View the dataFor best results:View the dashboard in full screen.Use Chrome or Firefox as your browser.Read the dataData viewsThere are two views with this dashboard. You can toggle between them by clicking the button on the top right of the dashboard.The views are:Crime summary viewCrime details viewViewing modesThere are ways to view with this dashboard. You can toggle between them by clicking the button.The modes to view the data are:DarkLightSearch the dataCrime summary viewThe search options allow you to select:Location: Options are citywide, each of the precincts, each of the wards, or each of the neighborhoods.Select Crime: Select a type of crime to display.Select Chart: Select a way to display the crime data.Crime detail viewThe search options allow you to select:Date range: Select a custom date range.Location: Options are citywide, each of the precincts, each of the wards, or each of the neighborhoods.Select Type: Select a type of crime.Select Categories: Select one or more categories of crime to display.Select Details: Select one or more details to filter the data displayed.Select Chart: Select a way to display the crime data.View dashboard data definitions and detailed directionsView the open data set

The IT Performance Dashboard a trusted source for IT performance information across VA. This is available only on the VA intranet. The dashboard is a collection of information from Regions, VISNs, Facilities, and other VA offices across a range of departments within the Office of Information & Technology (OIT)�from Product Development and Information Security to Human Resources to Finance. It presents often-complex information in easy-to-understand dashboard reports.Currently, you can see performance reports relating to Customer Service, Service Level Agreements (SLAs), Systems, Monthly Performance Reports (MPR), IT Assets and Debt Score, Human Resources, and Network/Security. There is also a Facility CIO Center tab where you can see Quarterly Performance Reviews, an OIT Daily Brief tab where you can access OIT Daily Brief Reports, and a Product Delivery tab that takes you to the PMAS Dashboard. As the IT Performance Dashboard continues to evolve, you will be able to see a broader range of performance information � including custom reports, upon request.This page displays the Systems Dashboard within the IT Dashboard:- VistA Availability: This report measures and displays the percentage of time the RPC Broker service (CPRS) was available on the data server for all the sites located in the data centers. Clicking on any of the lines denoting Regions will enable the user to drill down and view the data at the VISN level and further at the Server level.�Blue Button Users - First-Time and Returning Users: This report displays the First-Time Users and Returning Users that have accessed the Blue Button service for My HealtheVet. A new user is one that has never used the Blue Button to perform an activity before, while a returning user is one that has used the Blue Button on a previous occasion to perform any activity. Clicking on any of the bars on the chart denoting the Months will enable the user to drill down and view the daily level data associated with the users. Keep date range selection within 18 months for optimal viewing of the charts.- Availability of Enterprise Hosted Systems: These reports displays the percentage of total monthly availability for ADR, ACS, BDN, CH33, HDR, MPI, MHV, VETSNET, VVA, and VIE.- Blue Button Activity: This report displays Blue Button information for the types of data viewed and downloaded by users that have performed those actions, in addition to the different types of data downloaded by users - consisting of Blue Button, Txt, and PDF. Clicking on any of the bars on the chart denoting the Months will enable the user to drill down and view the daily level data associated with the activity; or in the case of Downloaded Data, the system will first present a breakdown of the downloaded data type, and then the daily level data associated with the information. Keep date range selection within 18 months for optimal viewing of the charts.- VANTS: The following reports are included: Number of Audio Conferences, Total Number of Minutes, Average Number of Calls, Expand Cost Summary Report Cost Summary Report- PIV: The following reports are included: Number of Cards Issued , Applications By Stages, % of Cards Issued, Cost Center-National Cemetery Administration (NCA): These reports displays the percentage of total monthly availability for AMAS, BOSS, Feith Databases

