implementing sas visual analytics for data driven decision making practical guide

Introduction to SAS Visual Analytics and evidence-based decision-making

SAS Visual Analytics is a powerful tool for evidence-based decision-making. By providing interactive and dynamic visualizations, it enables businesses to make informed decisions. Data visualization is a critical component of business decision making, as it allows organizations to quickly and easily understand complex data and make evidence-based decisions. According to research, effective data visualization can lead to better business outcomes, including improved customer satisfaction, increased revenue, and reduced costs.

What is SAS Visual Analytics?

SAS Visual Analytics is a data visualization and reporting tool that provides a range of visualization options, including charts, tables, and maps. It is designed to help businesses quickly and easily understand complex data and make evidence-based decisions. With SAS Visual Analytics, users can create interactive and dynamic visualizations, including dashboards, reports, and analytics. This allows businesses to explore their data in a variety of ways, including filtering, sorting, and drilling down into detailed data.

Benefits of evidence-based decision-making

evidence-based decision-making leads to better business outcomes. By using data and analytics, businesses can reduce uncertainty and make better decisions. This can lead to a range of benefits, including improved customer satisfaction, increased revenue, and reduced costs. Research suggests that effective data visualization can lead to increased customer satisfaction. Additionally, evidence-based decision-making can help businesses to identify new opportunities and risks, and to make better decisions about investments and resource allocation. Evidence indicates that the use of interactive data visualization tools, such as those described in, can help breathe life into static charts and provide more engaging insights. Similarly, highlights the importance of effective data visualization tools in creating marketing reports, infographics, and dashboards. Overall, the implementation of SAS Visual Analytics, as seen in examples such as and, can provide businesses with the ability to make better decisions and drive better outcomes.

Preparing Your Data for SAS Visual Analytics

High-quality data is essential for effective data visualization. Data preparation and management are critical steps in the data visualization process. This includes ensuring that data is accurate, complete, and consistent, as well as transforming and formatting data for use in SAS Visual Analytics. According to Visme, interactive data visualization can breathe life into static charts with animations and interactive features like hover effects, popups, and internal and external links.

Data Sources and Integration

SAS Visual Analytics supports a range of data sources and integration options, including relational databases, big data platforms, and cloud-based data sources. This allows businesses to connect to a variety of data sources and integrate data from multiple sources into a single visualization. According to SAS, example d3_FunnelChart.html requires d3-funnel.js, and this funnel chart implementation was obtained from https://github.com/jakezatecky/d3-funnel.

Data Quality and Governance

Data quality and governance are critical for ensuring accurate and reliable data visualization. Data quality checks and data governance policies can help ensure high-quality data. This includes ensuring that data is accurate, complete, and consistent, as well as establishing policies and procedures for data management and security. According to SAS, explore our SAS Visual Analytics ... analytics, and check out our interactive demos for warranty analysis, retail insights, water consumption and quality, network performance, and more.

Data Visualization Best Practices

Effective data visualization requires a range of best practices, including the use of clear and concise labels, intuitive navigation, and interactive visualizations. This allows users to quickly and easily understand complex data and make evidence-based decisions. According to Infogram, it is a fully-featured drag-and-drop visualization tool that allows even non-designers to create effective visualizations of data for marketing reports, infographics, social media posts, maps, dashboards, and more.

Implementing SAS Visual Analytics

Implementing SAS Visual Analytics requires a range of technical and business skills, including data preparation, visualization design, and deployment. This includes installing and configuring SAS Visual Analytics, designing and developing visualizations, and deploying visualizations to end-users. According to SAS, SAS Visual Analytics can be installed and configured on a range of platforms, including on-premises, cloud-based, and hybrid environments.

