Everything You Need to Know About Data Analytics Platforms

Data analytics platforms allow businesses to collect, process, analyze, and visualize large data volumes. It is a software solution or tool suite where organizations can contextualize information and gather insights by integrating multiple data sources. This allows users to consolidate their systems in one place where they can clean and transform their data.

Once the data is ready, the platform provides tools for exploratory analysis and statistical modeling. Users can also perform advanced analytics techniques, with many platforms offering algorithms and statistical functions to uncover patterns, correlations, and trends.

Businesses can use data analytics platforms to generate reports or schedule automatic data refreshes for collaborative sharing across teams They can handle high-velocity data streams and real-time data processing so that users can make immediate decisions based on up-to-date information.

These platforms oversee all data types and support organizations through the analytics lifecycle. Many solutions have user-friendly, configurable dashboards where businesses can track, interpret, and share data for meaningful use.

Most businesses have data. However, how they use it to influence business growth relies on how well the data is analyzed and actioned.

Research shows that organizations using big data saw an 8% increase in profit and a 10% reduction in cost. Businesses were also 23 times more likely to acquire customers and six times more likely to retain them.

This is where data analytics platforms are essential, as they provide a clear-cut way for businesses to see what growth decisions they need to make and why.

Their dashboards bring meaning to masses of information, combining various tools and technologies to transform data into easier-to-understand visualizations. Businesses can share the results across teams and stakeholders to achieve harmony in the company and ensure everyone is well-informed.

Companies can use these insights to make well-informed decisions and influence strategies, supporting their actions with data-backed evidence.

For example, marketing and product teams can interpret customer data to tailor ads and improve products, increasing sales and reducing churn. Operations and executive teams can also analyze performance data to boost company efficiency or minimize turnover rate.

With insight-driven businesses setting the competitive pace of growing at a rate of 30% annually, the case for data analytics platforms is strong.

Data analytics architecture differs slightly between platforms. What may be helpful for one business size and type might not apply to another.

However, most architecture includes essential layers, each delivering a specific function in the analytics lifecycle. These components provide the foundation for building a robust and effective platform for end-to-end data analysis processing.

Ingestion layer

The ingestion layer collects and ingests data from several sources into the analytics infrastructure. It plays a crucial role in the initial stages of data processing, where raw data is collected and prepped.

This layer gathers data from diverse sources across a business, including databases, files, APIs, streaming platforms, and external systems. Platforms often build and then offer direct integrations, in-app, between external solutions and tools. The ingestion layer establishes authenticated and verified connections with the sources, extracts the data, and transports it to the analytics platform for processing.

Storage layer

Data must be stored somewhere after it is gathered and ingested into the platform. The storage layer provides a structured and organized repository where you can easily access data for processing, analytics, and visualization.

Most platforms utilize data warehouses, lakes, and distributed or cloud storage systems. However, the type of storage system depends on the platform and business needs.

The storage layer might also employ compression and optimization techniques to reduce the size, therefore improving efficiency and performance. Turning the data into manageable chunks maintains its integrity while allowing faster operations.

Processing layer

The processing layer contains computational and analytical capabilities to transform raw data into meaningful insights.

It uses data validation, cleanup, normalization, transformation, and enrichment to turn the data into an insightful format. Depending on the solution, these are conducted in batch or real-time processing.

This layer performs several tasks to ensure the data meets quality, consistency, and relevancy standards. It also creates a unified view of the data, which could involve merging data sets, resolving conflicts, and aligning data structures. This process enables a comprehensive analysis across disparate sources.

Catalog and storage layer

The catalog and storage layer typically manages metadata, data cataloging, and other storage-related operations, ensuring efficient access to organized data.

Metadata plays a crucial role in helping users understand the meaning and reliability of the data. It includes information about the data's structure, format, quality, and lineage, among other aspects. The data catalog serves as a centralized hub for metadata, enabling users to discover, search, and navigate through data assets.

This layer stores processed data and manages tasks such as partitioning, replication, backup, and recovery. The specific technology employed in this layer depends on the platform's architecture.

Managing the lifecycle of data often falls into this component. It involves defining data retention policies, archiving or purging data, and effectively managing storage resources.

Business intelligence (BI) and analytics layer

The BI and analytics layer transforms raw data into actionable insights, equipping users with tools to explore, analyze, and visualize data to support decision-making.

