The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).

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...

Graph Data Integration Platform Market Outlook

According to our latest research, the global Graph Data Integration Platform market size reached USD 2.47 billion in 2024, demonstrating robust momentum across key verticals. The market is expected to expand at a remarkable CAGR of 18.9% from 2025 to 2033, reaching a forecasted value of USD 12.13 billion by 2033. This rapid growth is primarily driven by the increasing adoption of graph-based technologies to manage complex, interconnected data and the rising demand for advanced analytics capabilities across industries.

The surge in demand for graph data integration platforms is fundamentally linked to the exponential growth of data volumes and the increasing complexity of enterprise data environments. Organizations today are dealing with vast, diverse, and highly interconnected datasets that traditional relational databases struggle to handle efficiently. Graph-based solutions, by contrast, excel at representing and querying complex relationships, making them indispensable for applications such as fraud detection, recommendation engines, and network analysis. As digital transformation accelerates and businesses seek to extract deeper insights from their data, the need for robust graph data integration platforms is only expected to intensify.

Another vital growth factor for the graph data integration platform market is the expanding application of artificial intelligence and machine learning technologies. These advanced analytics tools rely heavily on the ability to process and analyze large volumes of interconnected data in real time. Graph data integration platforms enable organizations to seamlessly integrate disparate data sources, enhance data quality, and facilitate advanced analytics. This capability is particularly valuable in sectors such as BFSI, healthcare, and retail, where timely insights can drive competitive advantage and operational efficiency. The convergence of AI, machine learning, and graph data integration is poised to unlock new opportunities and fuel sustained market growth throughout the forecast period.

The growing emphasis on data governance, security, and compliance is also propelling the adoption of graph data integration platforms. As regulatory requirements become more stringent and organizations face increasing scrutiny over data privacy and integrity, the ability to track, manage, and audit complex data relationships becomes critical. Graph-based solutions offer unparalleled visibility into data lineage and dependencies, enabling organizations to meet compliance mandates more effectively. This, coupled with the rising threat of cyber-attacks and data breaches, is prompting enterprises to invest in advanced data integration solutions that can ensure both security and compliance.

From a regional perspective, North America continues to dominate the graph data integration platform market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, has witnessed widespread adoption of graph technologies across sectors such as finance, healthcare, and telecommunications. Meanwhile, Asia Pacific is emerging as a high-growth region, driven by rapid digitalization, expanding IT infrastructure, and increasing investments in big data and analytics. As organizations worldwide recognize the strategic value of graph data integration, the market is expected to witness significant growth across all major regions.

Component Analysis

The component segment of the graph data integration platform market is bifurcated into software and services, each playing a pivotal role in the overall ecosystem. The software component, which includes graph databases, integration tools, and visualization solutions, accounted for the largest share in 2024. This dominance is attributed to the continuous innovation in graph database technologies and the increasing demand for scalable, high-performance solutions that can handle complex data relationships. Leading vendors are investing heavily in R&D to enhance the capabilities of their software offerings, introducing features such as real-time analytics, automated data mapping, and advanced visualization tools. These advancements are enabling organizations to unlock deeper insights from their data and drive more informed decision-making.

On the services front, the market is witnessing robust

Master Data Graph Platforms Market Outlook

According to our latest research, the global Master Data Graph Platforms market size reached USD 2.14 billion in 2024, reflecting robust demand across diverse industries. The market is projected to expand at a CAGR of 18.2% from 2025 to 2033, culminating in a forecasted market size of USD 10.53 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of graph-based master data management solutions to address complex data relationships, enhance decision-making, and ensure regulatory compliance in an era defined by digital transformation and data-centric business models.

The primary growth factor fueling the Master Data Graph Platforms market is the explosive surge in enterprise data volumes and complexity. As organizations accumulate vast amounts of structured and unstructured data from multiple sources, the limitations of traditional relational databases have become increasingly apparent. Businesses are recognizing that graph platforms offer a highly flexible and scalable approach to modeling, integrating, and querying interconnected data. This capability is especially critical for organizations seeking to derive actionable insights from complex relationships, such as customer journeys, supply chain dependencies, and risk exposure. The market is further propelled by the need for real-time data integration and management, as enterprises strive to achieve a single, unified view of their master data across disparate systems and geographies.

