The ckanext-power-bi extension for CKAN integrates Power BI reports into CKAN resources. It introduces a new "Power BI" resource view, allowing users to embed and view Power BI reports directly within CKAN. The extension is designed to generate embed tokens with "View" permissions only, restricting interaction to viewing existing report bookmarks without edit capabilities. Key Features: Power BI Report Embedding: Enables embedding Power BI reports into CKAN resources, providing an interactive data visualization experience for CKAN users. View-Only Permissions: Generates embed tokens with "View" permissions, ensuring users can only view and interact with pre-existing report bookmarks and not modify the reports themselves. This means features such as editing are disabled and the experience is limited to viewing. Workspace ID Configuration: Requires the Power BI Workspace ID (Group ID) to correctly connect and display the desired reports. Optional Organization Name Configuration: Allows specifying the Azure organization (tenant) name, intended for possible future Power BI API enhancements (currently unused). i18n Support: Supports Power BI's Multiple-Language Reports feature, allowing the appropriate language to be displayed based on the user's CKAN locale. Provides configurations to facilitate the use of alternate i18n methods if internal translation is needed. MSI Authentication: Leverages ManagedIdentityCredential (MSI) to authenticate with Azure, simplifying authentication in Azure environments using system-assigned managed identities. Technical Integration: The extension integrates into CKAN by adding a new resource view type. It requires configuration settings in CKAN's config file (.ini) to specify the Power BI Workspace ID and optionally the organization name, as well as enabling the plugin in the ckan.plugins setting. It utilizes the Azure Identity library to handle authentication. Benefits & Impact: By integrating Power BI reports directly into CKAN, this extension enhances data accessibility and usability. Users can view and interact with data visualizations without leaving the CKAN environment, fostering a more seamless data exploration experience.

Power BI Consulting Service Market Outlook

The global Power BI Consulting Service market size was valued at approximately $1.2 billion in 2023 and is projected to reach around $4.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 16.3% during the forecast period. This substantial growth is driven by the increasing adoption of business intelligence and data analytics tools across numerous industries.

One of the primary growth factors for the Power BI Consulting Service market is the escalating demand for data-driven decision-making across various sectors. As organizations increasingly recognize the value of business intelligence tools in extracting actionable insights from raw data, the need for skilled consultants to implement and manage these tools has surged. Moreover, the proliferation of big data and the rising importance of data visualization techniques are further propelling market growth. Companies are looking to leverage Power BI's robust capabilities to transform complex data sets into intuitive and interactive dashboards, thereby enhancing their strategic decision-making processes.

Another significant driver for the market is the rapid digital transformation and the shift towards cloud-based solutions. With the advent of cloud computing, enterprises of all sizes are investing heavily in cloud infrastructure, which offers flexibility, scalability, and cost-effectiveness. Power BI, with its seamless integration with various cloud services and platforms, is becoming a go-to solution for businesses aiming to modernize their data strategies. Consequently, the demand for consultancy services to assist in the smooth adoption and integration of Power BI into existing IT ecosystems is on the rise.

The increasing trend of remote work and the need for real-time data access and collaboration have also contributed to market expansion. As businesses adapt to the new normal brought about by the COVID-19 pandemic, there is a growing requirement for tools that facilitate remote collaboration and instant data sharing. Power BI's capability to provide real-time analytics and its ease of use make it an attractive option for businesses looking to maintain productivity and efficiency in a distributed work environment. This has led to heightened demand for consulting services to ensure that organizations can effectively leverage Power BI to meet their dynamic needs.

Regionally, North America is expected to hold a dominant position in the Power BI Consulting Service market, driven by the presence of numerous technology giants and high adoption rates of advanced analytics tools. However, the Asia Pacific region is anticipated to witness the fastest growth, attributed to the burgeoning IT sector and increasing digital initiatives by governments and businesses. European markets, with their focus on regulatory compliance and data protection, also present significant opportunities for growth in the Power BI consulting domain.

In the realm of business intelligence, Win-Loss Analysis Service is gaining traction as a crucial tool for organizations striving to understand their competitive positioning. This service involves a detailed examination of past business deals, identifying factors that contributed to wins and losses. By leveraging insights from Win-Loss Analysis, companies can refine their strategies, enhance customer engagement, and improve their overall sales effectiveness. The integration of such analysis with Power BI enables businesses to visualize patterns and trends, offering a comprehensive view of market dynamics. As organizations seek to optimize their decision-making processes, the demand for Win-Loss Analysis Service is expected to rise, complementing the growth of Power BI consulting services.

