The landscape of data analytics and machine learning platforms has transformed significantly over the past decade. Modern platforms offer capabilities far beyond the on-premises reporting and business intelligence (BI) tools of the past. This article provides an in-depth guide to understanding these platforms’ features, use cases, user personas, and differentiating capabilities. Additionally, it outlines seven critical steps to selecting the right data platform for your needs, ensuring that your organization remains competitive and data-driven.
Evolution of Analytics Platforms
Analytics platforms have evolved from simple on-premises reporting tools to sophisticated, cloud-based solutions capable of handling complex data visualizations, real-time analytics, and machine learning models. These modern platforms cater to a wide range of business use cases, from basic dashboarding for business users to advanced predictive analytics for data scientists.
Many organizations, especially in lagging industries, are transitioning from managing analytics in spreadsheets to developing dashboards and predictive analytics capabilities. This shift is driven by the need for more scalable, error-free, and integrated solutions. Spreadsheet-based analytics are slow and prone to errors, making it difficult to scale operations and integrate with other data sources.
Larger enterprises that previously allowed departments to choose their own analytics tools are now considering consolidating to fewer platforms. This consolidation facilitates better collaboration between business users, data engineers, and data scientists, enhancing productivity and reducing costs. Modern analytics platforms support various user roles and requirements, from data visualization to modelops life cycles.
As organizations become more data-driven, addressing compliance and data governance within analytics workflows becomes crucial. Ensuring that analytics platforms can manage data governance and compliance requirements is essential for maintaining data integrity and avoiding legal pitfalls.
Identifying Business Use Cases for Analytics
A crucial first step in selecting an analytics platform is identifying your business use cases. Organizations strive to be data-driven, leveraging data, predictive analytics, and machine learning models to aid decision-making. Key use cases include:
One of the primary goals is to empower business users to become citizen data scientists. This involves enabling them to make smarter decisions and create data visualizations, dashboards, and reports without extensive technical knowledge. By democratizing data access, organizations can accelerate decision-making processes and improve overall efficiency.
Professional data scientists require tools that enhance their productivity throughout the machine learning lifecycle. This includes discovering new data sets, evolving machine learning models, deploying models to production, monitoring performance, and supporting retraining efforts. Advanced analytics platforms provide these capabilities, ensuring that data scientists can focus on high-value tasks.
DevOps teams benefit from analytics platforms that support the development of analytical products. This includes embedding dashboards in customer-facing applications, building real-time analytics capabilities, deploying edge analytics, and integrating machine learning models into workflow applications. These capabilities allow organizations to create more sophisticated and user-friendly applications.
Replacing siloed reporting systems with analytics platforms connected to integrated data lakes and warehouses is another common use case. Integrated data sources provide a more comprehensive view of the organization’s data, enabling better insights and decision-making.
Organizations must decide whether to use separate platforms for different use cases or consolidate their analytics solutions. Supporting multiple solutions can be costly and complex, but it may be necessary to meet diverse business needs. Finding the right balance requires careful consideration of productivity, speed, flexibility, and cost.
Reviewing Big Data Complexities
Analytics platforms differ in their ability to handle various data types, databases, and data processing requirements. Key considerations include:
Determine whether your organization primarily deals with structured data or requires text analytics on unstructured data. Structured data, such as SQL databases, is typically easier to manage, while unstructured data, like text or images, requires more advanced processing capabilities.
Review your current data integration and management architectures and project an ideal future state. Analytics platforms should address both current and future needs, including data cleansing, preparation, and wrangling tasks. Consider what data processing capabilities are necessary within the analytics platform.
Data provenance, privacy, and security requirements are critical, especially when using SaaS analytics solutions. Ensure that the platform can handle these requirements, including authorization, encryption, data masking, and auditing.
Consider the scale of your data and acceptable time lags from data capture to availability. Analytics platforms must handle large volumes of data efficiently and provide timely insights.
With the growing interest in generative AI, it’s essential to establish a consistent operating model for analytics solutions that may serve as sources for large language models (LLMs) and retrieval-augmented generation (RAG). Ensure that the platform can govern AI policies, processes, and practices with data assets.