Another significant driver is the growing emphasis on data governance, compliance, and risk management across regulated industries such as BFSI, healthcare, and government. Regulatory mandates like GDPR, HIPAA, and CCPA have heightened the importance of data lineage, transparency, and traceability. Master Data Graph Platforms excel at mapping data relationships and tracking data flows, enabling organizations to meet stringent compliance requirements while minimizing operational risk. The ability to visualize and audit data connections in real-time is a compelling value proposition, prompting enterprises to invest in advanced graph-based solutions that can adapt to evolving regulatory landscapes and safeguard sensitive information.

The proliferation of digital transformation initiatives, cloud migration, and the adoption of advanced analytics and artificial intelligence are also fueling market expansion. As organizations modernize their IT infrastructure and transition to cloud-native architectures, the demand for scalable, cloud-based master data management solutions is accelerating. The integration of Master Data Graph Platforms with AI and machine learning tools enhances the ability to uncover hidden patterns, automate data quality processes, and deliver personalized customer experiences. This convergence of technologies is creating new opportunities for innovation and competitive differentiation, further amplifying the market's growth trajectory.

From a regional perspective, North America continues to dominate the Master Data Graph Platforms market, accounting for the largest share in 2024, driven by early technology adoption, a mature digital ecosystem, and significant investments in data-driven initiatives. Europe is witnessing robust growth due to stringent data privacy regulations and the widespread adoption of advanced analytics in sectors such as finance, healthcare, and manufacturing. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, expanding IT infrastructure, and increasing demand for data management solutions among enterprises in China, India, Japan, and Southeast Asia. Latin America and the Middle East & Africa are also showing promising growth, albeit from a smaller base, as organizations in these regions embark on digital transformation journeys and seek to enhance operational efficiency through better data management.

Component Analysis

The Component segment of the Master Data Graph Platforms market is primarily categorized into software and services, both of which play pivotal roles in driving the adoption and effectiveness of graph-based master data management solutions. The software component encompasses graph databases, data modeling tools, integration frameworks, and analytics engines that form the backbone of modern master data platforms. These software solutions are designed to facilitate the ingestion, storage, querying, and

This dataset contains all current and active business licenses issued by the Department of Business Affairs and Consumer Protection. This dataset contains a large number of records /rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: 'ISSUE' is the record associated with the initial license application. 'RENEW' is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. 'C_LOC' is a change of location record. It means the business moved. 'C_CAPA' is a change of capacity record. Only a few license types my file this type of application. 'C_EXPA' only applies to businesses that have liquor licenses. It means the business location expanded.

LICENSE STATUS: 'AAI' means the license was issued.

Business license owners may be accessed at: http://data.cityofchicago.org/Community-Economic-Development/Business-Owners/ezma-pppn To identify the owner of a business, you will need the account number or legal name.

Data Owner: Business Affairs and Consumer Protection

Time Period: Current

Frequency: Data is updated daily

Graph Database-as-a-Service Market Outlook

According to our latest research, the global Graph Database-as-a-Service market size reached USD 2.1 billion in 2024, reflecting a robust expansion across multiple industries. The market is exhibiting a strong compound annual growth rate (CAGR) of 25.6%, and is projected to attain a value of USD 15.2 billion by 2033. This impressive growth trajectory is primarily driven by the increasing demand for highly scalable, flexible, and cloud-native data management solutions that can efficiently handle complex, interconnected datasets. The proliferation of digital transformation initiatives, surging adoption of advanced analytics, and the critical need for real-time data insights are further propelling the market forward, as organizations across sectors strive to optimize operations and unlock new business opportunities through graph-based technologies.

A significant factor fueling the expansion of the Graph Database-as-a-Service market is the escalating complexity of enterprise data environments. Traditional relational databases are often ill-equipped to manage the intricate relationships and dynamic data structures prevalent in modern business contexts. As a result, organizations are turning to graph databases for their ability to model, store, and analyze highly connected data efficiently. The rise of artificial intelligence, machine learning, and big data analytics has also intensified the need for data platforms that can seamlessly integrate with these technologies. Graph Database-as-a-Service solutions, with their cloud-native architecture and managed service offerings, enable businesses to rapidly deploy, scale, and maintain graph databases without the overhead of on-premises infrastructure, thus accelerating innovation and reducing operational costs.

