How to Choose a Data Analytics and Machine Learning Platform

Selecting the right data analytics and machine learning platform is crucial for businesses looking to harness the power of data. This comprehensive guide will explore the capabilities and differences of modern data visualization, data analytics, and data science platforms. It will also outline seven critical steps to help you choose the best platform for your needs. With detailed analysis, practical advice, and real-world examples, this guide aims to be a valuable resource for businesses of all sizes.

Evolution of Analytics Platforms

Analytics platforms have significantly evolved over the past decade. They have moved beyond the traditional on-premises reporting and business intelligence (BI) tools to offer a range of advanced features, including modern data visualization, dashboarding, and machine learning capabilities. These platforms cater to various business use cases, end-user personas, and data complexities, making them indispensable in today’s competitive landscape.

Despite widespread adoption, many businesses, especially in lagging industries, are still looking to develop their first dashboards and predictive analytics capabilities. They recognize the limitations of managing analytics in spreadsheets, such as being slow, error-prone, and difficult to scale. Additionally, reporting solutions tied to a single enterprise system can be restrictive without integrations with other data sources.

Large enterprises often find themselves in a position where different departments have selected their own analytics tools. This situation can lead to inefficiencies and the need for consolidation into fewer platforms. Enterprises seek analytics platforms that support collaboration between business users, data operations engineers, data scientists, and others involved in the data visualization, analytics, and model operations lifecycle. Moreover, as organizations become more data-driven, addressing compliance and data governance within analytics workflows becomes a critical requirement.

Identify Business Use Cases for Analytics

The first step in choosing the right analytics platform is identifying your business use cases for analytics. Businesses strive to become data-driven organizations, using data, predictive analytics, and machine learning models to aid decision-making. This overarching goal drives several use cases:

  • Empowering business users to become citizen data scientists, enabling smarter decision-making through data visualizations, dashboards, reports, and other analytics capabilities.
  • Increasing the productivity of professional data scientists throughout the machine learning lifecycle, including discovering new data sets, evolving models, deploying them to production, monitoring performance, and supporting retraining efforts.
  • Enabling development teams to create analytical products, such as embedding dashboards in customer-facing applications, building real-time analytics capabilities, deploying edge analytics, and integrating machine learning models into workflow applications.
  • Replacing siloed reporting systems with analytics platforms connected to integrated data lakes and warehouses.

Organizations often grapple with whether to use separate platforms for these different use cases or support multiple solutions. According to Helena Schwenk, VP in the chief data and analytics office at Exasol, “Organizations are trying to do more with less and often have to compromise on their data analytics platform, resulting in a myriad of data management challenges, including slow processing times, inability to scale, vendor lock-in, and exponential costs.”

Optimal solutions require a thorough investigation into data, organizational, functional, operational, and compliance factors. For instance, it’s essential to evaluate if a single platform can meet diverse needs without compromising on performance or flexibility.

Review Big Data Complexities

Analytics platforms vary in their ability to handle different data types, databases, and data processing requirements. As Colleen Tartow, field CTO and head of strategy at VAST Data, notes, “The choice of data analytics platform should be driven by the current and future use cases for data within the organization, particularly in light of the recent advances in deep learning and AI.”

Data science, engineering, and data operations teams should review their current data integration and management architectures and project an idealized future state. Key considerations include:

  • The types of data sources (structured, unstructured) you plan to analyze.
  • The types of databases (SQL, NoSQL, document, columnar, vector) you will connect to.
  • Integration requirements with SaaS platforms and whether the analytics platform should handle these integrations.
  • The extent to which data cleansing, prepping, and wrangling tasks are needed within the analytics platform.
  • Data provenance, privacy, and security requirements, especially for SaaS solutions that store or cache data.
  • The scale of data and acceptable time lags from data capture to availability in analytics platforms.

Given the growing interest in generative AI capabilities, establishing a consistent operating model for analytics solutions that may serve as a source for large language models (LLMs) and retrieval-augmented generation (RAG) is vital. Daniel Yu, SVP of solution management and product marketing at SAP Data and Analytics, emphasizes, “Integrating generative AI within a business hinges on a solid foundation of trusted and governed data, and selecting a data analytics platform that can adeptly govern AI policies, processes, and practices with data assets is indispensable.”

