a post for students in my database course

Effective communication is paramount in the field of data analytics, and often, the culmination of a data professional’s hard work is presented in a report. These reports are not merely collections of numbers; they are powerful tools designed to translate complex data into actionable insights, enabling informed decision-making across various organizational levels. Creating impactful reports involves understanding different report types, following a structured development process, considering the audience and specific requirements, incorporating essential elements, and applying thoughtful design principles.

The Diverse World of Reports

Reports come in many forms, each serving a unique purpose and catering to different needs. Understanding these distinctions is crucial for selecting the most appropriate reporting method for any given situation.

Static vs. Dynamic Reports

Reports can broadly be categorized as either static (point-in-time) or dynamic (real-time). The key difference lies in their connection to the original data.

• Static Reports are like a snapshot of data at a specific moment. Once the data collection stops, the report becomes static. They allow for more in-depth and complicated analyses, can answer more complex questions, and often preempt follow-up inquiries. While they can be generated quickly, they become outdated as data changes, often requiring regeneration for updated information. Static reports are ideal when detailed analysis is more important than real-time updates, or for one-time questions that don’t require continuous monitoring.
• Dynamic Reports, on the other hand, maintain a live connection to the data and update automatically whenever the underlying data changes. They act as a “live stream” of information, typically delivered through dashboards or web services. Their main advantage is providing the most up-to-date information possible, saving time in the long run once set up. However, dynamic reports are often limited to simpler analyses and questions and can take considerable time to initially configure. They are essential in situations where real-time data is critical, such as stock trading.

It’s important to recognize that both static and dynamic reports have their distinct roles and are not interchangeable. They fulfill different needs in the data reporting landscape.

Ad Hoc vs. Research Reports

These types of reports differentiate based on their purpose, depth, and turnaround time.

• Ad Hoc Reports are short, quick reports designed to answer a simple, often one-time question. They are typically static due to their short turnaround time and the nature of the questions they address. Ad hoc reports are concise, providing direct results and brief interpretations without lengthy explanations. For instance, determining whether a new sanitizing product reduced sick days or which coffee brand is preferred.
• Research Reports, also known as tactical reports, are large, in-depth reports that tackle complicated business questions using advanced statistical analysis. They are invariably static and can take significant time and effort to generate, often requiring new data collection or extensive studies. These reports aim to answer major strategic questions that could impact the overall direction or operations of a company, such as evaluating mergers or changes in flagship products.

Self-Service Reports

Self-service reports are increasingly common, particularly through dashboard reports. These reports utilize software, applications, or web services to provide limited access to specific metrics, data, or visualizations. Their popularity stems from empowering non-data specialists, such as mortgage agents, to quickly access the information they need without relying on data professionals for every query. Many dynamic reports are self-service, offering live data streams, though static dashboards that require manual refreshes are also common. Self-service reports typically feature simple analyses and visualizations of key metrics.

Recurring Reports

Recurring reports are those that are produced and delivered at regular intervals. They are generally static and often presented at meetings to facilitate discussion and decision-making. Common categories of recurring reports include:

• Compliance reports—These ensure adherence to regulations, which can be internal, third-party, industry-specific, or governmental. They confirm that the company is meeting all imposed requirements.
• Risk and regulatory reports—These focus on identifying and analyzing potential risks, often related to changes in regulations or their financial impact.
• Operational reports (KPI reports)—The most common type of recurring report, these provide a regular check on the current state of normal operations, frequently focusing on Key Performance Indicators (KPIs). KPIs are metrics used to gauge performance and progress towards goals, such as Return on Investment (ROI).

The Report Development Process

Creating a report typically follows a structured, four-step process, especially for dashboards and research reports, though ad hoc reports may have a less formal approach due to their quick turnaround time.

1. Create a plan–This initial phase involves outlining how the data will be presented and what questions it aims to answer. This can involve mockups or wireframes for visual layout, and data story planning to determine the most effective order and message for presenting findings.
2. Get the plan approved—Before significant time and resources are invested, the plan is presented to stakeholders (e.g., a manager or the person who requested the report) for approval. This step ensures alignment on the report’s objectives and presentation, preventing miscommunications and rework later on.
3. Create the report—This is the hands-on phase where data is gathered, cleaned, wrangled, analyzed, and finally integrated into the report. The specific actions here depend heavily on the type of report and its content.
4. Deliver the report—The final step involves presenting the findings to the intended audience. This could mean deploying a dashboard for live access, presenting results in a meeting, or simply sharing a document. The goal is to communicate the insights effectively so that informed decisions can be made.