Users have the flexibility to manipulate data to address business concerns. They can create custom queries, apply filters, perform aggregations, and conduct exploratory analysis. Users can also generate compelling and easily understandable data visualizations, presenting information through charts, graphs, heatmaps, and other formats.

Some data analytics platforms may also integrate advanced analytics and machine learning capabilities within this layer, enabling sophisticated data analysis and predictive modeling.

Security and governance layer

Businesses must adhere to industry rules and regulations that concern data usage. A data analytics platform's security and governance layer focuses on safeguarding data and ensuring compliance with privacy measures.

The components within this layer may vary based on the type of data collected, country-specific regulations, and business requirements. However, the security and governance layer typically includes data encryption, access controls, user authentication, and authorization mechanisms. These measures prevent unauthorized access, data breaches, and loss.

This layer often incorporates data quality management, lineage tracking, stewardship, policy enforcement, and metadata management to support data governance practices. These elements ensure integrity, reliability, and adherence to established data policies.

Data analytics platform features differ based on the software you choose and the specific needs of your business. It is important to note not all software components may be necessary, particularly for smaller organizations. On the other hand, if your business operates in a specific industry, you may need more specialized features tailored to your unique requirements.

The following features are recommended as essential components to ensure effective data analytics.

Dynamic dashboards

Dynamic dashboards offer interactive data updates, usually in real-time or near real-time, providing flexibility and responsiveness. These dashboards retrieve data and continuously update visualizations as information is received.

The interactive nature of these dashboards empowers users to customize displays and concentrate on specific data aspects. This capability is invaluable for thorough and effective data exploration, enabling users to delve deep into the insights.

In addition to their core functionality, some dynamic dashboards offer features like alerts and collaboration options. Users can establish thresholds or conditions that trigger notifications when specific events occur, informing them of critical changes. For example, product managers might want to be notified if the conversion rate falls below a predefined threshold, so they can analyze the drop and take action.

Collaboration elements allow for the sharing and discussion of dashboards among team members, fostering collective decision-making and enhancing business collaboration.

Data visualization

Having a wealth of raw data is futile if it is not presented effectively. Data visualization features are vital in providing users with a visual representation of their collected information.

Data can be presented in various formats, including charts, graphs, maps, and more, tailored to suit the business' specific needs and enhance the understanding of complex data sets.

By leveraging data visualization, organizations can discern patterns, trends, and relationships within their data, enabling them to uncover valuable insights and draw meaningful conclusions. For example, growth teams can create a funnel visualization to identify their customers’ drop-off points and could even segment the data further based on different attributes. With this information, they can work with other internal teams to refine campaign messages or optimize the purchasing experience.

Data visualization also facilitates the identification of anomalies and outliers, allowing for a deeper exploration of specific data subsets.

Businesses can elevate their data storytelling and communication when information is visually appealing. This, in turn, leads to improved decision-making and a more comprehensive understanding of the organization.

Scalability

When selecting a data analytics platform, it is crucial to prioritize scalability. As businesses expand and evolve, the chosen software must be able to accommodate growing data volumes, increased user loads, and greater computational demands. This ensures there is no need to switch to alternative data analytics software or invest in additional resources.

An ideal platform should scale seamlessly in response to a business's evolving needs while maintaining optimal performance, responsiveness, and reliability.

The approach and implementation of scalability may vary depending on the specific platform. Still, the software must have the ability to manage higher volumes of data and requests without experiencing significant downtime or compromising functionality.

Source control

Source control is an essential feature that helps maintain code quality and lets users revert to previous versions of a data analytics platform in the event of an error.

This layer is responsible for managing and tracking changes to the source code, scripts, configurations, and other elements within the development deployment of the analytics platform.

Version control systems are valuable for storing, managing, and organizing codebases and other files. They enable teams to collaborate effectively, track changes, and maintain a comprehensive history of modifications.

By utilizing source control, users gain insight into the platform's evolution. Businesses can manage different software versions, pinpoint the exact location and rationale behind specific changes, identify the individuals involved, and address potential issues. Deployment automation streamlines the process by making automatic changes using continuous integration and deployment practices.

Data governance security

As mentioned, governance and security are paramount in data analytics platforms to ensure businesses comply with regulations and safeguard sensitive information.