The incident locations represented are approximated and not the actual location of the incident (or individuals residence). A computer generated randomized distance adjustment is applied to each incident location to ensure data are anonymous. This approximated location data is also shown on the dashboard.Interacting with the DashboardMay 2018 Update: Click on one of the charts to filter the displayed data and drop down options. You can select multiple chart elements at a time (i.e. select male for gender and January and February for month). To clear the filters and return to seeing all the data, click on the selected chart elements to remove them.Click on one or more values in drop down to filter the data shown in the display. To clear filters and return to seeing all of the data, click on selected values in the drop down to remove them. For the date filter, select and then delete the text. The map legend is accessible through the navigation in the upper right hand cornerUse the map selection tool in the upper left corner or the map to select calls in specific areas. The following documents what data are collected and why they are being collected. Additional variables will be added to the dashboard in the next phase.Opioid Abuse ProbableA call may be coded as “opioid abuse probable” for many reasons, such asAre there are any medical symptoms indicative of opioid abuse?Are there physical indicators on scene (i.e. drug paraphernalia, pill bottles, etc.)?Are there witnesses or patient statements made that point to opioid abuse?Is there any other evidence that opioid abuse is probable with the patient?“Opioid abuse probable” is determined by Tempe Fire Medical Rescue Department’s Emergency medical technicians and paramedics on scene at the time of the incident. Narcan/Naloxone Given“Narcan/Naloxone Given” refers to whether the medication Narcan/Naloxone was given to patients who exhibited signs or symptoms of a potential opioid overdose or to patients who fall within treatment protocols that require Narcan/Naloxone to be given. Narcan/Naloxone are the same medication with Narcan being the trade name and Naloxone being the generic name for the medication. Narcan is the reversal medication used by medical providers for opioid overdoses.Groups“Groups” are used to determine if there are specific populations that have an increase in opioid abuse. The student population at ASU was being examined for other purposes to determine ASU's overall call volume impact in Tempe. Data collection with the university is consistent with Fire Departments who provide service to the other PAC 12 universities. Since this data set was already being evaluated, it was included in the opioid data collection as well.The Veteran and Homeless Groups were established as demographic tabs to identify trends and determine needs in conjunction with the City of Tempe’s Veterans and Homeless programs. Since these data sets were being evaluated already, they were included in the opioid data collection as well.The “unknown” group includes incidents where a patient is unable to answer or refuses to answer the demographic questions. GenderPatient gender is documented as male or female when crews are able to obtain this information from the patient. There are some circumstances where this information is not readily determined and the patient is unable to communicate with our crews. In these circumstances, crews may document unknown/unable to determine.

In this Excel project, I utilized data-cleaning techniques to preprocess a dataset and created an interactive dashboard using pivot tables and charts. The objective was to provide users with a dynamic and intuitive tool to analyze and visualize data across multiple dimensions.

Project Highlights:

Data Cleaning: Employed robust data cleaning techniques to ensure the accuracy and consistency of the dataset. This involved handling missing values, removing duplicates, and standardizing data formats.

Pivot Tables: Utilized Excel's pivot tables to summarize and aggregate the dataset, enabling efficient data analysis and exploration. Created three pivot tables to extract valuable insights from the data.

Interactive Dashboard: Developed an interactive dashboard that allows users to dynamically explore the data through three slicers. These slicers provide the flexibility to select different data columns for visualization on the charts, empowering users to dive deep into the dataset based on their specific needs.

Data Visualization: Designed visually appealing charts to represent key trends and patterns in the dataset. Leveraged Excel's charting capabilities to present information in a clear and concise manner. The charts provide valuable insights into the dataset and help users make data-driven decisions.

User-Friendly Experience: Ensured a user-friendly experience by implementing intuitive navigation and interaction within the dashboard. Users can easily filter and drill down into the data to gain a deeper understanding and uncover valuable insights.

This dashboard provides graphs with Consumer Price Index (CPI) information for food categories in Manitoba and other provincial jurisdictions in Canada. This dashboard provides graphs with Consumer Price Index (CPI) information for food categories in Manitoba, and other provincial jurisdictions in Canada. Food prices are increasing at a pace not seen before in the last 20 years. Requests for information have been received by Manitoba Agriculture from the general public. This dashboard focuses strictly on food and food categories, showing price changes through time, starting in 2002 until the present. The food categories shown in the dashboard, either in a graph or in the selection option, are: Meat Fish, seafood and other marine products Dairy products Eggs Bakery and cereal products Fruit, fruit preparations and nuts Vegetables and vegetable preparations Other food products and non-alcoholic beverages All Foods The dashboard contains three tabs: Manitoba: This chart provides a graph with the option of plotting the food CPI for All Foods (average of all food categories), or for a specific food category for Manitoba. The chart can be filtered to show year-to-date data, or data for the last one, five, 10, or all years going back to 2002. By Food Category: This chart provides a bar graph with the CPI of all the food categories for Manitoba. Information is available for the past 12 months of available data, so the chart shows one-year variation. By Province: This chart provides a bar graph with the CPI for all the provinces, and Canada. Each province is represented by one bar in the graph. The user can select the food category of interest or All Foods (average of all categories). Information is available for the past 12 months of available data, so the chart shows one-year variation. The data table used for this dashboard is the Consumer Price Index Food Product Statistics table. The source of the information is the Statistics Canada Table 18-10-0004-01 Consumer Price Index, monthly, not seasonally adjusted. Data are updated monthly by Manitoba Agriculture from Statistics Canada sources.

