Bridging the data gap: Effective reporting practices for cross-team collaboration

Whether you're a full-time data analyst, scientist, or product manager, analyzing data is just the first step of the work you do to support your stakeholders. 

The ultimate goal is to share insights with your peers and stakeholders in a way that drives decisive, strategic action. No matter how brilliant your analysis is, if it’s not presented in a format that’s accessible and understandable, its potential to make an impact is greatly diminished.

Below, we’ll explore best practices for sharing analyses with stakeholders and touch on the tools that can help you follow these practices. We’ll discuss why it’s important to meet your stakeholders where they are, ensure your analysis is traceable and reproducible, and iterate in short cycles to get faster feedback.

Note: This article focuses primarily on analyses that are ad hoc or exploratory (EDA) in nature. Analyses that must be delivered as a source-of-truth dashboard are ultimately best delivered in your business intelligence (BI) platform (which we discussed in another article here). 

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The importance of effective data sharing 

A successful data team does more than analyze data in isolation. They think critically about the business context and deliver reports that can actually impact decision-making. This means how you share your analysis can be just as important as the analysis itself. 

When you don’t (or can’t) share your analysis effectively, you and your org can lose opportunities to improve your product or way of operating and reduce the ROI of the programs your data supports.  

The tools you use and the way you present findings will vary depending on two main factors:

1. The nature of the analysis: Some analyses can be easily done in spreadsheets, but more complex machine learning models might require Python or R, making sharing results in a spreadsheet impractical.
2. Stakeholder preferences: Every stakeholder has their own preferred way of consuming data. While you might prefer an interactive dashboard, your stakeholder might be more comfortable with a simple spreadsheet or a slide deck. Some stakeholders will just want the answers, others will want to be able to explore the data on their own.

Understanding these factors is crucial to delivering insights in the most impactful way possible.

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Best practices for shareable data analysis

No matter how skilled you are at analysis, if no one beyond your data team knows how to act on the insights from it, you won’t be able to move the needle for your company. 

Creating shareable data analyses that truly resonate with stakeholders and help them make the next best choice requires more than just technical prowess. You need to hone a thoughtful approach around the needs of your audience, the right tools for the job, and the importance of clarity, collaboration, and flexibility. 

Below we’ll explore the six best practices for making sure your analyses are shareable, digestible, and actionable.

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1. Meet your stakeholders where they are

It’s easy to feel attached to your carefully designed dashboard or Python data app, but where you like to see the data presented may not be the easiest for your stakeholders to understand. A dashboard built in your favorite BI tool might end up as an exported spreadsheet or a screenshot embedded in a PowerPoint or Google slide.

While this can feel frustrating, it's a sign that your data is valuable. The key is to recognize what your stakeholder is trying to achieve and adapt your analysis to their needs. 

For example, if they primarily work in spreadsheets, solutions like the BigQuery Google Sheets connector or Python libraries that allow you to push DataFrames to Google Sheets could make your analysis more consumable. If the entire analysis can be done in a spreadsheet provided you just have the data, a solution like Coefficient may also be a good option. Delivering insights directly in your stakeholder’s desired workflow helps prevent losing insights in the steps between analysis and consumption. Creating a lossless analysis pipeline is critical in helping with traceability and reproducibility, which we’ll touch on in the following section. 

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2. Choose the right tools for the job

Choosing the right tools for data analysis is crucial for maximizing efficiency and insights. Here's an overview of main options (and here is the link to a recent post of ours that goes deep on how these tools compare):

• Business Intelligence (BI) platforms like Tableau, Power BI, and Looker excel at providing a single source of truth for key metrics. They're ideal for standardized reporting across organizations, offering powerful data visualization and dashboard creation. However, they can be inflexible for ad-hoc analyses and often require significant setup.
• Spreadsheets, while limited for complex analyses, are useful for quick, simple data manipulation. Tools like Coefficient enhance spreadsheet functionality, bridging the gap between basic Excel and more advanced BI platforms.
• Slack & chat notifications. They provide real time or scheduled summaries and updates directly in your team’s communication channels.
• Interactive Data Apps offer greater flexibility for complex analyses and data visualization. Popular options include Python-based tools (see: Streamlit, Plotly Dash), R-based tools (see: Shiny), and Jupyter Notebook plugins.
• Emerging Agile Platforms like Fabi.ai bridge the gap between technical and non-technical users. They combine the power of SQL, Python, and AI to offer efficient data analysis with a user-friendly interface, making advanced analytics more accessible.