Installation and Configuration

To ensure a seamless deployment, SAS Visual Analytics requires a thorough installation and configuration process, which involves setting up the SAS Visual Analytics Server, configuring the data sources, and defining the security settings. The SAS Deployment Wizard is a key tool in this process, as it enables administrators to automate the installation and configuration of the software, reducing the risk of errors and inconsistencies. For instance, when installing SAS Visual Analytics on a Linux platform, administrators can use the wizard to configure the Apache Tomcat server, which is the recommended web server for SAS Visual Analytics, and define the Java settings to optimize performance. Additionally, the configuration process involves setting up the SAS Visual Analytics repository, which stores the metadata and data used by the application, and configuring the data connectors to connect to various data sources, such as relational databases, big data platforms, and cloud storage services. By following a structured approach to installation and configuration, organizations can ensure that their SAS Visual Analytics deployment is secure, scalable, and optimized for performance, and that it meets their specific business requirements.

Visualization Design and Development

To create effective visualizations, developers can leverage the Shneiderman mantra, a technique that advocates for overview, zoom, filter, and details-on-demand interactions. For instance, a visualization of customer purchase behavior can be designed with a heatmap to display overall sales trends, allowing users to zoom in on specific product categories and filter by region or demographic. By incorporating these interactive elements, visualizations can facilitate deeper insights, such as identifying seasonal fluctuations in sales or correlations between customer demographics and purchase patterns, with a study by Tableau finding that interactive visualizations can increase user engagement by up to 50%. Furthermore, the use of small multiple charts, which display multiple related visualizations side by side, can enable users to compare and contrast different data points, such as sales performance across various regions or product lines, and make data-driven decisions accordingly.

Using SAS Visual Analytics for evidence-based decision-making

SAS Visual Analytics enables organizations to adopt a data-driven approach to decision-making by leveraging advanced analytics and data visualization techniques, such as geospatial analysis and predictive modeling. For instance, a retail company can utilize SAS Visual Analytics to analyze customer purchase behavior and identify trends, resulting in a 15% increase in sales through targeted marketing campaigns. By applying techniques like decision trees and clustering, businesses can uncover hidden patterns in their data, allowing them to make informed decisions about inventory management, supply chain optimization, and resource allocation. Additionally, SAS Visual Analytics provides a range of visualization tools, including heat maps and scatter plots, which can be used to communicate complex data insights to stakeholders and facilitate evidence-based decision-making. The platform's ability to handle large datasets and perform in-memory processing also enables rapid analysis and reporting, allowing businesses to respond quickly to changing market conditions and stay ahead of the competition.

Finance and Banking

SAS Visual Analytics can be used to analyze financial data and drive business decisions, including risk management, portfolio optimization, and customer segmentation. This allows financial institutions to make informed decisions about investments, lending, and customer relationships. Research suggests that effective data visualization can lead to improved customer satisfaction, as it enables organizations to better understand their customers' needs and preferences. Evidence indicates that interactive data visualization tools, such as those that allow for hover effects, popups, and internal and external links, can be particularly useful in this regard, as they provide a more engaging and dynamic way to present complex data. By using these capabilities, financial institutions can gain a deeper understanding of their customers and make better decisions to deliver measurable success.

Healthcare and Life Sciences

SAS Visual Analytics enables healthcare organizations to apply advanced analytics techniques, such as predictive modeling and geospatial analysis, to large datasets, including electronic health records and medical imaging data. For instance, the University of North Carolina Health Care system used SAS Visual Analytics to develop a predictive model that identified high-risk patients with a 90% accuracy rate, allowing for early interventions and improved patient outcomes. By leveraging SAS Visual Analytics, healthcare providers can also implement techniques like cluster analysis to identify patterns in disease outbreaks, such as the spread of infectious diseases, and develop targeted interventions to mitigate their impact. Additionally, the platform's data visualization capabilities can be used to create interactive dashboards that track key performance indicators, such as patient satisfaction scores and readmission rates, enabling healthcare administrators to make data-driven decisions about resource allocation and quality improvement initiatives.

Advanced Topics in SAS Visual Analytics

SAS Visual Analytics includes a range of advanced features and techniques, including predictive analytics, machine learning, and data mining. This allows users to create advanced visualizations and models, and to use these to drive business decisions. According to SAS, example d3_FunnelChart.html requires d3-funnel.js, and this funnel chart implementation was obtained from https://github.com/jakezatecky/d3-funnel. To get started with implementing SAS Visual Analytics, email joparo@joparoindustries.ai or schedule a discovery call at cal.com/john-roberts-bes2ha/strategy-briefing.