing overall costs compared with a monolithic data stack.
• Improved customer experience. Composable analytics enables more personalized and relevant services based on data insights that can come from any number of different tools that are part of the composable analytics stack.
• Enhanced scalability. Composable analytics can scale and grow more easily with organizational needs, adapting to increased data volumes and user requirements.

How does composable analytics work?

Composable analytics works by breaking down analytics capabilities into modular components that can be assembled and reassembled as needed. A composable analytics stack works in the following series of steps:

1. Component identification

Organizations can first identify and select modular components for their analytics needs, such as data ingestion tools, analytics engines, visualization tools and AI/ML capabilities.

2. Data integration

The various data sources can be connected and integrated, enabling the combination of structured data, unstructured data and semistructured data from across the organization.

3. Semantic layer creation

A semantic layer is established to provide a consistent understanding and interpretation of data across the organization and its applications.

4. API and microservices setup

It's critical to use an application programming interface approach to help connect the modular components. Those APIs can make use of and connect to microservices that enable different components to communicate and work together.

5. Low-code/no-code configuration

Tools are set up to enable business users to create their own analytics stack. These are commonly low-code or no-code tools that require minimal IT intervention and often provide drag-and-drop interfaces that are easy to use, with simple configuration options.

6. Analytics engine deployment

A metrics store -- also sometimes referred to as a headless BI engine -- can be deployed, separating analytical calculations from the end-user consumption layer. This provides consistent analytics across different platforms and applications.

7. Customization

Users compose their analytics applications by combining and configuring the various components for their specific needs.

8. Data analysis

With the system in place, users can perform various types of analysis, from simple reporting to complex predictive analytics, using the tools and data sources they've composed.

9. Continuous iteration

Components can be added, removed or reconfigured to adapt to new requirements, ensuring the composable analytics stack stays effective.

Key components of a composable analytics architecture

There are many key components of a composable analytics architecture. These include foundation components for services as well as analytics application components assembled in the composable analytics stack.

At the infrastructure layer, a composable analytics architecture includes the following:

• Cloud infrastructure. Cloud technologies provide the compute, storage and application delivery capabilities.
• Microservices architecture. One of the best ways to support a modular approach is with a microservices approach that divides complex operations into smaller, independent services that can be combined to create larger workflows.

Data application components in a composable analytics architecture include the following:

• Data ingestion. The system must be able to ingest data from different sources.
• Data preparation. The ability to prepare data properly, accurately and in the right format for analytics is a key factor.
• Data science tools. This includes components to support advanced statistical analysis and model building for data scientists.
• Metrics store. A centralized repository for metric definitions and calculation is necessary.
• Data visualization and reporting tools. Components to create dashboards, reports and other data visualizations are required.
• AI/ML capabilities. Integration with AI/ML tools is a common component enabling predictive analytics and advanced data processing.
• Security and governance tools. These components help to support data privacy and compliance with regulations. They should also support user access and permissions to the composable analytics stack.
• Orchestration and management tools. These tools manage the overall composable analytics ecosystem and coordinate interactions among different components.

Composable analytics use cases

Composable analytics can be applied across various industries and business functions. Composable analytics use cases include the following:

• Real-time fraud detection. Transactional data can be combined with machine learning models to identify and prevent fraudulent activities as they occur.
• Predictive maintenance. Sensor data from machinery and historical performance data can help to predict when repairs are needed, reducing downtime and maintenance costs.
• Customer behavior analysis. Data from various touchpoints such as sales, customer service and social media can be used to gain a comprehensive view of customer behavior and preferences.
• Supply chain optimization. Data from suppliers, inventory and logistics can be combined to help improve efficiency and reduce costs in the supply chain.
• Financial forecasting and planning. Financial data, market trends and economic indicators can provide accurate forecasting and budgeting.
• Employee hiring and retention. Human resources data, performance metrics and external market data can be analyzed to improve hiring processes and reduce employee turnover.
• Product development. Customer feedback, market trends and performance data can be used to inform new product development and improvements.
• Healthcare analytics. Patient data, treatment outcomes and research findings can be integrated to improve patient care and operational efficiency in healthcare settings.

Composable analytics challenges

While composable analytics has numerous use cases and offers significant benefits, it also has the following potential challenges:

• Integration complexity. Combining various components and data sources can be complex, especially when dealing with diverse data formats and quality issues, requiring careful planning and execution to ensure proper integration.
• Maintaining data consistency. Ensuring data remains consistent and accurate across all components and use cases can be challenging in a composable environment.
• Data governance. With data coming from multiple sources, maintaining strong data governance practices is a critical challenge. A core element of data governance is regulatory compliance, which can also become more of a challenge in a composable analytics type of deployment.
• Security concerns. With data flowing between various components and potentially across different environments, properly implementing security measures becomes more complex.
• Skills gaps. Implementing and managing composable analytics could require new skills that existing staff might not possess, necessitating training or new hires.
• Change management. Shifting to a composable analytics approach requires changes in organizational mindset and processes, which can be challenging to implement.
• Performance optimization. Ensuring optimal performance across all integrated components and data sources can be challenging, especially as the system scales.
• Cost management. While potentially cost-effective over time, the initial implementation of a composable analytics system can involve significant costs.