When choosing a tool, consider factors like ease of use, interactivity, scalability, and your team's technical skills. While BI platforms offer standardization, interactive data apps and emerging agile platforms provide the flexibility needed for in-depth, exploratory analyses. The right choice depends on your specific needs, from historical reporting and predictive analyses to correlative analysis and ad-hoc investigations.

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3. Ensure traceability and reproducibility

Traceability (the ability to track your data’s origin) and reproducibility (the capacity to recreate the results of your analysis) are essential for data analysis, especially if you need to revisit the analysis later or explain your methodology. 

Beyond technical need, both play a vital role in the human element of making data-driven decisions. A broken link in your process can lead to confusion or mistrust of the data, especially when analyses are passed between team members or presented to less technical stakeholders.

A good BI tool can help you maintain traceability, especially if it supports version control. However, for more complex coding-based analyses, Python and R tools like Jupyter notebooks, R Shiny, or Binder can help keep things organized (we talk about various jupyter notebook plugins and tools to share analyses in another post). Using version control systems like Git ensures that the code behind your analysis is always available for reference or reproduction.

If your stakeholders are less technical, tools like Fabi.ai can help bridge the gap by allowing you to conduct your analysis in code while easily pushing the results to familiar formats like spreadsheets or Slack. This ensures traceability is maintained while still catering to their needs.

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4. Implement an iterative approach

In the same way product development requires iteration, building good practices around how you share analyses is an iterative process. Getting feedback early and often ensures you’re meeting your stakeholders’ needs and staying aligned with their goals. Shipping early also helps avoid wasted effort on building complex solutions that miss the mark.

That said, be mindful of your tools. While BI dashboards are great for centralized reporting, they can be cumbersome if you’re in the early stages of analysis. This is especially true when conducting exploratory data analysis. Consider using Jupyter notebooks, spreadsheets, or even simple Python scripts for initial exploration, allowing for quick changes based on feedback.

For iterative reporting that remains traceable and reproducible, Python data apps can offer a lightweight alternative to BI tools. Frameworks like Dash and Streamlit allow for interactive dashboarding with full control over your code and versioning. 

Note: These frameworks are highly flexible but may require more technical skills. If you’re looking for a quicker, less technical option, Fabi.ai offers the same flexibility and can be built faster thanks to its integrated AI tools.

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5. Design for clarity and engagement

Effective data sharing hinges on clear, engaging presentations that resonate with your stakeholders. 

There are a five data visualization best practices to follow: 

1. Choose appropriate chart types: Bar charts excel for comparisons, while line charts are ideal for trends over time. 
2. Use color purposefully: Limit color use to highlight key points and maintain consistency across your visuals.
3. Eliminate clutter: Organize information logically, use familiar navigation patterns, incorporate tooltips or expandable sections for extra context, and provide clear labels and instructions.
4. Balance depth of information with ease of understanding: Layer your information by presenting high-level insights upfront, with options to delve deeper. 
5. Adapt your visualizations to stakeholder preferences: Tailor your approach based on your audience's technical proficiency and role (e.g. executives might prefer high-level dashboards, while analysts may want more granular data views). 

Remember, the goal is to make your data not just accessible, but actionable. By designing with clarity and engagement in mind, you can help your stakeholders translate your insights into tangible next steps.

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6. Foster collaboration and flexibility

Successful data sharing is a collaborative effort that requires flexibility and continuous improvement. 