It is important to note that if major changes are identified during the report creation, it’s best practice to return to the approval step.

Key Considerations When Making a Report

When crafting a report, several critical factors must be taken into account to ensure its effectiveness and relevance.

Business Requirements

These considerations help translate initial requests into a functional report.

• Data content—This refers to which variables, tables, or visualizations are included in the report. It’s crucial to include only the information necessary to answer the question, avoiding extraneous data that can muddle the message. For instance, a report tracking robot speed would need distance, time, and date variables to calculate and compare speed over time.
• Filtering–Filters narrow down the data to only the required content. This can occur during querying (when pulling data) or report generation (when selecting data for presentation). For dashboards, filters can be interactive elements, allowing the audience to customize their view.
• Views—In some data tools like SQL or Power BI, a “view” saves the result of a query as a separate, READ-ONLY data object. Views are primarily used for cleaner code (avoiding long, slow queries) and data integrity/security, as they allow users to access data without being able to modify the original database.
• Data range—This defines how much data is included, such as date ranges, geographical areas, or departmental data. The specificity of the question typically dictates the data range: highly specific questions lead to smaller data ranges, while vague questions may require broader ranges.
• Frequency—For recurring reports, frequency dictates how often the report is generated (e.g., yearly, quarterly, monthly, weekly, daily). Ideally, the reporting frequency should be one unit of time smaller than the goal being tracked (e.g., monthly reports for quarterly goals), though in practice, it often aligns with the goal period.
• Audience—The audience consists of the individuals who will receive and utilize the report. It’s a fundamental rule to only give reports to those who need them to avoid wasting time, causing confusion, or potentially leaking confidential information. Different audience types (or consumer levels) have varying access needs and preferences for information detail:
  • C-level executives (e.g., CEO, CFO)—Require high-level summaries with broad data ranges (e.g., year-to-year performance) and have high access to confidential information.
  • Management—Need mid-level information, often summarized but with some granular detail pertinent to their team or department, usually on a monthly or quarterly basis.
  • Stakeholders (e.g., investors)—Generally have mid-level access but receive highly summarized information with broad data ranges, primarily to understand the company’s overall financial health.
  • Technical experts (e.g., data analysts, specialists)—Need low to mid-level access and receive very detailed, granular data for specific, focused work, often requiring information in smaller time units like days or hours.
  • General public—Have  low to no access and receive extremely high-level, often vague announcements (e.g., “we are growing”). Information shared with this audience is highly scrutinized.

Dashboard-Specific Requirements

When creating dashboards, specific technical considerations arise:

• Data sources—This involves identifying the databases from which data is pulled and how that data is connected to the dashboard. Connections can be:
  • Live—A dynamic, real-time connection.
  • Last extracted—A static connection, indicating when the data was last refreshed.
  • Embedded—The report is directly embedded into the data source and published together, meaning the source can only be used with that specific report.
• Data attributes—These are terms used within dashboard environments to describe variables:
  • Dimensions—Represent qualitative or categorical variables that break data into groups.
  • Measures—Represent quantitative or numerical variables that hold counts, measurements, and calculations.
• Field Definitions: Provide specific information about each variable, including its data role, type, domain, aggregation method, and any formulas for calculated variables.

Essential Report Elements

Beyond the data itself, various elements contribute to a report’s completeness and usability.

• Cover page—The very first page of a report. For dashboards, it typically provides instructions on how to use the dashboard and navigate its features. For other reports, it may include a summary of observations and key insights, sometimes referred to as a “Bottom Line Up Front” (BLUF).
• Version number—Essential for reports that are regularly updated or changed. Each update should result in a new version number to ensure everyone refers to the same iteration of the report.
• Reference data sources—These indicate the data sources used in the report’s generation, ensuring transparency and traceability.
• Reference dates—Typically include two specific dates:
  • Report run date—The date the static report was actually generated.
  • Data refresh date—The last time the data was pulled for the report.
• Frequently Asked Questions (FAQs)—A section often included at the end of self-service or recurring reports to address common queries about the report’s content or usage.
• Appendix—A place for supplementary information that relates to the report but isn’t critical for immediate understanding. This could include code snippets, detailed definitions, or relevant regulations.

When a report is updated, elements like the cover page, version number, reference data sources, report run date, and data refresh date may all need to be updated accordingly.