This dashboard illustrates aggregated statistics on the status, actions, outcomes, and prosecutions from inspections conducted by the Manitoba Animal Welfare Program’s Animal Protection Officers (APO). This dashboard illustrates aggregated statistics on the status, actions, outcomes, and prosecutions from inspections conducted by the Manitoba Animal Welfare Program’s Animal Protection Officers (APO). The data is summarized on a quarterly basis, from 2016 to present. This dashboard will be updated on a quarterly basis with data from the Provincial Animal Welfare Program. The data table used for this dashboard reflects Manitoba Animal Welfare (AW) Program – Case Outcomes. The data table used for this dashboard is Manitoba Animal Welfare Program – Case Outcomes. There are two charts for this dashboard: Inspection Outcomes: This is a bar chart that ranks case outcomes from inspections conducted for assigned cases, in ascending order, for the user-selected time period. Prosecutions: This is a bar chart that ranks prosecution types associated with each assigned cases, in ascending order, for the user-selected time period. There are five indicators for this dashboard: Concerns Reported: This indicator displays the number of concerns reported for assigned cases in the user-selected time period. Inspections Conducted: This indicator displays the number of inspections conducted for assigned cases in the user-selected time period. Tickets Issued: This indicator displays the number of tickets issued for all assigned cases in a user-selected time period. Court Prosecutions: This indicator displays the number of court prosecutions for all assigned cases in the user-selected time period. Appeals Made: This indicator displays the number of appeals made for assigned cases in the user-selected time period. For more information, please refer to the Animal Welfare Main Page.

This interactive sales dashboard is designed in Excel for B2C type of Businesses like Dmart, Walmart, Amazon, Shops & Supermarkets, etc. using Slicers, Pivot Tables & Pivot Chart.

Dashboard Overview

1. Sales dashboard ==> basically, it is designed for the B2C type of business. like Dmart, Walmart, Amazon, Shops & supermarkets, etc.
2. Slices ==> slices are used to drill down the data, on the basis of yearly, monthly, by sales type, and by mode of payment.
3. Total Sales/Total Profits ==> here is, the total sales, total profit, and profit percentage these all are combined into a monthly format and we can hide or unhide it to view it as individually or comparative.
4. Product Visual ==> the visual indicates product-wise sales for the selected period. Only 10 products are visualized at a glance, and you can scroll up & down to view other products in the list.
5. Daily Sales ==> It shows day-wise sales. (Area Chart)
6. Sales Type/Payment Mode ==> It shows sales percentage contribution based on the type of selling and mode of payment.
7. Top Product & Category ==> this is for the top-selling product and product category.
8. Category ==> the final one is the category-wise sales contribution.

Datasheets Overview

1. The dataset has the master data sheet or you can call it a catalog. It is added in the table form.
2. The first column is the product ID the list of items in this column is unique.
3. Then we have the product column instead of these two columns, we can manage with only one also but I kept it separate because sometimes product names can be the same, but some parameters will be different, like price, supplier, etc.
4. The next column is the category column, which is the product category. like cosmetics, foods, drinks, electronics, etc.
5. Then we have 4th column which is the unit of measure (UOM) you can update it also, based on the products you have.
6. And the last two columns are buying price and selling price, which means unit purchasing price and unit selling price.

Input Sheet

The first column is the date of Selling. The second column is the product ID. The third column is quantity. The fourth column is sales types, like direct selling, are purchased by a wholesaler or ordered online. The fifth column is a mode of payment, which is online or in cash. You can update these two as per requirements. The last one is a discount percentage. if you want to offer any discount, you can add it here.

Analysis Sheet: where all backend calculations are performed.

So, basically these are the four sheets mentioned above with different tasks.

However, a sales dashboard enables organizations to visualize their real-time sales data and boost productivity.

A dashboard is a very useful tool that brings together all the data in the forms of charts, graphs, statistics and many more visualizations which lead to data-driven and decision making.

Questions & Answers

1. What percentage of profit ratio of sales are displayed in the year 2021 and year 2022? ==> Total profit ratio of sales in the year 2021 is 19% with large sales of PRODUCT42, whereas profit ratio of sales for 2022 is 22% with large sales of PRODUCT30.
2. Which is the top product that have large number of sales in year 2021-2022? ==> The top product in the year 2021 is PRODUCT42 with the total sales of $12,798 whereas in the year 2022 the top product is PRODUCT30 with the total sales of $13,888.
3. In Area Chart which product is highly sold on 28th April 2022? ==> The large number of sales on 28th April 2022 is for PRODUCT14 with a 24% of profit ratio.
4. What is the sales type and payment mode present? ==> The sale type and payment modes show the sales percentage contribution based on the type of selling and mode of payment. Here, the sale types are Direct Sales with 52%, Online Sales with 33% and Wholesaler with 15%. Also, the payment modes are Online mode and Cash equally distributed with 50%.
5. In which month the direct sales are highest in the year 2022? ==> The highest direct sales can be easily identified which is designed by monthly format and it’s the November month where direct sales are highest with 28% as compared with other months.
6. Which payment mode is highly received in the year 2021 and year 2022? ==> The payments received in the year 2021 are the cash payments with 52% as compared with online transactions which are 48%. Also, the cash payment highly received is in the month of March, July and October with direct sales of 42%, Online with 45% and wholesaler with 13% with large sales of PRODUCT24. ==> The payments received in the year 2022 are the Online payments with 52% as compared with cash payments which are 48%. Also, the online payment highly received is in the month of Jan, Sept and December with direct sales of 45%, Online with 37% and whole...

