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Hello again,
Last week we focused on Excel and if you missed out on that post then check it out via the link at the bottom of the page along with our other previous posts. This week we are looking at reports and dashboards and why they aren’t the same thing. We often hear people using the two words interchangeably and it can be quite confusing so check it out below to find out which term is to use in Power BI. We have also found some more interesting articles to have a read of this week.
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Reports and dashboards are two terms often used interchangeably. This post clarifies the key differences between them in Power BI and when to use each for the best results.
A Power BI dashboard is a single-page, often highly visual display that summarises key metrics from multiple reports, giving a quick, real-time overview of high-level data. Ideal for monitoring KPIs, dashboards use “tiles” to pull key visualisations (like charts, graphs, and metrics) from various sources into one space, allowing users to grasp the current state of operations at a glance. The single-page design is a deliberate choice, providing a snapshot that doesn’t overwhelm but rather highlights essential data points for fast decision-making.
Use a Dashboard When:
Real-Time Monitoring is Crucial: Dashboards update automatically if data sources are set to refresh, making them excellent for metrics that require continuous monitoring, such as sales figures or customer service metrics.
You Need an Overview: Dashboards provide a holistic view by pulling in information from different reports or datasets, making it easy to track KPIs across different departments or projects.
Interactivity is Limited: While dashboards can include interactive elements, they aren’t designed for deep exploration. A dashboard is best suited for users who need a quick update rather than detailed insights.
Reports in Power BI are multi-page documents that allow users to explore data more thoroughly. A report may contain various visualisations spread over several pages, offering users the flexibility to drill down into specifics and filter data dynamically. Reports can be created from a single dataset or multiple, depending on the complexity required, and are highly interactive—users can slice, filter, and drill through data for a deeper, more nuanced view.
Use a Report When:
Detailed Analysis is Required: Reports enable in-depth exploration, making them ideal when you need to understand the “why” behind trends or dive into segmented data.
Multiple Views are Necessary: With the ability to create multi-page reports, users can approach data from various perspectives, such as comparing different time periods or analysing data by specific regions.
Interactivity and Customisation are Important: Reports allow extensive customisation, from choosing specific fields to display to interactive drill-down features, which are essential for data analysts or stakeholders needing to interact with data.
If the goal is to provide a quick update that highlights a few key metrics, opt for a dashboard. This approach is effective for C-level executives or team leaders who need to stay on top of daily metrics without diving into the details. Alternatively, if your audience requires comprehensive data analysis, creating a report will allow them to explore the data independently, draw insights, and inform strategic decision-making.
Integrate Both: Often, the most effective strategy is to create reports with detailed analysis and summarise key insights on a dashboard.
Limit Visual Clutter: Whether in a report or dashboard, ensure each visual serves a purpose. Too many visuals dilute focus and can overwhelm the user.
Optimise for Real-Time Needs: For time-sensitive metrics, configure automatic data refresh to keep dashboards and reports up-to-date without manual input.
In summary, Power BI dashboards and reports are powerful tools, each with its own strengths. Dashboards are designed for quick, high-level monitoring, while reports allow for deeper data exploration. By choosing the right format based on your objectives, you can ensure your data is not only presented effectively but also empowers users to take action.
Data cleaning, often viewed as a tedious task, is essential to producing reliable, actionable insights. Whether you're working with customer data, sales figures, or operational metrics, clean data is crucial for accurate analysis. A systematic approach to data cleaning transforms raw, messy data into a trusted asset, setting the stage for powerful analytics.
Unclean data can compromise analytics efforts by introducing inaccuracies, skewing results, and creating mistrust. Common issues include missing values, duplicate entries, inconsistent formats, and outliers. By addressing these, you ensure that your analytics accurately reflect real trends, providing insights that decision-makers can confidently act upon.
Here’s a structured approach to cleaning data effectively:
Duplicates are often created when data is pulled from multiple sources. Duplicate customer entries, for example, can lead to double-counting, skewing customer insights. Detecting and removing duplicates is an essential first step that prevents inflated counts and preserves the integrity of your analysis.
Missing values present an incomplete picture, which can weaken analyses and distort trends. To address this, use strategies such as:
Filling in values: Use averages, medians, or other logical estimates based on the data context. For instance, if some sales figures are missing, you might use average sales as a substitute.
Filtering out entries: If missing data points are substantial and substituting them could lead to misleading insights, consider removing those entries.
