Last updated on Feb 24, 2024

You’re a product engineer looking for the best analytics tools. What features should you prioritize?

As a product engineer, you need to measure and optimize the performance, usability, and value of your products. To do that, you need the best analytics tools that can help you collect, analyze, and act on data from your users and systems. But with so many options available, how do you choose the right one for your needs? Here are some features that you should prioritize when looking for the best analytics tools for product engineering.

1 Data collection

The first feature that you need to look for is the ability to collect data from various sources, such as web, mobile, server, and third-party platforms. You want to have a comprehensive and consistent view of your users' behavior, preferences, and feedback across different channels and devices. You also want to have flexibility and control over how you define and capture events, properties, and attributes that are relevant to your product goals and metrics.

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John Keller

Corporate Technical Ladder, Manufacturing Engineering Director, Yield and PCM
What I've found is you need to be able to collect data across the manufacturing chain from raw materials to final test data. You also need the analytic skills to choose the correct statistical tools for your data set(most data is not normally distributed). Finally, you need to be able to visualize data effectively and communicate to management who is not necessarily knowledgeable in this area.
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Shaily Verma

Head of Data | Insights | Data Science | Data Architect, AI / LLM, Data Strategy | Advanced Analytics |
Hey there, as a product engineer hunting for analytics tools, focus on ones that gather data effectively, offer real-time reports, and let you customize dashboards. Look for tools that play nice with your current systems and offer advanced features like predictive analytics. Make sure they can handle growing data loads, keep your info safe, and provide solid customer support. These features will help you make informed decisions and improve your product.
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Amit Gupta

Senior Vice President - Engineering | Fintech | BFSI | Digital Lending | SaaS | Java | AWS | AI
As we explore analytics tools as product engineers, our focus should be on solutions with versatile data collection methods, seamless integration, and robust validation. We should look for tools offering real-time collection, scalability, and customizable parameters for tailored data insights. Prioritizing those emphasizing security measures is crucial for compliance with data privacy regulations. A user-friendly interface, advanced analytics capabilities, and collaboration features are vital for deriving actionable insights. We should choose tools aligned with our product engineering goals, facilitating efficient data extraction, processing, and analysis.
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2 Data analysis

The second feature that you need to look for is the ability to analyze data in a fast, accurate, and scalable way. You want to have access to powerful and user-friendly tools that can help you perform queries, calculations, segmentation, funnel analysis, cohort analysis, retention analysis, and more. You also want to have the option to use advanced techniques, such as machine learning, predictive analytics, and experimentation, to uncover insights and optimize your product decisions.

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3 Data visualization

The third feature that you need to look for is the ability to visualize data in a clear, interactive, and customizable way. You want to have tools that can help you create and share dashboards, reports, charts, graphs, and maps that can communicate your data findings and stories effectively. You also want to have the ability to customize and embed your data visualizations into your product interfaces, websites, and applications, to enhance the user experience and engagement.

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Charu P.

Product @Tekion | Top Product Management Voice | Mentor | Beam Suntory | IIM Lucknow | Hindu College | Miranda House | Amazon PPI
Data visualization makes complex data easy to understand by presenting it visually. This helps product managers see trends and patterns clearly. It also helps to spot opportunities and challenges in the product performance. Tools like Tableau and Power BI are great for this. They offer many ways to visualize data, like charts and graphs, making it easier to analyze and act on
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4 Data action

The fourth feature that you need to look for is the ability to act on data in a timely, automated, and personalized way. You want to have tools that can help you trigger actions based on data insights, such as sending notifications, emails, messages, or surveys to your users, or changing the product features, content, or layout dynamically. You also want to have the ability to personalize and segment your actions based on user attributes, behavior, and feedback, to increase the relevance and impact of your data-driven interventions.

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5 Data security

The fifth feature that you need to look for is the ability to secure data in a compliant, ethical, and transparent way. You want to have tools that can help you protect your data from unauthorized access, loss, or corruption, by using encryption, authentication, backup, and recovery methods. You also want to have tools that can help you comply with the data privacy and regulation laws, such as GDPR and CCPA, by allowing you to manage user consent, anonymize data, and delete data upon request.

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6 Data integration

The sixth feature that you need to look for is the ability to integrate data with other tools and platforms that you use for product engineering. You want to have tools that can help you connect your data sources and destinations seamlessly, by using APIs, SDKs, webhooks, or connectors. You also want to have tools that can help you sync your data with your product development, marketing, sales, and support tools, such as Jira, GitHub, Slack, Salesforce, and Zendesk, to enable cross-functional collaboration and alignment.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Anthony Branda, MBA, PhD, CAP

Chief AI & Data Analytics Officer
Data visualization is often misconstrued as a project to create a few tactical dashboards; the most successful organizations at Data Visualization take a programmatic view of data visualization by creating a platform, governance, and a team for self-service analytics. This is imperative to create scaleable access to data, the ability to generate more automated insights, and to make sure the teams with the right fit-for-purpose skill sets are present to help facilitate the power users and ultimately to create a citizen data science paradigm. For example, in Self Service Analytics, the firm takes a programmatic view of Gen AI and Insight Generation and ensures these capabilities are deployed at scale.
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You’re a product engineer looking for the best analytics tools. What features should you prioritize?

1

2

3

4

5

6

7

1 Data collection

2 Data analysis

3 Data visualization

4 Data action

5 Data security

6 Data integration

7 Here’s what else to consider

Product Engineering

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You’re a product engineer looking for the best analytics tools. What features should you prioritize?

1

2

3

4

5

6

7

1 Data collection

2 Data analysis

3 Data visualization

4 Data action

5 Data security

6 Data integration

7 Here’s what else to consider

Product Engineering

Rate this article

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