What is an analytics platform?

An analytics platform is a unified solution that combines multiple functions, supporting the needs of analysts and business users across the end-to-end analytics lifecycle. An analytics platform manages all facets of the data analysis lifecycle, including data acquisition, preparation, enrichment, curation, collaboration, storage and analysis. It makes data readily available, enabling self-service access and supporting the full range of analytic and data science applications.

This article covers the analytics platform as a category: what it includes and how to choose one. For where a platform runs, see what is cloud analytics?, and for the technology behind modern platforms, see what is a data analytics platform?

Key capabilities of an analytics platform

Each stage of the analytics lifecycle maps to a capability the platform should provide:

• Data acquisition: Data connectors that pull data from ERP, CRM, finance, supply chain and other source systems.
• Preparation and enrichment: Cleansing, joining and transforming data so it is ready for analysis.
• Curation and modeling: Business-friendly definitions, often through a semantic layer, so metrics mean the same thing everywhere.
• Storage: A performant, governed store for current and historical data.
• Analysis and visualization: Dashboards, ad hoc exploration and reporting for business users.
• Data science and AI: Support for notebooks, machine learning and AI agents working from the same data.
• Collaboration and governance: Sharing, security, lineage and access controls across teams.

Why use an analytics platform?

Traditional solutions are costly and complex. In the past, organizations would typically develop an analytics capability in-house using several discrete purpose-built tools. For example, organizations might have used a data integration solution from one vendor, a data warehouse from another, and one or more business intelligence tools to access and visualize data.

The problem with this traditional, bolt-on approach is that users find themselves in the integration business, managing and maintaining multiple complex software platforms. This traditional approach to deploying analytic environments results in replicated data, high costs, data governance challenges, multiple sources of truth, data delivery delays, extensive overhead to maintain, and difficulties obtaining accurate and up-to-date information.

All-in-one analytics platform vs. bolt-on stack

The two approaches differ in several practical ways:

• Components: A bolt-on stack uses separate tools for integration, warehousing and BI. An all-in-one platform provides these capabilities in one solution.
• Data copies: In a bolt-on stack, data is replicated at each layer. An all-in-one platform requires fewer copies and less data movement.
• Ownership: A bolt-on stack involves multiple vendors and contracts, while an all-in-one platform is a single solution to manage.
• Access to new data: Adding a source to a bolt-on stack typically requires new pipelines and models. An all-in-one platform can provide faster access to new sources.
• Governance: In a bolt-on stack, rules are spread across tools. An all-in-one platform centralizes security and lineage.

A bolt-on stack can make sense when an organization has a deep investment in specific tools. However, as data volumes and user demand grow, the integration burden tends to grow with them.

Common use cases

• Self-service analytics for business users who need answers without waiting on IT.
• Financial analytics and close reporting across ERP and planning systems.
• Supply chain and manufacturing analytics that combine operational data from many sources.
• Decision intelligence and AI initiatives that require trusted, detailed data.

How to choose an analytics platform

Key evaluation criteria include:

1. Source coverage: Can it connect to the organization's applications, databases and files, including multiple sources with varying data structures?
2. Data fidelity: Does it preserve transaction-level detail, or only aggregates?
3. Performance: Can it query large data sets quickly as users and volume grow?
4. Self-service: Can business users explore data without engineering help?
5. Governance: Does it provide security, lineage and a consistent definition of metrics?
6. Breadth of use cases: Does it support BI, data science and AI from the same data?
7. Total cost of ownership: Account for licenses, infrastructure, integration and maintenance effort, not just list price.
8. Deployment options: Does it run where the organization needs it, in the cloud, on-premises or both?

Which analytics platform is right for your business?

Incorta’s Direct Data Platform™ is an example of this new breed of analytic platform. It delivers exceptional performance, connects to virtually any data source, including multiple data sources with varying data structures, and enables a wide variety of analytics and data science use cases. Better yet, as an all-in-one analytics platform, it enables organizations to avoid the need for costly and complex data warehouses, ETL environments, discrete BI tools, and the integration efforts associated with making them work.

Unified analytic platforms such as Incorta empower everyone in the business by providing self-service access to actionable insights, faster time to value, and security without compromise. By combining data from various source systems in the organization in a common data environment, cross-functional teams can generate insights and collaborate from a complete, end-to-end view of business data and take action with confidence in data accuracy and lineage. And since the data is not aggregated, users have full fidelity to run analysis and new queries on 100% of business data down to the transaction-level details.