Start by incorporating feedback mechanisms into your data sharing process. This could look like adding comment features in your dashboards, regular feedback sessions, or surveys to collect feedback on how data is presented. 

Once you have rituals for collecting feedback, make sure you spend time implementing those lessons learned as you iterate. Adopting an agile approach to your data projects will help here by letting you break them into smaller, manageable chunks. As you build on past deliverables, make sure to version control your analyses and dashboards so it’s easy to track changes and revert if needed.

For your rituals and process around sharing analysis to last, you need to work on building a culture of data-driven decision making. Stakeholders should feel literate in your data work so they can comfortably make decisions based on it. To foster better data literacy across the company, consider hosting data training sessions and celebrate examples of data-driven strategic decision making as they happen in the wild. 

Lastly, as we touched on a bit in the last section, make sure you’re delivering insights where your stakeholders spend most of their time. Some people may prefer regular email reports, while others might want access to live dashboards. Be prepared to deliver your insights in multiple formats and stay flexible in your tooling choices.

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Real-world examples: The power of shareable, accessible analysis 

Let’s look at some common use cases across different verticals that demonstrate good, shareable analysis practices. 

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B2B: Manufacturing company streamlines supply chain

A global manufacturing firm faced challenges in managing its complex supply chain of multiple suppliers and production lines spanning multiple countries. 

• The data challenge: Integrating and analyzing vast amounts of data from diverse sources to identify inefficiencies and bottlenecks.
• The solution: Data team developed an interactive dashboard with drill-down capabilities, allowing stakeholders to explore data at various levels of granularity.
• The outcome: Making data more available in dashboards gave stakeholders more context for decisions around supplier negotiations and production scheduling, which helped to reduce inventory costs.

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B2C: E-commerce retailer optimizes customer experience

A fast growing e-commerce platform is struggling to get all their teams aligned around a new set of customer experience metrics and strategies for improving them. 

• The data challenge: Catering to stakeholders with varying technical skills, from data analysts to marketing managers and C-suite executives.
• The solution: Implementing a two-pronged approach combining Tableau dashboards for high-level insights and Google Sheets reports for detailed data exploration.
• The outcome: The flexible reporting system made it easy for each team to see how the company was tracking against the new metrics and quickly identify and respond to customer behavior trends to increase customer satisfaction. 

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SaaS: Project management tool improves feature prioritization

A SaaS company building a project management tool needed to balance user feedback with technical constraints to guide their product development roadmap.

• The data challenge: Synthesizing qualitative user feedback with quantitative usage data to inform feature prioritization.
• The solution: The data team developed a Python data app integrated with Slack, providing easy access to real-time user insights and feature performance metrics.
• The outcome: Making data insights available easily in Slack fostered better collaboration between product, engineering, and customer success teams, which helped them work together to drive better user engagement for new features.

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Putting it all together: Your roadmap to impactful data sharing

Being a successful data professional in today’s data driven world is just as much about your technical skills as it is the soft skills that help you translate your findings to your teammates. Throughout this article, we've explored six key best practices for making your data analysis truly shareable and impactful:

1. Meet your stakeholders where they are
2. Choose the right tools for the job
3. Ensure traceability and reproducibility
4. Implement an iterative approach
5. Design for clarity and engagement
6. Foster collaboration and flexibility

The common thread running through these practices is the need for a flexible, stakeholder-centric approach. By embracing these practices and leveraging the right tools, you can ensure all your hard analysis work doesn’t end up collecting digital dust in a folder or email somewhere down the line. 

The exact details of how these best practices apply to your organization and what tools you need to support them will vary. If you’re working with a mix of technical and nontechnical stakeholders and want to consolidate your analysis work into one platform, Fabi.ai is worth exploring. We’ve built the tool around a user-friendly interface that combines the power of SQL, Python, and AI so you can easily carry out efficient, shareable data analysis. 

It’s free to get started, and takes less than 5 minutes to set up. Create your free account and start exploring.