Data analytics platforms incorporate a range of measures to address data governance and security requirements. These measures encompass:

• Access controls
• Data encryption
• Data masking and anonymization
• Data loss prevention mechanisms
• Auditing and logging
• Security monitoring and threat detection

A combination of these features ensures proper protection and adherence to compliance standards. Businesses must also verify that data governance security features align with their location and industry, meeting specific regulatory requirements and industry standards.

With many businesses utilizing data analytics software, choosing the right platform for your needs can be complicated.

You must consider several factors, including the type and amount of data you want to analyze, how you want to present it, accessibility and collaboration needs, and security requirements.

A data analytics platform is vital for business growth, but the software takes time to implement and integrate into your operations. You must think about your current resources, if training is needed, and potential downtime.

To help make the decision easier, we have highlighted some of the most popular data analytics software tools.

Amplitude

Amplitude Analytics is ideal for companies who want an easy-to-implement, out-of-the-box solution. Achieve business growth with detailed insights and trendspotting, supported by machine learning.

With Amplitude, businesses can easily define and track custom events relevant to their goals. This flexibility allows for detailed monitoring of user actions, such as clicks, conversions, and feature usage, empowering companies to track and measure the success of their KPIs accurately.

The robust segmentation capabilities enable businesses to create dynamic user segments based on specific attributes and behaviors. These features help identify and understand user cohorts, producing targeted marketing campaigns, personalized experiences, and tailored communication strategies.

Amplitude's funnel analysis feature visualizes user conversion funnels, allowing businesses to identify drop-off points and optimize the user journey. Data tables let users explore and find answers quickly, while cohort analysis provides insights into how different user segments compare.

Create comprehensive reports and visual representations of key metrics with intuitive visualization tools, customizable dashboards. Data governance is also a priority, with features such as data access controls, user permissions, and compliance with privacy regulations.

With its extensive feature set and growth mindset, Amplitude Analytics empowers companies to make informed decisions and stay ahead in a data-driven landscape.

Segment

Segment acts as a centralized data hub, integrating data from various sources, such as websites, mobile apps, and third-party services. The platform supports a range of data collection methods, including JavaScript libraries, mobile SDKs, server-side integrations, and cloud-based data warehouses.

The tool offers a suite for managing and analyzing data and data governance features to ensure compliance.

Segment also integrates with various analytics, marketing, and customer engagement tools, allowing businesses to activate their data across different systems.

Smartlook

With a focus on behavioral analytics, Smartlook uses various features to help companies understand the user journey, identify pain points, and optimize the customer experience.

Session recording lets businesses capture and replay every action on their website or app, while heatmaps visually represent user interactions.

Smartlook also offers conversion funnels so businesses can track and analyze the customer journey. The platform provides real-time monitoring of user activity, including live view and event tracking.

Sensitive customer information is protected through Smartlook’s data anonymization and customizable privacy settings to comply with privacy regulations.

ProductPlan

ProductPlan is a platform for product-related information where businesses can plan, visualize, and communicate their product strategies.

It has a drag-and-drop interface where users create and adjust product roadmaps. Teams can modify timelines, manage backlogs, track progress, and organize ideas. Multiple stakeholders can contribute to the roadmap directly on the platform.

Businesses can connect product planning efforts with the execution and delivery processes. Analytics features allow users to track key metrics, measure progress, and evaluate the success of their product initiatives.

Intercom

Intercom is a customer messaging platform focused on customer engagement and support that allows businesses to manage their customer communication in one place.

The platform combines multiple channels, including live chat, email, and in-app messaging, so organizations can have personalized, real-time customer conversations.

Intercom provides analytics and reporting features to help businesses measure the impact of their customer communication efforts. Companies can track key metrics, such as response times, conversation volumes, and customer satisfaction.

It doesn’t matter what industry, size, or type of business you are in—you cannot ignore data analytics’ importance.

Data analytics platforms have revolutionized how businesses harness the power of data to gain insights, make informed decisions, and drive growth. These platforms offer the features and capabilities needed for organizations to collect, manage, analyze, and visualize data properly.

Businesses can unlock insights into customer behavior, market trends, and operational efficiency by leveraging data analytics platforms. Such insights empower companies to make data-driven decisions, optimize processes, and enhance customer experiences.

Adopting data analytics platforms is essential in today's competitive business landscape. In an increasingly data-centric world, and with the advancement of technology and data availability, investing in a robust solution is crucial for success.

Interested in seeing how a data analytics platform works? Try Amplitude’s self-service demo.