As a framework, composable analytics allows insight consumers to reuse and combine modular components for different workflows and use cases. Data ingestion tools, data prep tools, analytics engines, and data visualization tools operate as distinct blocks that can easily be mixed and matched, often using machine learning (ML) and microservices to make it all function as a single unit.
For example, a retail company might want to understand why sales are dropping in certain regions. Instead of spending hours coding a new analytics process or reconfiguring the data pipeline to feed into a new dashboard, data teams just pull together a sales database, a customer feedback sentiment model, and a regional weather data source. They connect and integrate these components in an analytics platform which assembles them into a custom insight workflow. A manager can then easily view reports that link product category-specific sales dips to negative sentiment and weather anomalies.
Composable analytics is not particularly new. It’s been around as a base concept since 2012 and it’s been a strong trend in business since at least 2020, when Gartner’s Daryl Plummer delivered a keynote speech hailing “composable” as the future of business. Today, however, the concept is riding the wave of popularity. All of a sudden, everything is composable: composable documents, composable metrics, composable ERP, composable architecture, composable business.
Composable might be a trending buzzword simply because modular and agile are already old-fashioned. But whatever you call it, there are solid reasons why you’re seeing it everywhere.
What You Will Learn
- What makes composable analytics flexible and accessible
- How composable analytics lowers the cost for enterprise analytics
- Why composable analytics delivers accelerated time-to-insight
Environment-Agnostic Accessibility
Embedded analytics interfaces are everywhere nowadays. You’ll find them in banking apps, CRMs, and ecommerce platforms; in dashboards that measure web traffic, winning streaks in gaming, and stock market changes. In enterprise, they’re used by every department to generate visualizations and reports.
Composable analytics makes it possible. Because data, tools, and services are packaged as discrete components and linked using APIs, they can be placed into different environments without losing governance, business logic, or metrics. This makes them accessible even in hybrid environments (which are increasingly common in business), and in every situation. Pyramid Analytics offers a decoupled metrics layer that ensures consistent governance in every situation. The platform’s enterprise-grade embedding solution makes it easy for developers to integrate conversational analytics in any host app.
Offering the Freedom to Customize
Before composable analytics, data teams had to work within the confines of what was available in all-in-one analytics suites.
Data analysis platforms might be designed for different end user needs, unable to connect to all your data sources, and/or be too slow or compute-heavy, but companies just had to make the best of it. In contrast, composable analytics makes it possible to customize use cases for each circumstance.
Components can be combined at will to meet specific requirements. Enterprise data teams can break out of the restrictions of tool capabilities and mix vendors, interfaces, data sources, and logic layers to build analytics flows that meet their needs.
Empowering Citizen Analysts
Now that data is everywhere, everyone wants to take advantage of insights and recommendations. While that’s a positive development, it means that data scientists spend more time replying to requests than getting on with their own work. Even self-service business intelligence (SSBI) often requires significant setup help from analysts.
Composable analytics makes true self-service more accessible. Line-of-business (LOB) users without data science knowledge can drop pre-built analytics components into a low-code or no-code platform and embed them directly into operational tools or decision-centric applications, to surface new insights that deliver exactly what they need. It’s a freedom offered by tools like Holistics, which is built for self-service drag-and-drop usage.
The centralized semantic layer, consistent governance, and seamless connections with all kinds of data sources translates into assured compliance, and prevents data from becoming polluted or confused. LOB users don’t need to turn to data teams to build a new data pipeline, and the data retains its integrity, informing trustworthy insights.
Keeping Costs Down
Traditional analytics is costly. Each dashboard and report is custom-built, which is expensive in itself. Adjusting one requires expert data engineering time and often multiple specialists, while maintaining components adds to the cost. Plus you usually have to pay for multiple tools and data storage, because each team needs a different analytics setup.
Composable analytics slashes through many costs. The modular setup is easy to reconfigure for new requirements, cutting work hours. Maintenance and resource costs fall because systems share components and governed layers; changes propagate automatically; and cloud-native infrastructure means you only pay for the resources you need. Composable analytics also future-proofs your analytics stack, so you won’t face a high price tag in another few years.
You can replace individual modules as and when a better alternative comes along, instead of having to choose between the expense of replacing it all or the hidden costs of using outdated tools. For example, fintech apps need identity verification as part of real-time analysis to approve credit lines. iDenfy offers a composable-friendly API that could fit the identity verification workflow. If you find a better API for your needs, you can just swap it out without expense.
Insights at the Speed of Business
Enterprises today move faster than the speed of light, and markets change more swiftly than ever. Time to insight can be a real competitive differentiator in industries like ecommerce, retail, and financial services, while fast responses to changing conditions can be critical for functions like marketing, transportation, and logistics.
With composable analytics, you can turn your analytics stack on a dime. Because components are modular, teams can share, remix, and reuse analytics created by others. There’s no wait time while data teams build new dashboards, and the centralized semantic layer which ensures a single source of truth removes the time needed to realign definitions.
Insights are also ready more quickly because analytics systems connect directly to live or streaming data sources like Snowflake, BigQuery or even Salesforce. Data teams don’t have to constantly build or export data pipelines anew. When data is updated in one place, it’s updated in every dashboard and analytics app, so new insights propagate instantly across the whole organization.
Key Takeaways
- A composable analytics approach allows enterprises to customize their analytics stack easily.
- Composable analytics frameworks remove friction, time, and cost from analytics processes.
- LOB users can access insights more easily and quickly with composable analytics.
A Framework for Adaptive, Future-Ready Data Teams
Whatever name it goes under, composable analytics is a value-driver for enterprises. The ability to take apart and put together modular analytics components reduces friction, time, and expense for enterprise analytics while improving accessibility for non-data science experts. It may not be radically new, but composable analytics deserves the plaudits.
