The Summary Statistics dashboard includes rural and urban measures for roadway mileage, lane miles, vehicle miles traveled, fatalities, and fatality rate.

The Performance Dashboard (formerly Performance Outcomes System) datasets are developed to improve outcomes and inform beneficiaries who receive Medi-Cal Specialty Mental Health Services (SMHS). The intent of the dashboard is to gather information relevant to particular mental health outcomes, which will provide useful summary reports to help ensure ongoing quality improvement and to support decision making. Please note: the Excel file Performance Dashboard has been discontinued and replaced with the SMHS Performance Dashboards found on Behavioral Health Reporting (ca.gov).

The District of Columbia offers several interactive online visualizations highlighting data and information from various fields of interest such as crime statistics, public school profiles, detailed property information and more. The web visualizations in this group present data coming from agencies across the Government of the District of Columbia. Click each to read a brief introduction and to access the site. This app is embedded in https://opendata.dc.gov/pages/dashboards.

This is a dashboard that is created with the available data gathered from multiple sources through desk research for pilot regions in my PhD research. This dashboard is created as a spreadsheet where the list of information about baseline data was stored. The spreadsheet enabled the categorisation of available data based on the source and type of data, data collection method, frequency, etc, which is helpful to identify the boundaries in the model. Conducting desk research on the available datasets, statistics and reports could be useful to evaluate the quality of available information and identify any data gap that may need to be filled. This helped with the initial planning for future data collection to measure the key model outcomes such as visitor number, expenditure, and revenue as a result of the innovation scenarios testing that was conducted for the purpose of model demonstration.

This dashboard for local public health shares data on the 2022-2023 Capacity Assessment conducted among municipalities participating in the Public Health Excellence Shared Services Grant Program. These data represent health departments’ self-reported ability to meet the Performance Standards for local public health.

The Performance Dashboard (formerly Performance Outcomes System) datasets are developed in accordance with legislative mandates to improve outcomes and inform decision-making for beneficiaries receiving Medi-Cal Specialty Mental Health Services (SMHS). The intent of the Dashboard is to gather information relevant to particular mental health outcomes to provide useful summary reports for ongoing quality improvement and to support decision-making. Please note: the Excel file Performance Dashboard has been discontinued and replaced with the SMHS Performance Dashboards found on Behavioral Health Reporting (ca.gov).

This Dashboard is connected to our Comprehensive GIS Map to display interesting City statistics.

Real Time Dashboard Market Outlook

The global Real Time Dashboard market size was valued at approximately $12 billion in 2023 and is projected to reach $32 billion by 2032, growing at a CAGR of 11.5% during the forecast period. This robust growth can be attributed to the increasing demand for data-driven decision-making and the proliferation of business intelligence tools. Real-time dashboards are becoming indispensable tools across various industries, facilitating instantaneous data visualization and analytics, which drive operational efficiency and strategic planning.

One of the key growth factors driving the Real Time Dashboard market is the rising importance of data analytics in business operations. Organizations across diverse sectors are increasingly leveraging data to gain insights, automate processes, and enhance decision-making capabilities. Real-time dashboards enable businesses to monitor key performance indicators (KPIs) and other critical metrics instantly, allowing for swift responses to market changes and operational challenges. This immediacy in data access and visualization is vital in today's fast-paced business environment, thereby fueling the market's growth.

Another significant factor contributing to the market's expansion is technological advancements in data visualization and analytics tools. Innovations such as AI and machine learning are being integrated into real-time dashboards, enhancing their analytical capabilities and user experience. These advancements allow more sophisticated data processing, predictive analytics, and intuitive interfaces, making dashboards more accessible and valuable to a broader range of users. As a result, businesses are more inclined to invest in these advanced tools to stay competitive and innovative.

The growing adoption of cloud-based solutions is also a crucial driver for the Real Time Dashboard market. Cloud deployment offers several advantages, including scalability, cost-effectiveness, and accessibility from anywhere with an internet connection. These benefits are especially appealing to small and medium-sized enterprises (SMEs) that might lack the resources for extensive on-premises infrastructure. The flexibility and reduced upfront costs associated with cloud-based dashboards make them an attractive option for businesses looking to quickly implement and benefit from real-time data analytics.

