What Should Determine What I Put in a Data Analytics Report or Dashboard?

Creating a data analytics report or dashboard can be both an art and a science. It's not just about gathering data but presenting the right insights that will drive decision-making. Whether you’re reporting to executives, managers, or technical teams, what you include should be thoughtfully selected to make the information valuable and actionable. So, what exactly determines what goes into your data analytics report or dashboard, What data would make it valuable and actionable? Here are seven factors that I found from my research and a few months of learning.

1. Clear Business Objectives

2. Audience Requirements

3. Relevant KPIs and Metrics

4. Data Accuracy and Reliability

5. Historical vs. Real-Time Data

6. Visualization and Simplicity

7. Contextual Data and Comparisons

1. Clear Business Objectives

The most crucial factor in determining what goes into a data analytics report or dashboard is your business objective. Without a well-defined goal, your report could be a jumble of unrelated metrics. The content of your report should be aligned with the questions the business is trying to answer or the problems it seeks to solve.

For example, let’s say you’re working for a digital marketing company like PluralCord Digital, and the objective is to improve client engagement. Your dashboard might include metrics such as Website bounce rate, Time spent on site, Number of page views per session, Social media engagement (likes, shares, comments)

These KPIs (Key Performance Indicators) are directly tied to the goal of increasing engagement. By focusing on these metrics, you can clearly communicate the health of your digital engagement efforts.

Key questions to ask:

- What business problem are we trying to solve?

- What are the critical success factors or goals?

- How will this data influence business decisions?

2. Audience Requirements

The next key factor is understanding who will be using the report or dashboard. Different stakeholders have different needs, and this should determine the depth and complexity of the information included.

For instance:

- Executives may prefer a high-level overview with KPIs like revenue growth, customer satisfaction, and cost efficiency. They typically don’t need granular details, but they do need to see trends and projections.

Example for executives: A dashboard showing monthly revenue growth trends alongside a forecast for the next quarter.

- Middle managers need to track the day-to-day operational metrics. For a marketing manager, this might include campaign performance data, such as ad click-through rates (CTR), cost-per-click (CPC), and conversion rates.

Example for managers: A report highlighting the **performance of different marketing channels, broken down by cost per acquisition and return on ad spend (ROAS).

- Data teams require detailed metrics and the ability to drill down into raw data. They need insights that allow for deeper analysis, which can help refine campaigns or products.

?Example for data teams: A sales funnel report showing detailed metrics from the awareness stage down to conversions, with the ability to drill down into different customer segments.

Key questions to ask:

- Who is the primary audience for this report or dashboard?

- What level of detail will they need?

- How will they use the insights?

3. Relevant KPIs and Metrics

Once you know the business objective and the audience, the next step is selecting the right KPIs and metrics. Including too many metrics can overwhelm the audience and dilute the focus. The goal is to present data that is meaningful and actionable.

For example, if the objective is to optimize a sales funnel, relevant metrics might include Lead conversion rate, Customer acquisition cost (CAC), Average deal size, Sales cycle length

Each of these metrics directly impacts your ability to measure and optimize the sales process. Avoid adding irrelevant data, such as social media metrics, which may not be tied to the sales performance.

Key questions to ask:

- Which KPIs will provide insights into business goals?

- Which metrics can be actionable?

- Are there any vanity metrics that don’t drive decisions (e.g., page views without context)?

Creating a data analytics report or dashboard can be both an art and a science. It's not just about gathering data but presenting the right insights that will drive decision-making.

4. Data Accuracy and Reliability

A report or dashboard is only as good as the data that powers it. Data accuracy and reliability should be a top priority, as misleading data can lead to poor decision-making. It’s important to ensure that the data you include is clean, accurate, and relevant.

For example, imagine creating a dashboard for monitoring website performance. If your traffic data isn’t filtered to exclude internal users (employees), you may report inflated visitor numbers, leading to incorrect conclusions about marketing performance.

To avoid such pitfalls:

- Regularly validate your data sources to ensure accuracy.

- Use filters to exclude irrelevant data points (e.g., internal traffic, bots).

- Ensure the data is up-to-date to reflect current performance.

Key questions to ask:

- Is the data accurate and reliable?

- Is there any outdated or irrelevant data in the report?

- How frequently should the data be updated to reflect real-time performance?

5. Historical vs. Real-Time Data

Another crucial factor is determining whether your report needs to focus on historical trends or real-time data. The choice depends on the business context and the decisions that need to be made.

For example:

- Historical data: If you’re analyzing annual sales performance, a report might focus on trends over time, such as year-over-year revenue growth, customer acquisition, and retention rates.

Example: A quarterly sales performance report showing historical trends can help management plan for seasonal variations and forecast future revenue.

- Real-time data: In fast-paced environments, such as e-commerce or social media, real-time data is critical for tracking current campaigns. For example, a marketing dashboard that tracks ad performance by the hour can help optimize bids and adjust budgets in real-time.

Example: A real-time social media dashboard that tracks the impact of posts, engagement, and follower growth during a live campaign.

Key questions to ask:

- Does the audience need real-time data or historical analysis?

- What decisions are being made based on this data?

- Should the dashboard include both real-time and historical perspectives for better context?

A report or dashboard is only as good as the data that powers it. Data accuracy and reliability should be a top priority

6. Visualization and Simplicity

Once you have determined the business objectives, audience, and data points, the next step is visualization. How you present the data is just as important as the data itself. Cluttered dashboards filled with complicated graphs can overwhelm users, while simple, clear visuals help communicate insights quickly and effectively.

For example:

- Bar charts and line graphs: These are excellent for showing trends over time, such as revenue growth or website traffic.

Example: A line graph showing daily website traffic over a month is simple yet effective in illustrating a traffic spike after a particular campaign.

- Pie charts: These work well for showing proportions, such as market share or product performance.

Example: A pie chart that breaks down revenue by product line can help executives quickly see which products are performing best.

- Heatmaps: Heatmaps can show website or user behavior visually, making it easy to identify trends such as popular website areas or product pages.

Example: A heatmap of an e-commerce website can show where customers are most likely to drop off, indicating potential UX issues.

Key questions to ask:

- Are the visualizations clear and easy to interpret?

- Does the dashboard or report layout highlight the most critical insights?

- Are complex visuals necessary, or would simpler charts suffice?

7. Contextual Data and Comparisons

Context is key when interpreting data. Raw numbers without context can be misleading. Providing comparisons—whether to targets, benchmarks, or previous periods—helps paint a complete picture.

For example:

- Showing that your website traffic increased by 20% sounds impressive, but it’s more meaningful if you compare it to last month’s performance or the industry average.

?Example: A comparison of Q1 and Q2 website traffic, alongside the industry benchmark, shows not only growth but also whether that growth is competitive.

- Include target metrics or thresholds so viewers can instantly see whether a KPI is on track. For example, comparing current sales to a monthly quota lets managers know if they need to ramp up efforts.

Key questions to ask:

- How does this data compare to previous periods?

- Are we hitting our targets or industry benchmarks?

- Can I add context to help interpret the data?

Conclusion

The key to determining what should go into a data analytics report or dashboard lies in understanding the business goals**, identifying the target audience, and selecting relevant and accurate KPIs. Reports that are aligned with business objectives and designed with simplicity, accuracy, and clear visualizations will deliver actionable insights. Whether you're tracking digital marketing campaigns, sales funnels, or customer behavior, the most effective reports and dashboards will provide the right data at the right time for the right people.

By following these principles, data analysts can create reports and dashboards that not only inform but drive impactful decision-making.