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Визуализация данных при помощи дашбордов и отчетов в Excel
Эта книга научит вас мыслить новыми категориями, а не просто представлять данные в виде строк и столбцов. Вы сможете перекинуть мостик от элементарной работы с данными к их систематизации и выстраиванию на их основе полноценных историй. Узнаете, как обращаться с выбросами и представлять информацию в наиболее выгодном свете, а также как в сжатом виде отображать огромные по объему данные. Наконец, освоите методы перехода от обычных рабочих листов с необработанными данными к визуально привлекательным отчетам и дашбордам, позволяющим принимать правильные решения.
Издание идеально подойдет менеджерам, специалистам по финансам, руководителям отделов и всем, кто работает с данными в Excel.
Издание идеально подойдет менеджерам, специалистам по финансам, руководителям отделов и всем, кто работает с данными в Excel.
338 pages, Hardcover
Published February 1, 2022
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31 people want to read
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August 18, 2024
"Data Visualization with Excel Dashboards and Reports" focuses on three main areas: the dashboard development lifecycle, Excel's capabilities in data querying, transformation, and visualization, and the use of purpose-driven visualization elements.
The first topic, though concise, offers practical advice that could be expanded into a comprehensive checklist. Before creating a dashboard, it's crucial to clarify the requirements with all relevant stakeholders: the dashboard requester, data provider, and end users. It's important to prevent any preconceived conclusions by framing them as a series of questions. Additionally, understanding the details of your data from the outset is essential—this includes knowing the source, whether it's internal or external, its aggregation level, who maintains it, and its refresh frequency. End users can play various roles, such as monitoring, making decisions based on feedback, or planning. The developer should establish iterative feedback checkpoints with users to save time and stay aware of any changes in business objectives.
The second topic delves into design principles and Excel functionalities for creating dashboards. It's recommended to break down data into layers to facilitate quick updates (sources, staging & analysis, presentation). Use Excel to create a wireframe mockup that adheres to key principles: keeping everything on a single screen, delivering messages within five seconds, ensuring data consistency, and using charts appropriately. Common Excel tools for data transformation include PivotTables, Worksheet functions (XLOOKUP, SUMPRODUCT, Array Formulas, PivotCharts), and organizing tables with Structured Table Referencing and Data Models. These design principles are illustrated through three case studies: monitoring a software project, tracking HR KPIs, and analyzing financial information and ratios.
The third area categorizes charts into three types: Comparisons (mostly changes over time), Compositions (parts of a whole), and Relationships. Comparison charts include line charts, panel charts for when there are too many lines, bar charts with differences indicated by arrows, dot/box plots for counts, bullet charts for progress tracking, and funnel charts for tracking progress through interdependent stages. Composition charts include pie and doughnut charts (with the latter being more recommended), sunburst charts for breaking down pie charts, waffle charts (like those used on GitHub for commits), stacked bar charts, histograms, waterfall charts, and treemap charts (like those used on stock boards). Lastly, relationship charts include scatter plots and bubble charts, which can capture an extra dimension with the size of the circles. The book also covers non-chart elements, such as custom number formatting, icons, color scales, data bars, and sparklines, which can be used to render visualizations within a single cell.
The first topic, though concise, offers practical advice that could be expanded into a comprehensive checklist. Before creating a dashboard, it's crucial to clarify the requirements with all relevant stakeholders: the dashboard requester, data provider, and end users. It's important to prevent any preconceived conclusions by framing them as a series of questions. Additionally, understanding the details of your data from the outset is essential—this includes knowing the source, whether it's internal or external, its aggregation level, who maintains it, and its refresh frequency. End users can play various roles, such as monitoring, making decisions based on feedback, or planning. The developer should establish iterative feedback checkpoints with users to save time and stay aware of any changes in business objectives.
The second topic delves into design principles and Excel functionalities for creating dashboards. It's recommended to break down data into layers to facilitate quick updates (sources, staging & analysis, presentation). Use Excel to create a wireframe mockup that adheres to key principles: keeping everything on a single screen, delivering messages within five seconds, ensuring data consistency, and using charts appropriately. Common Excel tools for data transformation include PivotTables, Worksheet functions (XLOOKUP, SUMPRODUCT, Array Formulas, PivotCharts), and organizing tables with Structured Table Referencing and Data Models. These design principles are illustrated through three case studies: monitoring a software project, tracking HR KPIs, and analyzing financial information and ratios.
The third area categorizes charts into three types: Comparisons (mostly changes over time), Compositions (parts of a whole), and Relationships. Comparison charts include line charts, panel charts for when there are too many lines, bar charts with differences indicated by arrows, dot/box plots for counts, bullet charts for progress tracking, and funnel charts for tracking progress through interdependent stages. Composition charts include pie and doughnut charts (with the latter being more recommended), sunburst charts for breaking down pie charts, waffle charts (like those used on GitHub for commits), stacked bar charts, histograms, waterfall charts, and treemap charts (like those used on stock boards). Lastly, relationship charts include scatter plots and bubble charts, which can capture an extra dimension with the size of the circles. The book also covers non-chart elements, such as custom number formatting, icons, color scales, data bars, and sparklines, which can be used to render visualizations within a single cell.
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