Real-Time Analytics is a transformative force in the Real Time Dashboard market, enabling organizations to process and analyze data as it is generated. This capability is crucial for businesses aiming to maintain a competitive edge in fast-paced environments. By leveraging real-time analytics, companies can make informed decisions swiftly, optimize operations, and enhance customer experiences. The integration of real-time analytics into dashboard solutions allows for the immediate visualization of data trends and patterns, facilitating proactive strategies and timely interventions. As industries continue to prioritize agility and responsiveness, the demand for real-time analytics within dashboard platforms is expected to grow, driving further innovation and adoption.

Regionally, the market outlook varies, with North America leading due to its early adoption of advanced technologies and a high concentration of market players. The Asia Pacific region is expected to witness substantial growth over the forecast period, driven by rapid digital transformation, increasing investments in IT infrastructure, and a burgeoning startup ecosystem. Europe and Latin America also present significant growth opportunities, although at a relatively moderate pace compared to North America and Asia Pacific.

Component Analysis

The Real Time Dashboard market is segmented into three primary components: Software, Hardware, and Services. Each of these components plays a critical role in the overall functionality and appeal of real-time dashboards. The Software segment is pivotal as it encompasses the various applications and platforms that facilitate data visualization and analytics. This segment is poised for considerable growth due to continuous advancements in software capabilities, including AI integration and enhanced user interfaces. Companies are increasingly seeking robust software solutions to handle complex data sets and provide actionable insights in real-time, driving demand in this segment.

Hardware, although a smaller segment compared to software

Exploring Online Sales Data with Power BI !!

Another productive day diving into online sales dataset! Here’s a roundup of the insights I uncovered today:

Revenue by Category: Analyzed revenue distribution across different product categories to identify high-performing sectors.

Revenue by Sub-Category: Drilled down into sub-categories for a more granular view of revenue streams.

Revenue by Payment Mode: Examined revenue patterns based on payment methods to understand customer preferences.

Revenue by State: Mapped out revenue by state to pinpoint geographical strengths and opportunities.

Profit by Category: Evaluated profitability across product categories to assess which categories yield the highest profit margins.

Profit by Sub-Category: Explored profit levels at a sub-category level to identify the most profitable segments.

Profit by Payment Mode: Analyzed profit distribution across different payment methods.

Top 5 States by Revenue and Profit: Highlighted the top 5 states driving the most revenue and profit, offering insights into regional performance.

Sales Map by State: Visualized sales data on a map to provide a geographical perspective on sales distribution.

Total Quantity, Revenue, and Profit: Aggregated data to give an overview of total quantities sold, overall revenue, and total profit.

Filter by Category: Added a filter functionality to focus on specific categories and refine data analysis.

Diabetes Analytics Dashboard – Power BI 🩺📊 This practice dashboard is built for Data Analytics, Data Visualization, and Data Science learning. It provides meaningful insights into diabetes risk factors using interactive visuals and advanced analytics.

🔹 Key Metrics – Total patients, BMI, glucose, blood pressure, and insulin levels. 🔹 Diabetes Risk Segmentation – Categorized into High, Medium, and Low risk groups. 🔹 Trends & Distribution – Glucose vs. Age, BMI categories, and Blood Pressure analysis. 🔹 Correlation Analysis – Exploring the relationships between glucose, BMI, and diabetes risk. 🔹 Gauge & Pie Charts – Visualizing risk percentage, BMI distribution, and glucose levels. 🔹 Interactive Filters & Drilldowns – Allowing deeper exploration of specific patient groups. 🔹 Predictive Insights – Identifying potential risk patterns through visual analytics.

This project helps in understanding data-driven healthcare insights using Power BI. Thanks to Kaggle for the dataset!

The Performance Dashboard (formerly Performance Outcomes System) datasets are developed in line with legislative mandates to improve outcomes and inform decision making regarding individuals receiving Medi-Cal Specialty Mental Health Services (SMHS). The Dashboard gathers information relevant to specific mental health outcomes and provides useful summary reports for ongoing quality improvement and to support decision making. Please note: the Excel file Performance Dashboard has been discontinued and replaced with the SMHS Performance Dashboards found on Behavioral Health Reporting (ca.gov).