The IT Performance Dashboard is a trusted source for IT performance information across VA. This is available only on the VA intranet. The dashboard is a collection of information from Regions, VISNs, Facilities, and other VA offices across a range of departments within the Office of Information & Technology (OIT)�from Product Development and Information Security to Human Resources to Finance. It presents often-complex information in easy-to-understand dashboard reports.Currently, you can see performance reports relating to Customer Service, Service Level Agreements (SLAs), Systems, Monthly Performance Reports (MPR), IT Assets and Debt Score, Human Resources, and Network/Security. There is also a Facility CIO Center tab where you can see Quarterly Performance Reviews, an OIT Daily Brief tab where you can access OIT Daily Brief Reports, and a Product Delivery tab that takes you to the PMAS Dashboard. As the IT Performance Dashboard continues to evolve, you will be able to see a broader range of performance information � including custom reports, upon request.This page displays the Human Resources Dashboard.Total Loss, Quit, Retirement and Termination Rates: This report displays the percentage of employees who have left the OIT Organization. The data is displayed on a quarterly basis under the following categories: Total Loss, Quit, Retirement and Termination. The report also displays Sub Organization level details for all Organizations (except QPO and Others) by way of a 'Sub Organization' filter.Total Cash Awards: This report displays the total count of the different types of awards that fall under Cash Awards. It also displays the total dollar values of the awards in 'tool tips'. Clicking on any of the lines in the chart (except QPO and Others) will enable the user to drill down to the Sub Organization level of data.Overtime Hours and Pay: This report displays the Average Overtime Pay and Overtime hours per Full Time Employee Equivalent (FTEE) at the Organization level. It also displays the data as a percentage of Total FTE hours and Gross salary (in 'tool tips'). Clicking on any of the bars in the chart (except QPO and Others) permits the user to drill down further to the Sub Organization level. Selecting any fiscal year from the time period filter will display the data by pay periods in that fiscal year.Recruitment, Relocation and Retention Bonuses: This report displays the Average dollar value of the Recruitment, Relocation and Retention bonuses. This information is also displayed as a percentage of the Gross pay for each of the bonus categories in the 'tool tips' available on the report. Clicking on any of the bars in the chart (except QPO and Others) permits the user to drill down further to the Sub Organization level. Selecting any fiscal year from the time period filter will display the data by pay periods in that fiscal year.