Using pivot tables, you can quickly identify where data gaps exist. A pivot table summarising counts by key dimensions (e.g., by date or product category) reveals missing values in a dataset and ensures no gaps compromise your analysis.
Inconsistent data formats are another barrier to accurate analysis. Dates, for instance, may appear in various formats (e.g., DD/MM/YYYY or MM/DD/YYYY), and text data like names or addresses may use differing abbreviations. By standardising formats, you enable reliable grouping, filtering, and sorting. For example, using consistent date formats allows for accurate time-based analysis, preventing mismatches and ensuring clarity.
In datasets with time-based data, such as daily sales, it’s important to ensure all expected dates are represented. Pivot tables are an invaluable tool here:
Confirm missing dates: Create a pivot table with dates as rows and check for any missing days. If certain dates don’t appear, investigate whether these were non-operational days or if data is genuinely missing. For continuous data, such as time series, missing dates may indicate data entry errors.
Count rows per date: Another way to detect inconsistencies is by calculating the row count for each date or category or product in the pivot table. Unexpected variations in row counts might suggest data entry issues, such as certain dates having more or fewer records than expected. Such inconsistencies can skew analysis, so verify and adjust as necessary.
Outliers can signal data entry errors or significant, unusual events. For instance, an unexpectedly high sales figure might be due to a data entry error rather than a legitimate sales spike. Reviewing these anomalies ensures they don’t distort your analysis. Use filters or conditional formatting to highlight these values and verify their accuracy, adjusting as needed.
Relevance is a subtle but crucial part of data cleaning. Outdated or irrelevant records dilute the value of your analysis, making it harder to focus on meaningful insights. For instance, if building a retention model, outdated customer records may skew results. Regularly reviewing and removing irrelevant entries keeps your dataset streamlined and ensures insights are current.
Pivot tables allow for efficient data validation. Here’s how they enhance data cleaning:
Identify Missing Values: Use pivot tables to count occurrences of values across dimensions (such as dates or categories) to spot gaps or anomalies.
Verify Data Completeness by Date: By creating a pivot table with dates as rows, you can easily check for missing days and confirm each date has the correct number of records.
Highlight Inconsistencies: Pivot tables quickly reveal irregularities in data, such as dates with unusually high or low counts, helping you track down and resolve these issues.
For optimal data cleaning, consider these best practices:
Automate Where Possible: Automate repetitive tasks like removing duplicates or reformatting dates. Tools like Excel macros or Python scripts streamline these tasks, saving time and reducing errors.
Document Cleaning Steps: Clear documentation ensures that others can replicate your process and verify your work. This also makes it easier to update the dataset if new data arrives.
Apply Validation Rules: Set up validation rules at data entry points, such as constraints on acceptable values or required fields, to prevent errors at the source.
Data cleaning is foundational to producing high-quality, actionable insights. Using pivot tables to identify missing values, check for complete dates, and count rows for consistency enables a thorough, reliable data cleaning process. By investing time in this process, you lay the groundwork for analytics success, ensuring that your insights are accurate, relevant, and trustworthy. Embrace data cleaning as an art—it’s your gateway to meaningful analytics.
We round up some recent articles we think you will find interesting.
CEOs Struggle with Data Driven Decisions
A Confluent survey reveals that CEOs frequently struggle with data-driven decisions due to pressures for rapid choices and limited access to real-time data. Over half of surveyed executives rely on intuition, particularly in key areas like workforce and market expansion, citing challenges in accessing timely insights. Despite this, there is strong interest in real-time data capabilities: 97% of companies are investing in dashboards and data streaming, with leaders believing it will improve decision quality. Confluent highlights data streaming as essential for bridging the gap between fast and informed decision-making.
Read on here: https://technologymagazine.com/articles/confluent-why-do-ceos-struggle-with-data-driven-decisions
Forbes recently reviewed the best data visualisation tools
Forbes Advisor recently published a guide that ranks the best tools available, including Power BI, Tableau, and Google Data Studio. Each tool is reviewed based on ease of use, customisation options, and pricing, helping you decide which aligns best with your business needs.
The article describes Microsoft Power BI as the best overall data visualisation tool due to its affordability, Microsoft integration, and user-friendly interface.
Read on here: https://www.forbes.com/advisor/business/software/best-data-visualization-tools/
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