Service Type Analysis

The Power BI Consulting Service market can be segmented by service type into Implementation, Training, Support, and Maintenance. Among these, the implementation segment is expected to hold the largest market share during the forecast period. The increasing complexity of data environments and the need for customized solutions are driving the demand for implementation services. Organizations often require expert assistance to configure and deploy Power BI according to their specific requirements, ensuring that the tool integrates seamlessly with existing systems and processes.

Training services are also gaining prominence as businesses strive to empower thei

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.

From 2016 to 2018, we surveyed the world’s largest natural history museum collections to begin mapping this globally distributed scientific infrastructure. The resulting dataset includes 73 institutions across the globe. It has:

• Basic institution data for the 73 contributing institutions, including estimated total collection sizes, geographic locations (to the city) and latitude/longitude, and Research Organization Registry (ROR) identifiers where available.

• Resourcing information, covering the numbers of research, collections and volunteer staff in each institution.

• Indicators of the presence and size of collections within each institution broken down into a grid of 19 collection disciplines and 16 geographic regions.

• Measures of the depth and breadth of individual researcher experience across the same disciplines and geographic regions.

This dataset contains the data (raw and processed) collected for the survey, and specifications for the schema used to store the data. It includes:

1. A diagram of the MySQL database schema.
2. A SQL dump of the MySQL database schema, excluding the data.
3. A SQL dump of the MySQL database schema with all data. This may be imported into an instance of MySQL Server to create a complete reconstruction of the database.
4. Raw data from each database table in CSV format.
5. A set of more human-readable views of the data in CSV format. These correspond to the database tables, but foreign keys are substituted for values from the linked tables to make the data easier to read and analyse.
6. A text file containing the definitions of the size categories used in the collection_unit table.

The global collections data may also be accessed at https://rebrand.ly/global-collections. This is a preliminary dashboard, constructed and published using Microsoft Power BI, that enables the exploration of the data through a set of visualisations and filters. The dashboard consists of three pages:

Institutional profile: Enables the selection of a specific institution and provides summary information on the institution and its location, staffing, total collection size, collection breakdown and researcher expertise.

Overall heatmap: Supports an interactive exploration of the global picture, including a heatmap of collection distribution across the discipline and geographic categories, and visualisations that demonstrate the relative breadth of collections across institutions and correlations between collection size and breadth. Various filters allow the focus to be refined to specific regions and collection sizes.

Browse: Provides some alternative methods of filtering and visualising the global dataset to look at patterns in the distribution and size of different types of collections across the global view.

Context

Analysis of sales reports of an organization for different products over the three years, and it presents through visualization. You can view that report at the following link.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

In this project, I conducted a comprehensive analysis of customer data using Power BI. The objective was to visualize and gain insights from the data, focusing on customer demographics and product categories.

📈The analysis includes the following key visualizations:

Customer Distribution by Age: illustrates the number of customers across different age groups, providing insights into the demographic distribution.

Customer Distribution by Time: This visualization shows the count of customers segmented by year, quarter, month, and day, helping identify trends over time.

Customer Distribution by Gender: displays the distribution of customers by gender, highlighting any significant differences.

Total Amount by Product Category: depicts the total revenue generated by each product category, allowing for easy comparison.

Quantity by Product Category: shows the total quantity of products sold in each category, helping to identify popular items.

The cards display key metrics:

Average Age: 41.39 Total Customers: 1000 Total Quantity Sold: 2514 Total Amount Sold: 465 000$ Total Transactions: 1000 Additionally, I implemented filters for product category, date, gender, quantity, and age, providing users with the ability to refine their analysis.

Findings:

The analysis of customer distribution by age reveals no specific relationship between age and the quantity of products sold. This indicates that purchasing behavior may not be strongly influenced by the customer’s age. There are notable peaks in the quantity sold on May 20, 2023, and again in July, suggesting higher purchasing activity during these periods. The customer distribution by gender shows that 49% of customers are female, while 51% are male. In terms of total amount sold by product category, electronics is the top category, generating the highest revenue, followed by clothing, with beauty ranking last. Similarly, when looking at quantity sold by product category, electronics makes up 33.77%, clothing is slightly higher at 35.56%, and beauty is the smallest category at 3.67%. This project demonstrates the power of Power BI in analyzing customer data and deriving actionable insights. The visualizations created provide a clear understanding of customer behavior and preferences, which can help businesses make informed decisions.