Another key growth driver is the surge in demand for real-time analytics and personalized customer experiences across industries such as BFSI, retail, healthcare, and telecommunications. Graph databases excel at uncovering hidden patterns, detecting fraud, and enabling recommendation engines, which are critical for delivering tailored services and mitigating risks. Enterprises are leveraging Graph Database-as-a-Service platforms to enhance customer analytics, streamline risk and compliance management, and optimize network and IT operations. The flexibility of deployment models—including public, private, and hybrid cloud—further amplifies adoption, as organizations can select the architecture that best aligns with their security, scalability, and regulatory requirements. The integration of graph databases with existing IT ecosystems and the availability of robust APIs and developer tools are making it increasingly accessible for businesses of all sizes to harness the power of connected data.

From a regional perspective, North America continues to dominate the Graph Database-as-a-Service market, owing to its advanced technological infrastructure, early adoption of cloud computing, and a vibrant ecosystem of innovative startups and established enterprises. Europe is witnessing rapid growth, driven by stringent data privacy regulations and the increasing digitalization of industries. The Asia Pacific region is emerging as a significant growth engine, propelled by the expansion of e-commerce, financial services, and healthcare sectors, coupled with substantial investments in digital transformation initiatives. As organizations worldwide recognize the strategic value of graph data management, the market is expected to experience widespread adoption across both developed and emerging economies, with tailored solutions catering to diverse industry verticals and regulatory landscapes.

Component Analysis

The Graph Database-as-a-Service market is segmented by component into software and services, each playing a pivotal role in shaping the overall market dynamics. The software segment encompasses the core graph database platforms and associated tools that facilitate data modeling, querying, visualization, and integration. These platforms are designed to deliver high performance, scalability, and ease of use, enabling organizations to manage complex relationships and large volumes of interconnected data seamlessly. Leading vendors are continuously innovating, introducing advanced features such as multi-model support, enhanced security, and automated scaling, which are driving widespread adoption across various industry verticals. The software component is particularly critical for enterprise

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

This dataset contains sales transaction records used to create an interactive Excel Sales Performance Dashboard for business analytics practice.

It includes six columns capturing essential sales metrics such as date, region, product, quantity, sales revenue, and profit. The data is structured to help analysts and learners explore data visualization, PivotTable summarization, and dashboard design concepts in Excel.

The dataset was created for educational and demonstration purposes to help users:

1. Build dashboards that visualize total sales and profit trends
2. Identify top-performing products and high-profit regions
3. Practice Excel-based business analytics workflows

Columns: Date – Transaction date (daily sales record) Region – Geographic area of the sale (East, West, North, South) Product – Product category or item sold Sales – Total revenue generated from the sale (USD) Profit – Net profit made per transaction Quantity – Number of units sold

Typical uses include: Excel or Power BI dashboard projects PivotTable practice for business reporting Data cleaning and chart-building exercises Portfolio development for business analytics students Built and tested in Microsoft Excel using PivotTables, Charts, and Conditional Formatting.

Graph Neural Network Platform Market Outlook

According to our latest research, the global Graph Neural Network (GNN) Platform market size is valued at USD 1.08 billion in 2024, underscoring its rapid ascent in the artificial intelligence domain. The market is projected to expand at a robust CAGR of 32.4% from 2025 to 2033, reaching an estimated USD 13.5 billion by 2033. This remarkable growth trajectory is fueled by the increasing adoption of graph-based deep learning for complex data analytics, especially in sectors such as BFSI, healthcare, and IT & telecommunications, where traditional AI models fall short in capturing intricate data relationships.

One of the primary growth drivers for the Graph Neural Network Platform market is the exponential increase in connected data and the need for advanced analytics to derive actionable insights from it. With the proliferation of IoT devices, social networks, and enterprise systems, organizations are accumulating vast volumes of data with complex interdependencies. GNN platforms excel in analyzing these intricate networks, enabling businesses to uncover hidden patterns, detect anomalies, and optimize decision-making processes. The ability of GNNs to model relationships in data far surpasses conventional machine learning algorithms, making them indispensable for applications like fraud detection, recommendation systems, and knowledge graph construction.

Moreover, the growing emphasis on personalized customer experiences and targeted marketing strategies is accelerating the adoption of Graph Neural Network Platforms in retail, e-commerce, and financial services. Enterprises are leveraging GNNs to enhance recommendation engines, predict customer behavior, and deliver hyper-personalized offerings, thereby increasing customer engagement and retention. In the healthcare sector, GNNs are revolutionizing drug discovery and patient care by facilitating the analysis of biological networks, protein interactions, and disease pathways. This technological edge, combined with increasing investments in AI research and development, is propelling the market forward at an unprecedented pace.