Effective Report Delivery

The method of delivery and how the report is optimized for consumption are also crucial.

• When it is delivered:
  • Subscription: Allows a dashboard or report to be automatically refreshed and sent to chosen recipients (e.g., via email) at regular, scheduled intervals.
  • Scheduled Delivery: Similar to a subscription but involves a one-time delivery on a specific date.
• How it is delivered:
  • Web interface—Dashboards can be hosted on the web, often through third-party services, or locally on a company’s network.
  • Access permissions—Security features that limit who can view the dashboard, crucial for protecting confidential information.
  • Static—Refers to the connection type; a static dashboard means the data won’t update automatically without manual intervention.
• How optimized it is:
  • Interactive saved searches / filtering—Allow the audience to customize their view, making the dashboard more flexible. However, increased interactivity can slow down the dashboard.
  • Dashboard optimization—Focuses on the speed and efficiency of the dashboard, which is often a balancing act between interactivity and performance.

Designing for Impact

The visual design of a report significantly influences how it is perceived and understood. A well-designed report commands attention and conveys professionalism.

• Branding—Company branding guidelines should always take precedence in report design. This includes using specific color codes, logos, watermarks, fonts, and layouts to ensure consistency with the company’s image.
• Fonts—Clarity and professionalism are key. Font size should be legible (preferably no smaller than 11pt). Line spacing and color contrast are also important for readability. Generally, it’s best to limit the number of different fonts to two or three to avoid a cluttered appearance.
• Layouts—Avoid overfilling pages or slides. White space is important for readability. Ideally, each slide or page should feature only one visualization, and each visualization should convey a single, clear data story. This enhances comprehension and avoids overwhelming the audience.
• Key chart elements—Every chart should have:
  • Titles—Accurate and concise names for the chart.
  • Labels—Identify elements like axes.
  • Legends—A table identifying other chart elements, especially colors or symbols. All these elements must be accurate and concise to prevent misinterpretation.
• Color Theory: Understanding which colors complement each other and which clash is essential for visual appeal and readability. If no company branding guidelines exist, prioritize colors that are easy on the eyes and do not cause strain.

Common Visualizations in Reports

Data visualization is a highly effective way to communicate complex information, often conveying meaning more powerfully than raw numbers. Reports frequently incorporate various chart types tailored to the data and message.

• Infographics—Used to explain broader concepts or flows that traditional charts might not capture. They are visual explanations, often containing other visualizations, designed for clear, self-explanatory messages.
• Word clouds—A visual representation of text data where word size indicates frequency or importance. Useful for quickly understanding the main themes or focus of a text, often employed in natural language processing.
• Bar charts:
  • Classic bar charts—Compare one quantitative variable against a qualitative variable.
  • Stacked bar charts—Compare two qualitative variables (or levels of one) against one quantitative variable, showing parts of a whole within each category.
• Histograms—Visualize the frequency distribution of a single numerical variable.
• Waterfall charts—Focus on the differences between bars, useful for tracking progress or changes over time, highlighting sequential positive or negative contributions.
• Line charts—Track changes in a quantitative variable over time, showing trends. Time is typically on the x-axis, and the numerical variable on the y-axis.
• Pie charts—Show a qualitative variable broken into percentages of a whole, ideal for proportions (e.g., demographic data).
• Scatter plots—Compare two quantitative variables to identify relationships, showing individual data points. A positive correlation implies both variables increase together, while a negative correlation implies one increases as the other decreases. It’s crucial to remember that correlation does not imply causation.
• Bubble charts—A variation of scatter plots that display three quantitative variables, with the third variable represented by the size of the bubbles.
• Mapping visualizations:
  • Heat maps—Compare two qualitative variables (often scales) against one quantitative variable, with color indicating the quantitative value. Useful for showing relationships across scales.
  • Tree maps—Depict nested or hierarchical qualitative variables with a quantitative variable, where box size represents the quantitative value and color can represent another qualitative level or an additional quantitative variable.
  • Geographic maps—Traditional maps that visualize data across geographical locations, depicting the relationship between elements based on spatial variables.

In conclusion, reporting is a cornerstone of data analytics, transforming raw data into clear, compelling narratives that drive decision-making. By mastering the various types of reports, adhering to a structured development process, carefully considering the audience and specific requirements, incorporating essential elements, and applying thoughtful design principles, data professionals can ensure their insights are not only accurate but also effectively communicated and understood.