This dashboard defaults to a presentation of the crash points that will cluster the crash types and determine a predominant crash type. In the case two crash types have the same number of crashes for that type the predominant type will not be colored to either of the crash types. Clicking on the clusters will include a basic analysis of the cluster. These clusters are dynamic and will change as the user zooms in an out of the map. The clustering of crashes is functionality availalble in ArcGIS Online and the popups for the clusters is based on items that include elements configured with the Arcade language. Users interested in learning more about point clustering and the configuration of popups should read through some of the examples of the following ESRI Article (https://www.esri.com/arcgis-blog/products/arcgis-online/mapping/summarize-and-explore-point-clusters-with-arcade-in-popups/) . The dashboard itself does include a map widget that does allow the user to toggle the visibility of layers and/or click on the crashes within the map. The popups for single crashes can be difficult to see unless the map is expanded (click in upper right of map widget). There is a Review Crashes tab that allows for another display of details of a crash that may be easier for users.This dashboard includes selectors in both the header and sidebar. By default the sidebar is collapsed and would need to be expanded. The crash dataset used in the presentation includes columns with a prefix of the unit. The persons information associated to each unit would be based on the Person that was considered the driver. Crash data can be filtered by clicking on items in chart widgets. All chart widgets have been configured to allow multiple selections and these selections will then filter the crash data accordingly. Allowing for data to be filtered by clicking on widgets is an alternative approach to setting up individual selectors. Selectors can take up a lot of space in the header and sidebar and clicking on the widget items can allow you to explore different scenarios which may ultimately be setup as selectors in the future. The Dashboard has many widgets that are stacked atop each other and underneath these stacked widgets are controls or tabs that allow the user to toggle between different visualizations. The downside to allowing a user to filter based on the output of a widget is the need for the end user to keep track of what has been clicked and the need to go back through and unclick.Many of the Crash Data Elements are based on lookups that have a fairly large range of values to select. This can be difficult sometimes with charts and the fact that a user may be overwhelmed by the number of items be plotted. Some of these values could potentially benefit by grouping similar values. The crash data being used in this dashboard hasn't been post processed to simplify some of the groupings of data and represent the value as it would appear in the Crash System. This dashboard was put together to continue the discussion on what data elements should be included in the GIS Crash Dataset. At the moment there is currently one primary dataset that is used to present crash data in Map Services. There is lots of potential to extend this dataset to include additional elements or it might be beneficial to create different versions of the crash data. Having an examples like this one will hopefully help with the discussion. Workable examples of what works and doesn't work. There are lots of data elements in the Crash System that could allow for an even more detailed safety analysis. Some of the unit items included in the example for Minot Ave in Auburn are the following. This information is included for the first three units associated to any crash.Most Damaged AreaExtent of DamageUnit TypeDirection of Travel (Northbound, Southbound, Eastbound, Westbound)Pre-Crash ActionsSequence of Events 1-4Most Harmful Event Some of the persons items included in the example for Minot Ave in Auburn are the following. This information is included for the first three units associated to any crash and the person would be based on the driver.Condition at Time of CrashDriver Action 1Driver Action 2Driver DistractedAgeSexPerson Type (Driver/Owner(6), Driver(1))In addition to the Units and Persons information included above each crash includes the standard crash data elements which includesDate, Time, Day of Week, Year, Month, HourInjury Level (K,A,B,C,PD)Type of CrashTownname, County, MDOT RegionWeather ConditionsLight ConditionsRoad Surface ConditionsRoad GradeSchool Bus RelatedTraffic Control DeviceType of LocationWork Zone ItemsLocation Type (NODE, ELEMENT) used for LRS# of K, # of A, # of B, # of C, # of PD InjuriesTotal # of UnitsTotal # of PersonsFactored AADT (Only currently applicable for crashes along the roadway (ELEMENT)).Location of First Harmful EventTotal Injury Count for the CrashBoolean Y/N if Pedestrian or Bicycles are InvolvedContributing EnvironmentsContributing RoadRoute Number, Milepoint, Element ID, Node ID

This dashboard illustrates aggregated statistics on the status, actions, outcomes, and prosecutions from inspections conducted by the Manitoba Animal Welfare Program’s Animal Protection Officers (APO). This dashboard illustrates aggregated statistics on the status, actions, outcomes, and prosecutions from inspections conducted by the Manitoba Animal Welfare Program’s Animal Protection Officers (APO). The data is summarized on a quarterly basis, from 2016 to present. This dashboard will be updated on a quarterly basis with data from the Provincial Animal Welfare Program. The data table used for this dashboard reflects Manitoba Animal Welfare (AW) Program – Case Outcomes. The data table used for this dashboard is Manitoba Animal Welfare Program – Case Outcomes. There are two charts for this dashboard: Inspection Outcomes: This is a bar chart that ranks case outcomes from inspections conducted for assigned cases, in ascending order, for the user-selected time period. Prosecutions: This is a bar chart that ranks prosecution types associated with each assigned cases, in ascending order, for the user-selected time period. There are five indicators for this dashboard: Concerns Reported: This indicator displays the number of concerns reported for assigned cases in the user-selected time period. Inspections Conducted: This indicator displays the number of inspections conducted for assigned cases in the user-selected time period. Tickets Issued: This indicator displays the number of tickets issued for all assigned cases in a user-selected time period. Court Prosecutions: This indicator displays the number of court prosecutions for all assigned cases in the user-selected time period. Appeals Made: This indicator displays the number of appeals made for assigned cases in the user-selected time period. For more information, please refer to the Animal Welfare Main Page.

This dashboard provides a visual of the Coronavirus Disease 2019 (COVID-19) surveillance in San Bernardino County.

The legend items for both graphs (i.e., Positives, Negatives, Total) can be turned on/off by clicking on the legend item. Use the scrollbar on the top of each graph to zoom in/out of the graph items. Selecting certain dates on either graph will also dynamically change the data to the right of the dashboard.