Another significant factor contributing to the market’s growth is the rapid evolution of cloud computing and scalable infrastructure. Cloud-based deployment modes are making GNN platforms more accessible to organizations of all sizes, eliminating the need for heavy upfront investments in hardware and specialized personnel. The integration of GNNs with big data analytics, edge computing, and other AI technologies is further expanding their use cases across industries. As regulatory frameworks mature and data privacy concerns are addressed, adoption rates are expected to soar, particularly in regions with strong digital transformation initiatives.

From a regional perspective, North America currently dominates the Graph Neural Network Platform market due to its robust technological ecosystem, high concentration of AI startups, and significant R&D investments. However, the Asia Pacific region is emerging as a formidable contender, driven by rapid digitization, government support for AI initiatives, and the presence of large-scale enterprises in countries like China, India, and Japan. Europe also represents a substantial share, bolstered by stringent data regulations and a focus on innovation in healthcare and finance. Latin America and the Middle East & Africa are gradually catching up, fueled by growing awareness and adoption of advanced analytics solutions.

Component Analysis

The Component segment of the Graph Neural Network Platform market is bifurcated into Software and Services, each playing a pivotal role in the ecosystem. The Software sub-segment dominates the market, accounting for over 68% of the total revenue in 2024. This dominance is attributed to the increasing demand for robust, scalable, and easy-to-integrate GNN frameworks and libraries that can be tailored for diverse use cases. Software solutions are continuously evolving to offer greater flexibility, interoperability with existing data systems, and user-friendly interfaces that cater to both data scientists and business analysts. The proliferation of open-source GNN libraries and the integration of proprietary features by leading vendors are further enhancing the value proposition for enterprises.<br

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

This dataset contains all current and active business licenses issued by the Department of Business Affairs and Consumer Protection. This dataset contains a large number of records /rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: 'ISSUE' is the record associated with the initial license application. 'RENEW' is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. 'C_LOC' is a change of location record. It means the business moved. 'C_CAPA' is a change of capacity record. Only a few license types my file this type of application. 'C_EXPA' only applies to businesses that have liquor licenses. It means the business location expanded.

LICENSE STATUS: 'AAI' means the license was issued.

Business license owners may be accessed at: http://data.cityofchicago.org/Community-Economic-Development/Business-Owners/ezma-pppn To identify the owner of a business, you will need the account number or legal name.

Data Owner: Business Affairs and Consumer Protection

Time Period: Current

Frequency: Data is updated daily

This data set is perfect for practicing your analytical skills for Power BI, Tableau, Excel, or transform it into a CSV to practice SQL.

This use case mimics transactions for a fictional eCommerce website named EverMart Online. The 3 tables in this data set are all logically connected together with IDs.

My Power BI Use Case Explanation - Using Microsoft Power BI, I made dynamic data visualizations for revenue reporting and customer behavior reporting.

Revenue Reporting Visuals - Data Card Visual that dynamically shows Total Products Listed, Total Unique Customers, Total Transactions, and Total Revenue by Total Sales, Product Sales, or Categorical Sales. - Line Graph Visual that shows Total Revenue by Month of the entire year. This graph also changes to calculate Total Revenue by Month for the Total Sales by Product and Total Sales by Category if selected. - Bar Graph Visual showcasing Total Sales by Product. - Donut Chart Visual showcasing Total Sales by Category of Product.

Customer Behavior Reporting Visuals - Data Card Visual that dynamically shows Total Products Listed, Total Unique Customers, Total Transactions, and Total Revenue by Total or by continent selected on the map. - Interactive Map Visual showing key statistics for the continent selected. - The key statistics are presented on the tool tip when you select a continent, and the following statistics show for that continent: - Continent Name - Customer Total - Percentage of Products Sold - Percentage of Total Customers - Percentage of Total Transactions - Percentage of Total Revenue

This article describes a free, open-source collection of templates for the popular Excel (2013, and later versions) spreadsheet program. These templates are spreadsheet files that allow easy and intuitive learning and the implementation of practical examples concerning descriptive statistics, random variables, confidence intervals, and hypothesis testing. Although they are designed to be used with Excel, they can also be employed with other free spreadsheet programs (changing some particular formulas). Moreover, we exploit some possibilities of the ActiveX controls of the Excel Developer Menu to perform interactive Gaussian density charts. Finally, it is important to note that they can be often embedded in a web page, so it is not necessary to employ Excel software for their use. These templates have been designed as a useful tool to teach basic statistics and to carry out data analysis even when the students are not familiar with Excel. Additionally, they can be used as a complement to other analytical software packages. They aim to assist students in learning statistics, within an intuitive working environment. Supplementary materials with the Excel templates are available online.