Capture End-User Responsibilities and Skills

Understanding the responsibilities and skills of end users is critical when deploying analytics tools. Organizations must consider how these tools will be used and governed to avoid issues such as spreadsheet disasters, duplicate data sources, data leakage, and data silos. Common end-user personas include:

  • Citizen Data Scientists: They prioritize ease of use and the ability to analyze data, create dashboards, and perform enhancements quickly.
  • Professional Data Scientists: They focus on modeling, analytics, and visualizations, relying on data operations for integrations and data engineers for data prep work.
  • Developers: They require APIs, embedding tools, JavaScript enhancement options, and extension capabilities for integrating dashboards and models into applications.
  • IT Operations Teams: They need tools to identify performance issues, processing errors, and other operational challenges.

Governance considerations include reviewing current data governance policies, evaluating platform flexibilities in creating access controls, and ensuring data security requirements are met. The analytics platform should fit the organization’s operating model, especially when access is provided to multiple departments and business units.

Prioritize Functional Requirements

When selecting an analytics platform, it’s essential to prioritize functional requirements. While vendors may impress with their latest capabilities, having a clear list of must-have features helps distinguish essential functionality from nice-to-have options. According to Dhruba Borthakur, co-founder and CTO of Rockset, “In choosing a data analytics platform, it is important to think through the full spectrum of analytic and AI use cases you’ll need to support both now and in the future.”

Key areas to consider include:

  • Generative AI capabilities: Some platforms enable using natural language prompts to query data and produce dashboards, which is useful for larger, less-skilled user communities.
  • Text summaries: Generating text summaries from data sets, dashboards, or models to highlight trends and outliers.
  • Embedding analytics: Organizations are increasingly interested in embedding analytics capabilities directly into customer-facing applications and employee workflows.

Ariel Katz, CEO of Sisense, highlights the importance of embedding analytics, stating, “The fusion of AI innovation with the growing API economy is leading to a developer-focused shift, enabling intuitive and rich applications with sophisticated analytics embedded into the user experience.”

Specify Non-Functional Technical Requirements

Non-functional requirements are equally important and should include performance objectives, machine learning and generative AI model flexibilities, security requirements, cloud flexibilities, and other operational factors. Roy Sgan-Cohen, GM of AI, platforms, and data at Amdocs, advises, “Technical leaders should prioritize data platforms that offer multi-cloud support and various generative AI frameworks.”

Key considerations include:

  • Performance: Setting performance objectives to ensure the platform can handle data processing and analytics tasks efficiently.
  • Security: Evaluating security features, including authorization, encryption, data masking, and auditing.
  • Cloud Infrastructure: Considering multi-cloud support and the flexibility of the platform to integrate with other parts of the tech stack.
  • Implementation and Integration: Weighing implementation, training, and change management considerations.
  • Vendor Support: Assessing the level of vendor support, including onboarding processes, educational materials, and ongoing support.

Piotr Korzeniowski, COO of Piwik PRO, emphasizes the importance of ease of implementation and integration, noting, “When choosing the right analytics platform, consider ease of implementation and level of integration with the rest of the tech stack, and both should not generate unnecessary costs or consume too many resources.”

Estimate Costs Beyond Pricing

Finally, estimating costs beyond the platform’s pricing is crucial. While vendors’ pricing models may focus on the number of users, data volumes, and functionality levels, it’s essential to consider the total cost of ownership. This includes implementation, training, support, and productivity factors. Bennie Grant, COO of Percona, warns, “Open-source solutions reduce exposure to lock-in and favor portability, and having the flexibility of an open-source solution means you can easily scale as your data grows, all while maintaining peak performance.”

Some key factors to consider when estimating costs include:

  • Implementation Costs: The initial cost of setting up the platform and integrating it with existing systems.
  • Training and Support: Ongoing costs associated with training users and providing support.
  • Productivity: Evaluating how the platform’s ease of use and functionality impact productivity.
  • Scalability: Considering how the platform scales with your data and user base over time.

Final Thoughs

Choosing the right data analytics and machine learning platform is a complex but critical decision for any organization aiming to leverage data for better decision-making. By carefully evaluating business use cases, data complexities, end-user responsibilities, functional and non-functional requirements, and costs, businesses can find a platform that meets their needs both now and in the future. This guide provides a comprehensive framework to help you navigate this decision-making process, ensuring that your organization can unlock the full potential of its data.

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