Inspiration Retail and e-commerce businesses thrive on data to optimize operations, inventory management, and customer satisfaction. Analyzing sales, shipping, and profitability can reveal crucial patterns that help businesses make informed decisions. This dataset, modeled after a typical "Superstore" environment, provides an opportunity to apply analytical skills to solve common challenges faced by retailers, such as identifying best-selling products, managing inventory, optimizing shipping times, and improving customer segmentation.

Context Superstores often deal with massive volumes of transactional data, which include sales, product categories, customer demographics, order quantities, discounts, and shipping details. Understanding and analyzing these data points can unlock valuable insights to drive efficiency and improve profitability. This dataset could be inspired by popular global retailers, providing a comprehensive look at how different regions, product lines, and customer profiles interact to affect overall performance.

Whether you're looking to understand which regions generate the highest sales, or which product categories are the most profitable, this dataset offers a rich source for various types of analysis. It is particularly useful for business analysts, data scientists, and decision-makers aiming to model the financial and operational aspects of retail management.

Dataset Description The Superstore dataset consists of transactional data over a specified period of time, typically including the following key attributes:

Order ID: Unique identifier for each transaction. Order Date & Ship Date: The dates when the order was placed and shipped. Customer ID: Unique identifier for each customer. Customer Name: The name of the customer (optional depending on privacy). Segment: Type of customer (e.g., Consumer, Corporate, Home Office). Country/Region/City: Geographic location of the sale. State: Specific state or province. Product ID: Unique identifier for each product. Product Category: High-level grouping (e.g., Office Supplies, Technology, Furniture). Sub-Category: More detailed product category. Product Name: Description of the product sold. Sales: Total revenue generated from the transaction. Quantity: Number of units sold. Discount: Applied discount rate for the transaction. Profit: Profit margin of the sale. Shipping Mode: The type of shipping selected (e.g., Standard, Express). Potential Uses of the Dataset Sales Analysis: Understanding the overall sales performance across different segments, regions, and product categories. Profitability Analysis: Identifying which products or regions are the most profitable and which are underperforming. Customer Segmentation: Exploring purchasing patterns of different customer types and using this information for targeted marketing. Inventory Management: Analyzing demand for specific products to optimize stock levels. Shipping Optimization: Assessing shipping modes and times to identify inefficiencies and improve delivery performance. Discount and Profit Correlation: Understanding the impact of discounts on profitability and sales volume. This dataset is a rich source for beginners and experienced data scientists alike to practice data manipulation, cleaning, visualization, and building predictive models that can provide actionable business insights.

Solar photovoltaic (PV) energy is a large component of meeting New Jersey's clean energy goals. With more than 3.3 gigawatts (GW) of installed solar PV capacity, New Jersey is currently ranked in the top 10 nationwide. This interactive dashboard was created by the NJDEP’s Bureau of Climate Change & Clean Energy and provides a summary of solar PV installations in New Jersey's counties. For each county, the following data is available in the dashboard:Total number of solar installationsTotal installed capacityBreakdown of installation type (i.e. Residential, Non-Residential, and Grid Supply) Percentage of installed solar capacity (total, residential, non-residential, and grid supply)For an in depth analysis of the installed solar PV in each county, one or more counties can be selected in the map (using the select tool) or by clicking the corresponding county or counties in the bar graph. Doing so will filter all of the widgets in the dashboard based on the user's selection. The underlying data for this dashboard is published monthly via the New Jersey Board of Public Utilities Solar Activity Reports. Visit the NJBPU's Clean Energy Program website for more information on programs to increase clean energy and/or improve the energy efficiency of your home or business.

This dashboard shows aggregated statistics on non-compliances to five sections under The Animal Care Act, following inspections conducted by the Manitoba Animal Welfare Program’s Animal Protection Officers (APO). This dashboard shows aggregated statistics on non-compliances to five sections under The Animal Care Act following inspections conducted by the Manitoba Animal Welfare Program’s Animal Protection Officers (APO). Non-compliances to any of the five sections of the act, are represented by a bar on the chart. The number for each section is shown from 2016 to present. Data from this dashboard will be updated on a quarterly basis and all data come from the Provincial Animal Welfare Program. The data table used for this dashboard is Manitoba Animal Welfare Program – Non-Compliances to The Animal Care Act. There is one chart on this dashboard, a bar chart that ranks the number of non-compliances to any of the five sections under the act in ascending order for the user-selected time period of quarterly intervals, beginning in 2016, to the current quarter. For more information, please refer to the Animal Welfare Main Page.