The Department of Health Care Services (DHCS) Long-Term Services and Supports (LTSS) Data Dashboard is an initiative of the Home and Community Based Services Spending Plan. The initiative's primary goal is to create a public-facing LTSS data dashboard to track demographic, utilization, quality, and cost data related to LTSS services. This dashboard will link statewide long-term care and home and community-based services (HCBS) data with the goal of increased transparency to make it possible for regulators, policymakers, and the public to be informed while the state continues to expand, enhance, and improve the quality of LTSS in all home, community, and congregate settings.

The first iteration of the LTSS Dashboard was released in December 2022 as an Open Data Portal file with 40 measures pertaining to LTSS beneficiaries, which includes ten different demographics, plan-related dimensions, and dual stratification. The December 2023 Data Release includes 16 new measures on the Medi-Cal LTSS Dashboard and Open Data Portal (Select “View Underlying Data”); and additional measures and dimensions, including dual stratification, will be added to the Open Data Portal in 2024.

Note: The LTSS Dashboard measures are based on certified eligible beneficiaries who were enrolled in Medi-Cal for one or more months during the reporting interval. Most of the DHCS LTSS dashboard measures report the annual number of certified eligible Medi-Cal beneficiaries who have used LTSS services within a year. Other departments may report on these programs differently. For example, the Department of Social Services (CDSS) reports monthly IHSS recipient/consumer counts. The California Department of Aging (CDA) reports monthly CBAS Medi-Cal participants. DHCS’ annual utilization / enrollment counts of IHSS and CBAS beneficiaries are larger than CDSS/CDA's monthly counts because of data source differences and new enrollment or program attrition over time. Monthly snap-shot measures (average monthly utilization) for IHSS and CBAS have been added to the LTSS Dashboard to align with CDSS and CDA monthly reporting.

Refer to the LTSS-Dashboard (ca.gov) program page for: 1) a Fact Sheet with highlights from the initial data release including changes over time in use of Home and Community-Based Services as well as select demographic information; 2) the Measure Specifications document – that describes business rules and inclusion/exclusion criteria related to age groups, plan types, aid code, geographic, or other important program/waiver-specific eligibility criteria; and 3) User guide – that shows how to navigate the Open Data Portal data file with specific examples.

Comprehensive learning analytics and statistics from over 50,000 students using science-backed study methods.

A study on four interaction and information presentation dashboard problems: 1. information overload, 2. inappropriate data order and grouping 3. ineffective data presentation 4. misalignment in visual literacy expectations

Each dashboard had two types: problematic and adapted. Then each type had two tasks.

File 1 (dependent and independent variables): 63 participants completed the experiment, so for each user, we have user graph literacy (0-4), effectiveness(0 or 1 for each task), efficiency (completion time in minutes for each task), perceived performance (0-20 for each task) and perceived cognitive demand (0-20 for each task).

File 2 (interaction data): 50 participants interacted with the dashboards while their low-level inttraction data was being collected using UCIVIT tool. The zip file contains 50 JSON files for each participant on eight dashboards.

File 3 (interaction patterns): Using VMSP sequential pattern mining algorithm, we mined the low-level interaction data to find the patterns of behaviour exhibited by the participants. This file explains the benchmarking used to arrive at the parameters used and lists all the patterns found on each dashboard. It also further explains all the interpretations of events on all elements.

This dataset displays demographics for the families and individuals residing in the Department of Homeless Services (DHS) shelter system.

Use these interactive dashboards to explore data on Massachusetts incarcerated populations, admissions and releases.

The following datasets are based on the adult (age 21 and over) beneficiary population and consist of aggregate MHS data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

The following dashboard shows statewide Behavioral Health Help Line (BHHL) utilization data and some demographic data about BHHL callers. This data is collected by the Massachusetts Behavioral Health Partnership (MBHP), the vendor that operates the BHHL, and maintained by the Department of Mental Health (DMH).

Interactive data reports on the Philadelphia District Attorney's Office's work, including incidents (from the Philadelphia Police Department), arrests, charges, bail, outcomes, case length, future years of incarceration, future years of supervision, summary arrests, summary charges, summary case outcomes, and summary case lengths. Dashboards are organized into final year-end data (updated at the end of each year) and year-to-date data (updated daily). Each dashboard displays one or more interactive graphs showing trends, a table of data, and, optionally, an interactive map displaying the data by police districts. The dashboard does not provide for downloading data. Data downloads can be found at https://opendataphilly.org/datasets/district-attorney/

The following datasets are based on the children and youth (under age 21) beneficiary population and consist of aggregate Mental Health Service data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.