Manufacturing Knowledge Graph Platform Market Outlook

According to our latest research, the global Manufacturing Knowledge Graph Platform market size reached USD 1.42 billion in 2024, reflecting a robust trajectory as enterprises accelerate digital transformation initiatives. The market is projected to expand at a CAGR of 24.1% from 2025 to 2033, reaching an estimated USD 11.52 billion by 2033. This remarkable growth is primarily driven by the manufacturing sector’s increasing need for advanced data integration, real-time insights, and enhanced decision-making capabilities, as knowledge graph platforms become central to Industry 4.0 strategies.

The proliferation of connected devices and the exponential growth of industrial data are among the key factors propelling the adoption of knowledge graph platforms in manufacturing. Modern manufacturing environments generate vast amounts of heterogeneous data from sensors, machines, enterprise systems, and external sources. Traditional data management tools often struggle to provide context and actionable insights from such complex datasets. Knowledge graph platforms, with their ability to semantically integrate and relate diverse data points, enable manufacturers to unlock hidden value, improve operational transparency, and drive smarter, data-driven decisions across the production lifecycle. This capability is particularly crucial for organizations seeking to optimize processes, ensure quality, and maintain competitiveness in an increasingly digitalized marketplace.

Another significant growth driver is the rising emphasis on predictive maintenance and process optimization. Manufacturers are under constant pressure to reduce downtime, minimize costs, and enhance asset utilization. Knowledge graph platforms facilitate the creation of comprehensive digital twins and interconnected knowledge networks that can analyze patterns, detect anomalies, and predict equipment failures before they occur. By leveraging advanced AI and machine learning algorithms, these platforms empower manufacturers to transition from reactive to proactive maintenance strategies, thereby reducing unplanned outages and extending asset lifespans. Moreover, knowledge graphs support continuous process improvement by providing holistic views of workflows, dependencies, and bottlenecks, enabling agile responses to changing market demands.

Regulatory compliance and supply chain resilience have also emerged as critical considerations fueling market growth. Increasingly stringent regulations in industries such as automotive, pharmaceuticals, and food & beverage require manufacturers to maintain detailed records, ensure traceability, and demonstrate accountability across their operations. Knowledge graph platforms excel at mapping complex relationships between entities, processes, and compliance requirements, simplifying audit trails and risk management. Additionally, the disruptions caused by global events have highlighted the importance of resilient and transparent supply chains. By integrating internal and external data sources, knowledge graphs help manufacturers identify vulnerabilities, optimize sourcing strategies, and enhance collaboration with suppliers and partners.

From a regional perspective, North America currently leads the Manufacturing Knowledge Graph Platform market, accounting for a significant share due to the early adoption of advanced digital technologies and the presence of major manufacturing hubs. Europe follows closely, supported by strong government initiatives promoting Industry 4.0 and digital innovation. Meanwhile, the Asia Pacific region is experiencing the fastest growth, driven by rapid industrialization, expanding manufacturing bases, and increasing investments in smart factory solutions across China, Japan, and India. The Middle East & Africa and Latin America are also witnessing growing interest, particularly as manufacturers in these regions seek to enhance operational efficiency and global competitiveness.

Component Analysis

The Manufacturing Knowledge Graph Platform market is segmented by component into Software and Services. The software segment dominates the market, accounting for over 65% of the total revenue in 2024. This dominance is attributed to the critical role of software solutions in enabling semantic data integration, knowledge modeling, and real-t

Graph Database Market Outlook

According to our latest research, the global graph database market size in 2024 stands at USD 2.92 billion, with a robust compound annual growth rate (CAGR) of 21.6% projected from 2025 to 2033. By the end of 2033, the market is expected to reach approximately USD 21.1 billion. The rapid expansion of this market is primarily driven by the rising need for advanced data analytics, real-time big data processing, and the growing adoption of artificial intelligence and machine learning across various industry verticals. As organizations continue to seek innovative solutions to manage complex and interconnected data, the demand for graph database technologies is accelerating at an unprecedented pace.

