Introduction

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Sigma Computing is a robust cloud-based business intelligence and analytics platform that enables users to create, analyze, and visualize data in a collaborative environment. Sigma enables users of all skill levels to transform raw data into actionable insights. There are many ways data can be visualized within Sigma. The idea is that by choosing the right type of visualization, you will be able to garner real insights into the data trends and thereby make informed decisions through continuous improvements.

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Key Features

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• Spreadsheet-like interface
• Cloud-based analytics
• Realtime collaboration
• Direct data access
• Advanced analytics
• Scalable and secure data management
• Broad data integration capabilities

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Benefits of Using Sigma Computing

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• Predictive Analytics: Provides predictive insight by using AI and ML models and allows one to do advanced analytics on data.‍
• Advanced Analytics Integration: Supports integration of AI and machine learning tools for enhanced data analysis. ‍
• Powerful Data Modeling: Rich functionality to model complex metrics, calculations, and data models within. ‍
• Handles Large Volumes of Data: Keeps up with continuously growing volumes of data while sustaining performance.
• ‍Greater Adoption: More users within the team can access data analysis, hence encouraging a data-driven culture throughout the company.
• ‍Unified View of Data: The integration of data from several sources, aiming for a complete understanding of all data.
• ‍Governance Capabilities: Comprises functionality that ensures the quality of data, security, and compliance.

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Workbooks, Pages and Elements in Sigma

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Workbooks

Workbooks in Sigma Computing are a way to organize your analysis and visualizations. Workbooks are considered digital files hosting multiple pages, much like a spreadsheet file in Excel or Google Sheets.

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Pages

Pages are the individual tabs within a workbook. Like the tabs in a spreadsheet, each page can be used to show different views of your data.

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Elements

Elements are the objects that you place on a workbook page.

Element types include:

• Data elements: Tables, visualizations, and pivot tables  

• UI elements: Text, images, buttons, embeds, spacers and dividers  

• Control elements: Filters and parameters  

You can have an element in your workbook act as a data source for another element. We'll refer to the source element as the parent and the element making use of the data as the child.

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Types of Visualization in Sigma

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Visualization isn't just graphical elements to add oomph to an analysis; they add context to the data. They let you build, but more importantly, explore and view, your data in a more focused and digestible format. You would create each visualization to deliver specific data insights and answer key questions that will drive and empower better business decisions.

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Chart Requirements 

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When choosing a chart, take a moment to think about what you are trying to convey about your data.

Different charts highlight different aspects. Here are some types of visualizations in Sigma.

Bar chart

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A bar chart is a chart that depicts values of different categories using rectangular bars. It is very good at representing categorical data and depicting comparisons across groups. Bar charts are readable and an efficient way of summarizing big data. Bar charts are effective at showing comparisons in the size, frequency, or value of different categories or groups. The common types of bar charts include vertical, horizontal, stacked, and grouped; each has its fit for a particular need in visualization.  

Below is an example of a column chart using the tool Sigma Computing.

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Line Chart

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The line graph represents data trends across continuous intervals. It's particularly useful in representing time series information. It is simple to understand and allows for easy comparison between several sets of data. General uses include the performance of sales, the fluctuation of stock prices, temperature changes, and traffic flow on a website. In general, line graphs can provide clear and detailed insight into trends found in data, hence finding broad applicability in business, science, and finance-oriented uses.

Below is an example of a line chart using the Sigma Computing tool.

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Pie chart

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Pie charts show the proportion of a whole, and they are useful for a simple and fast comparison among categories. In fact, pie charts work best when there is a limited number of distinct categories with large differences. Other examples of uses include market shares, distribution of the budget, survey results, and population composition. It is a statistical diagram in the form of a circle divided into slices to demonstrate the proportion of numbers. Each slice indicates a category or part of the whole, with the size of each slice corresponding to the proportion of that category relative to the total.

For instance, a pie chart can clearly present the share of different brands in the market.

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Area Chart

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Area charts represent the trend of data over time or in categories through showing filled areas underlines for the cumulative effect of values. They really excel at highlighting magnitude and multiple data comparisons simultaneously, so they are quite well-suited for illustrating various changes in financial data, environmental trends, sales figures, and population studies. It colours or shades the area under a line, drawing attention to the size of the values and focusing on their accumulated total.

Below is a sample of the area chart using the Sigma Computing tool.

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KPI Chart

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A KPI chart serves as a graphical display of the most important measures against which to track organizational performance for improvement. It underlines the most critical indicators, based on which assessment of progress towards objectives and identification of improvement areas become easy. KPI charts immediately present complicated data in a simple and comprehensible manner so that all levels of stakeholders understand the performance trend and make competent decisions to ensure continuous improvement towards goal attainment. In Sigma, the KPI chart summarizes either the total value over a specified period, the comparison of that value over time, or measures it against a benchmark or target.

Below, you have an example of a KPI chart created using the Sigma Computing tool.

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Now, some of the charts built in Sigma are as follows:

Charts are like visualization tools that make complex information simple and understandable. Charts allow comparing, finding trends, and showing visual proportions or relationships between items. A suitable type of chart removes ambiguity and gives an overview to present information better.

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User Requirements

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While specifying user requirements for the dashboard functionalities of Sigma Computing, focus on the following:

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• Customizability through drag-and-drop and customizable widgets to suit personalized business needs.
• Real-time refresh of data and interactive visualizations by using charts and graphs, providing dynamic insights through ad-hoc analysis.
• Mobile-friendly and collaborative by way of shared dashboards and scheduled reporting.
• Performance and scalability are ensured, optimized loading times for dashboards, and strong security features ensure data privacy and adherence to regulatory laws.
• This provides the organization with actionable insights in making quick yet efficient decisions.

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Benefits of Using Sigma Visualization

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Sigma Computing is a cloud-native analytics platform designed for collaboration in real time. With Sigma Computing, users are accorded self-service, in which less technical users can independently create and surface insights from data. A strong module for data governance assures security and compliance, while interactive visualization extends data exploration and knowledge. All in all, Sigma Computing allows agile and efficient work with data analysis across modern businesses.

Best Practices in the Implementation of Sigma Visualization

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Best Practices to Get the Most Out of Sigma's Visualization Capabilities are

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• Effectively use the visualization features of Sigma by first choosing an appropriate chart type that will best present your data and business outcome so that it is clear and relevant.
• Keep the visualizations simple and focused on one key message, avoiding the overwhelm of your audience.
• Use colour effectively, but make sure there's enough colour contrast to maintain readability for those with colour blindness.
• Clearly label all aspects of your visualization’s axes, legends, and titles so context and understanding of the data are clear.
• As such, following these best practices will fully realize the tools at hand with Sigma for delivery of accessible and actionable insights that drive informed decisions.

Summary

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In the end, Sigma Computing stands tall to provide high-level computation and visualization of data. Sigma intuitively holds several powerful features in its design, making it a must-have for those intent on digging deep into their data. Transform your data into action with the power of Sigma. Whether quality monitoring, distribution analysis, or relationships, these sigma visualizations will help raise your level of understanding and productivity in data-driven environments.

For those who want to know more about the Sigma Computing Analytical Tool, refer to the Sigma Computing Documentation.

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