This Power BI dashboard shows the COVID-19 vaccination rate by key demographics including age groups, race and ethnicity, and sex for Tempe zip codes.Data Source: Maricopa County GIS Open Data weekly count of COVID-19 vaccinations. The data were reformatted from the source data to accommodate dashboard configuration. The Maricopa County Department of Public Health (MCDPH) releases the COVID-19 vaccination data for each zip code and city in Maricopa County at ~12:00 PM weekly on Wednesdays via the Maricopa County GIS Open Data website (https://data-maricopa.opendata.arcgis.com/). More information about the data is available on the Maricopa County COVID-19 Vaccine Data page (https://www.maricopa.gov/5671/Public-Vaccine-Data#dashboard). The dashboard’s values are refreshed at 3:00 PM weekly on Wednesdays. The most recent date included on the dashboard is available by hovering over the last point on the right-hand side of each chart. Please note that the times when the Maricopa County Department of Public Health (MCDPH) releases weekly data for COVID-19 vaccines may vary. If data are not released by the time of the scheduled dashboard refresh, the values may appear on the dashboard with the next data release, which may be one or more days after the last scheduled release.Dates: Updated data shows publishing dates which represents values from the previous calendar week (Sunday through Saturday). For more details on data reporting, please see the Maricopa County COVID-19 data reporting notes at https://www.maricopa.gov/5460/Coronavirus-Disease-2019.

The October 2017 dataset of the IMMAP-UNHCR Protection Movement Initiative for South Syria. Explore the PMI Power BI dashboard: https://app.powerbi.com/view?r=eyJrIjoiMjYyYjE5NWUtZTdmYi00ZDZhLTg2N2UtMDg1MzUxMWIxZDA2IiwidCI6ImY2ZjcwZjFiLTJhMmQtNGYzMC04NTJhLTY0YjhjZTBjMTlkNyIsImMiOjF9

I'm excited to share my latest project—an interactive Power BI dashboard that provides a comprehensive analysis of bike sales data from 2019 to 2024!

Key Highlights of the Dashboard:

📈 Sales Trend Analysis: Understand how bike sales have fluctuated over the years, with peaks in specific months that give us clues about seasonal demand. 🏢 Sales by Store Location: See how different cities like New York and Phoenix lead in terms of total sales revenue. 🚴‍♀️ Customer Demographics: Almost equal contributions from male and female customers—showing the broad appeal of our products. 💳 Payment Method Preferences: Breakdown of the most used payment methods, with insights that can help improve our customer experience. 📊 Revenue by Bike Model: A detailed look at which bike models drive the most revenue, helping guide product focus and inventory management. This dashboard was built to provide actionable insights into the sales performance and customer behavior of a large dataset of 100K records. It highlights the power of data visualization in turning numbers into strategic insights!

Why Power BI? Power BI's flexibility and interactive capabilities made it the ideal tool for visualizing the data, allowing users to drill down into specific details using slicers for bike models and time periods. 💡

Would love to hear your thoughts or any feedback on this project! If you’re interested in how this dashboard was built or want to discuss data visualization, feel free to reach out. Let’s transform data into stories that drive success! 🌟

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

With Oxylabs Datasets, you can count on:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

Chicago Crime Incident Data

65,000+ Records of Arrests, Locations, and Descriptions

By City of Chicago [source]

About this dataset

This dataset is a compilation of reported crimes that have taken place in the City of Chicago over the past year, and provides an invaluable insight into the criminal activity occurring within our city. Featuring more than 65,000 records of data, it contains information on the date of each incident, its location (down to the block level), type of crime committed (determined by FBI Crime Classification Codes) and whether or not an arrest has been made in connection with each crime. As this dataset reveals detailed information on crime incidents which may lead to personal identification, addresses are masked beyond block level and specific locations are not disclosed.