The orgdashboards extension for CKAN provides a way to create visual dashboards that summarize datasets within an organization. It allows users to add charts, graphs, and maps to a single overview screen for a quick snapshot of key data. This aims to simplify data understanding and provide a centralized view of important information. Key Features: Data Visualization: Enables the creation of dashboards with graphs, charts, and maps for clear data summarization. Customizable Organization Entity Names: Allows customization of text references for "Organization/Organizations" to "Country/Countries" or vice-versa, adapting the user interface to the specific needs. This feature primarily applies to select sections of the UI, with potential template overrides needed for full coverage of modifications. Customizable Group Entity Names: Provides flexible naming conventions with the option to switch Group/Groups to Theme/Themes, or vice-versa, to tailor the UI to match specific terminology. This feature predominantly applies to selected sections of the UI, requiring template overrides for full UI modification. Custom Domain Support: Offers the functionality to assign custom domains to individual dashboards, allowing for personalized and branded dashboard access via user assigned domain. Translation Support: Provides infrastructure for translating the extension's interface, including steps to create and compile language catalogs, enabling multilingual support within CKAN. Technical Integration: The extension integrates with CKAN through plugins and requires specific configuration settings within the CKAN configuration file. These include settings such as: ckan.plugins, ckanext.orgdashboards.datasetsperpage, ckanext.orgdashboards.organizationentityname, ckanext.orgdashboards.groupentityname, and ckanext.orgdashboards.customdnsactive. Implementing the ITranslation interface within the extension's plugin.py is essential for translating additional strings and allows support for multiple language strings. Benefits & Impact: The orgdashboards extension enhances CKAN by providing a user-friendly way to visualize and understand organizational data. This can improve data accessibility, communication, and decision-making by providing a single, comprehensive view of key datasets. The ability to customize entity names further enhances the user experience by aligning the interface with the organization's specific terminology.

"In this comprehensive multi-page Power BI project, I've delved into the world of cryptocurrency forecasting, leveraging dynamic slicers for both year and currency selection.

The project is designed to provide a holistic view of cryptocurrency performance, with various key metrics available at your fingertips. On the initial page, you'll find a dashboard featuring cards displaying essential data points such as trading volume, market capitalization, daily highs, lows, and their respective averages.

But that's just the beginning. This report seamlessly connects to additional pages, allowing for a more granular analysis. On the secondary page, we unveil two crucial graphs. The first graph unveils insights into the historical highs and lows of each cryptocurrency, charted against date, enabling a deeper understanding of price fluctuations over time. Meanwhile, the second graph presents data on opening and closing prices over date intervals.

This comprehensive report is a valuable resource for tracking and comprehending the trends within the cryptocurrency market. It's a powerful tool to gain insights into the world of digital currencies, their historical performance, and their market dynamics."

Feel free to adapt and refine this post to fit your personal style and preferences.

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Data Visualization Tools Market Size 2025-2029

The data visualization tools market size is forecast to increase by USD 7.95 billion at a CAGR of 11.2% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for business intelligence and AI-powered insights. Companies are recognizing the value of transforming complex data into easily digestible visual representations to inform strategic decision-making. However, this market faces challenges as data complexity and massive data volumes continue to escalate. Organizations must invest in advanced data visualization tools to effectively manage and analyze their data to gain a competitive edge. The ability to automate data visualization processes and integrate AI capabilities will be crucial for companies to overcome the challenges posed by data complexity and volume. By doing so, they can streamline their business operations, enhance data-driven insights, and ultimately drive growth in their respective industries.

What will be the Size of the Data Visualization Tools Market during the forecast period?

Request Free SampleIn today's data-driven business landscape, the market continues to evolve, integrating advanced capabilities to support various sectors in making informed decisions. Data storytelling and preparation are crucial elements, enabling organizations to effectively communicate complex data insights. Real-time data visualization ensures agility, while data security safeguards sensitive information. Data dashboards facilitate data exploration and discovery, offering data-driven finance, strategy, and customer experience. Big data visualization tackles complex datasets, enabling data-driven decision making and innovation. Data blending and filtering streamline data integration and analysis. Data visualization software supports data transformation, cleaning, and aggregation, enhancing data-driven operations and healthcare. On-premises and cloud-based solutions cater to diverse business needs. Data governance, ethics, and literacy are integral components, ensuring data-driven product development, government, and education adhere to best practices. Natural language processing, machine learning, and visual analytics further enrich data-driven insights, enabling interactive charts and data reporting. Data connectivity and data-driven sales fuel business intelligence and marketing, while data discovery and data wrangling simplify data exploration and preparation. The market's continuous dynamism underscores the importance of data culture, data-driven innovation, and data-driven HR, as organizations strive to leverage data to gain a competitive edge.

How is this Data Visualization Tools Industry segmented?