Capturing End-User Responsibilities and Skills
Understanding the responsibilities and skills of end users is crucial when deploying analytics tools. Different user personas have unique requirements:
Citizen data scientists prioritize ease of use and the ability to analyze data, create dashboards, and perform enhancements quickly. Platforms should offer intuitive interfaces and user-friendly tools to support these users.
Professional data scientists focus on models, analytics, and visualizations. They rely on dataops for integrations and data engineers for data preparation. Collaboration and role-based controls are essential for larger organizations, while smaller organizations may seek platforms that empower multi-disciplined data scientists.
Developers require APIs, embedding tools, JavaScript enhancement options, and extension capabilities for integrating dashboards and models into applications. The platform should support seamless integration with other development tools.
IT operations teams need tools to identify slow performance, processing errors, and other operational issues. The platform should provide robust monitoring and diagnostic capabilities.
Review current data governance policies, including data entitlements, confidentiality, and provenance. Evaluate the platform’s flexibility in creating row, column, and role-based access controls. Ensure that the platform meets data security requirements and integrates with third-party data catalogs if necessary.
Prioritizing Functional Requirements
Having a prioritized functionality list helps separate must-have features from nice-to-haves. Key areas to consider include:
Some platforms enable using prompts and natural language to query data and produce dashboards. This can be a powerful tool for deploying analytics to larger, less-skilled user communities. Generative AI also allows generating text summaries from data sets, dashboards, or models, highlighting trends and outliers. Organizations increasingly embed query and analytics capabilities into customer-facing applications and employee workflows. The platform should support intuitive integration and provide sophisticated analytics within the user experience.
Consider the full spectrum of analytic and AI use cases you need to support, both now and in the future. This includes filtering on text, performing aggregations, and incorporating geospatial search to limit analytics to regions of interest.
Specifying Non-Functional Technical Requirements
Non-functional requirements encompass performance objectives, machine learning and generative AI model flexibilities, security requirements, cloud flexibilities, and other operational factors.
Set clear performance objectives and evaluate the platform’s ability to handle large data volumes and complex analytics tasks. Ensure that the platform can scale as your data grows.
Evaluate the platform’s security features, including encryption, role-based access controls, and data masking. Ensure that the platform meets your organization’s compliance requirements.
Consider the platform’s support for multi-cloud environments and various generative AI frameworks. Flexibility in cloud deployment can enhance operational efficiency and reduce costs.
Ease of implementation and integration with your existing tech stack are crucial. The platform should not generate unnecessary costs or consume excessive resources. Evaluate the onboarding process, available educational materials, and vendor support.
Open-source solutions reduce exposure to vendor lock-in and offer greater portability. They provide the flexibility to scale as your data grows while maintaining performance.
Estimating Costs Beyond Pricing
Pricing for analytics platforms can be complex, with factors including the number of end users, data volumes, and functionality levels. Consider the total cost of ownership, including implementation, training, and support.
Vendor pricing for the platform can be a small component of the total cost. Consider productivity factors, as some platforms focus on ease of use while others target comprehensive functionality. Evaluate long-term costs and potential hidden expenses.
Choose a platform that enhances productivity and efficiency, balancing ease of use with advanced capabilities. Ensure that the platform can scale with your organization’s needs without sacrificing performance.
Conclusion
Selecting the right data analytics and machine learning platform is a complex but crucial task for any data-driven organization. By understanding the evolving landscape of analytics platforms, identifying your business use cases, reviewing data complexities, capturing end-user responsibilities and skills, prioritizing functional requirements, specifying non-functional technical requirements, and estimating costs beyond pricing, you can make an informed decision that supports your organization’s goals.
Remember, the ideal platform should not only meet your current needs but also be flexible enough to adapt to future requirements. By following these seven steps, you can ensure that your chosen platform will empower your organization to leverage data effectively, drive smarter decision-making, and stay competitive in an increasingly data-driven world.
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