For additional questions regarding this dataset, please do not hesitate to reach out to The Research & Development Division at 312.745.6071 or RandDchicagopolice.com who will be more than happy to help answer any inquiries you may have about our data findings! All visualized maps should be considered approximate however—it is prohibited for any attempts to derive specific addresses from them as accuracy cannot be guaranteed with regards to mechanical or human error when collecting this data over time. So come join us as we explore a year's worth of criminal activities throughout Chicago!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This guide will provide an overview on how to use this dataset to analyze patterns or draw conclusions about crime incidents in and around Chicago.

Secondly, become familiar with columns names which appear at top most row of your opened file which helps you understand what kind of data is stored at each column such as - CASE# - Unique identifier for the crime incident., DATE OF OCCURRENCE - Date when crime incident occurred , BLOCK - Block where event took place , LOCATION DESCRIPTION- Description of location where incident happened . Through these columns name you can easily recognize what kind of data exists within that record/row. That’s why it’s important to get familiar with them first before diving into raw datasets because they’ll help make exploring and understanding large sets easier later on when we go further into illustrating charts & graphs using programs such as Tableau & Power BI or even spreadsheets (Excel). After understanding column names its time to explore further by digging deeper into each record/row and apply filters if required e.g below $100 value will show only those rows having value less than 100 thus it will filter entire dataset according to your requirement. Lastly analyse collected datasets either Visually through plotting graphs with help tableau software OR By using Mathematical mathematical equations based on research questions such as finding out average values after applying sum/avg functions from respective cells etc

Research Ideas

• Creating a visualization mapping tool to help visualize the types of crimes and their locations over time within Chicago.
• An analysis tool for city officials or police departments so they can understand correlations between crime type, geography, and other factors like weather changes or economic downturns in order to develop long-term plans for crime prevention.
• Developing an AI model that would be able to predict what areas may be more vulnerable for certain types of crimes or even predict crimes ahead of time based on the data from this dataset

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: crimes-one-year-prior-to-present-1.csv | Column name | Description | |:-------------------------|:------------------------------------------------------------------------------| | CASE# | Unique identifier for each crime incident (String) | | BLOCK | Block where the crime incident occurred (String) | | LOCATION DESCRIPTION | Description of where an incident took place (String) | | ARREST | Indicates if an arrest was made in connection with a crime incident (Boolean) | | DOMESTIC | Indicates if a reported incident is domestic related (Boolean) | | BEAT ...

Warning: Large file size (over 1GB). Each monthly data set is large (over 4 million rows), but can be viewed in standard software such as Microsoft WordPad (save by right-clicking on the file name and selecting 'Save Target As', or equivalent on Mac OSX). It is then possible to select the required rows of data and copy and paste the information into another software application, such as a spreadsheet. Alternatively, add-ons to existing software, such as the Microsoft PowerPivot add-on for Excel, to handle larger data sets, can be used. The Microsoft PowerPivot add-on for Excel is available from Microsoft http://office.microsoft.com/en-gb/excel/download-power-pivot-HA101959985.aspx Once PowerPivot has been installed, to load the large files, please follow the instructions below. Note that it may take at least 20 to 30 minutes to load one monthly file. 1. Start Excel as normal 2. Click on the PowerPivot tab 3. Click on the PowerPivot Window icon (top left) 4. In the PowerPivot Window, click on the "From Other Sources" icon 5. In the Table Import Wizard e.g. scroll to the bottom and select Text File 6. Browse to the file you want to open and choose the file extension you require e.g. CSV Once the data has been imported you can view it in a spreadsheet. What does the data cover? General practice prescribing data is a list of all medicines, dressings and appliances that are prescribed and dispensed each month. A record will only be produced when this has occurred and there is no record for a zero total. For each practice in England, the following information is presented at presentation level for each medicine, dressing and appliance, (by presentation name): - the total number of items prescribed and dispensed - the total net ingredient cost - the total actual cost - the total quantity The data covers NHS prescriptions written in England and dispensed in the community in the UK. Prescriptions written in England but dispensed outside England are included. The data includes prescriptions written by GPs and other non-medical prescribers (such as nurses and pharmacists) who are attached to GP practices. GP practices are identified only by their national code, so an additional data file - linked to the first by the practice code - provides further detail in relation to the practice. Presentations are identified only by their BNF code, so an additional data file - linked to the first by the BNF code - provides the chemical name for that presentation.