The data visualization tools industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesCloudCustomer TypeLarge enterprisesSMEsComponentSoftwareServicesApplicationHuman resourcesFinanceOthersEnd-userBFSIIT and telecommunicationHealthcareRetailOthersGeographyNorth AmericaUSMexicoEuropeFranceGermanyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.The market has experienced notable expansion as businesses across diverse sectors acknowledge the significance of data analysis and representation to uncover valuable insights and inform strategic decisions. Data visualization plays a pivotal role in this domain. On-premises deployment, which involves implementing data visualization tools within an organization's physical infrastructure or dedicated data centers, is a popular choice. This approach offers organizations greater control over their data, ensuring data security, privacy, and adherence to data governance policies. It caters to industries dealing with sensitive data, subject to regulatory requirements, or having stringent security protocols that prohibit cloud-based solutions. Data storytelling, data preparation, data-driven product development, data-driven government, real-time data visualization, data security, data dashboards, data-driven finance, data-driven strategy, big data visualization, data-driven decision making, data blending, data filtering, data visualization software, data exploration, data-driven insights, data-driven customer experience, data mapping, data culture, data cleaning, data-driven operations, data aggregation, data transformation, data-driven healthcare, on-premises data visualization, data governance, data ethics, data discovery, natural language processing, data reporting, data visualization platforms, data-driven innovation, data wrangling, data-driven sales, data connectivit

An application (https://maps.seattle.gov/ACS-Neighborhood-Profiles) that presents U.S. Census Bureau 5-year American Community Survey data for census tracts in King County, Washington. Presented in a dashboard format with selectors for different time periods and levels of geographies, these snapshots are a curated set of data grouped into 12 topical profiles. Data is pulled from the demographic profiles DP02-DP05 and several supplemental tables for multiple nonoverlapping vintages starting in 2006-2010 and shown by the corresponding census tract vintage. Also includes the most recent release annually (usually released in December for the previous year) with the vintage identified in the "ACS Vintage" field. Use caution when looking at the most recent year as some data in the sample are the same as in the five-year period just prior.Data is presented in charts and graphs for pre-defined geographies as well as custom selection of census tracts either from a list or by selecting tracts on the map (shift-click to select multiple tracts). The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Charts allow downloading of the summarized data shown in the chart.The City of Seattle geography does not include the small portions of tracts 263, 264, 265, so city totals will vary slightly from published Census Bureau numbers.Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves. Get all the data for these profiles from SeattleGeoData. The full range of data products from the U.S. Census Bureau can be found by visiting the online data portal Explore Census Data.Interested in mapping the ACS? Check out this gallery for mapping apps and other census related dashboards.Important notes: ACS estimates are based on a survey mailed to a small percentage of houeholds and may carry substantial margins of error for small geographic areas or population groups. The margin of error (MOE) is an indicator of the reliability of the ACS estimate. Please see the Census Bureau guidance on calculating ....can't find something easy to link to....The 2010 and 2015 ACS vintages use the 2010 census tracts. The years 2020 and beyond use the 2020 census tracts. There were a significant number of new tracts created in 2020 so please use caution when comparing at the tract level between those time periods.Medians for aggregated areas are the weighted averages of the medians for the tracts selected.Monetary values are inflation-adjusted to the vintage year.Housing characteristics may not match other sources of housing data such as the King County Assessor or City of Seattle permit reports.Credits:Most icons sourced from the Noun Project.(Lars Meiertoberens, MRK, Gan Khoon Lay ....)

Electric Vehicles Dashboard Overview

This interactive dashboard, created by Yash Motiani, presents a concise visualization of electric vehicle trends and manufacturer comparisons. It is designed to offer insights into EV market distribution and vehicle characteristics, helping analysts and enthusiasts explore industry patterns efficiently. Online View:: https://public.tableau.com/app/profile/yash.motiani/viz/ElectricVehicles_17529501384880/Dashboard2?publish=yes**

📊 Key Features Vehicle Manufacturer Comparison: A horizontal bar chart ranks EV manufacturers based on vehicle count, spotlighting key players like Tesla, Chevrolet, and Nissan.

Battery Size Distribution: A histogram reveals the spread of battery sizes across the dataset, providing clarity on dominant EV battery capacities and potential outliers.

Vehicle Class Breakdown: A color-coded chart categorizes electric vehicles by class (e.g., SUV, Sedan, Truck), making it easy to identify which segments are gaining traction.

State-wise EV Presence: A filterable map highlights vehicle distribution across U.S. states, allowing users to track regional adoption patterns.

⚙️ Functionality Interactive Filters: Users can refine visual outputs by selecting specific years, manufacturers, or vehicle types to analyze targeted subsets of the data.

Hover Tooltips: Detailed metrics are available on hover, enabling quick access to individual data points without cluttering the visualization.

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