One of the most significant growth factors for the graph database market is the exponential increase in data complexity and volume. Traditional relational databases often struggle to efficiently handle highly connected data, which is becoming more prevalent in modern business environments. Graph databases excel at managing relationships between data points, making them ideal for applications such as fraud detection, social network analysis, and recommendation engines. The ability to visualize and query data relationships in real-time provides organizations with actionable insights, enabling faster and more informed decision-making. This capability is particularly valuable in sectors like BFSI, healthcare, and e-commerce, where understanding intricate data connections can lead to substantial competitive advantages.

Another key driver fueling market growth is the widespread digital transformation initiatives undertaken by enterprises worldwide. As businesses increasingly migrate to cloud-based infrastructures and adopt advanced analytics tools, the need for scalable and flexible database solutions becomes paramount. Graph databases offer seamless integration with cloud platforms, supporting both on-premises and cloud deployment models. This flexibility allows organizations to efficiently manage growing data workloads while ensuring security and compliance. Additionally, the proliferation of IoT devices and the surge in unstructured data generation further amplify the demand for graph database solutions, as they are uniquely equipped to handle dynamic and heterogeneous data sources.

The integration of artificial intelligence and machine learning with graph databases is also a pivotal growth factor. AI-driven analytics require robust data models capable of uncovering hidden patterns and relationships within vast datasets. Graph databases provide the foundational infrastructure for such applications, enabling advanced features like predictive analytics, anomaly detection, and personalized recommendations. As more organizations invest in AI-powered solutions to enhance customer experiences and operational efficiency, the adoption of graph database technologies is expected to surge. Furthermore, continuous advancements in graph processing algorithms and the emergence of open-source graph database platforms are lowering entry barriers, fostering innovation, and expanding the marketÂ’s reach.

Graph Analytics is becoming an essential component in the realm of graph databases, offering powerful tools to analyze and visualize complex data relationships. As organizations strive to extract deeper insights from their data, graph analytics enables them to uncover hidden patterns and trends that are not easily detectable with traditional analytics methods. This capability is particularly beneficial for sectors such as finance, healthcare, and retail, where understanding intricate connections can lead to more informed strategic decisions. By leveraging graph analytics, businesses can enhance their predictive modeling, optimize operations, and ultimately drive competitive advantage in a data-driven world.

From a regional perspective, North America currently dominates the graph database market, owing to the early adoption of advanced technologies and the presence of major industry players. However, the Asia Pacific region is anticipated to witness the highest growth rate over the forecast period, driven by rapid digitalization, increasing investments in IT infrastructure, and the rising demand for data-driven decision-making across emerging economies. Europe also holds a significant share, supported by stringent dat

Graph Database for Telecom Networks Market Outlook

According to our latest research, the global graph database for telecom networks market size is valued at USD 1.34 billion in 2024, reflecting a robust adoption rate across the telecom sector. The market is experiencing a strong upward trajectory with a CAGR of 22.7% from 2025 to 2033. By 2033, the market is projected to reach a substantial USD 10.15 billion, driven by the increasing complexity of telecom networks and the urgent need for advanced data management and analytics solutions. The primary growth factor is the surging demand for real-time network analytics and fraud detection capabilities, which are critical for telecom operators seeking operational efficiency and competitive advantage.

The rapid proliferation of connected devices, 5G rollouts, and the exponential growth of data traffic are fundamentally transforming the telecom industry landscape. Telecom networks are evolving into highly complex, dynamic ecosystems that generate vast amounts of interconnected data. Traditional relational databases are often inadequate for handling such intricate relationships and real-time analytics requirements. Graph database solutions are uniquely positioned to address these challenges by enabling telecom operators to model, analyze, and visualize complex network topologies, customer interactions, and transactional data with unparalleled speed and flexibility. This technological shift is a key growth driver, as telecom providers increasingly seek scalable, agile, and intelligent data management platforms to enhance customer experience, optimize network performance, and accelerate digital transformation initiatives.

Another significant growth factor for the graph database for telecom networks market is the escalating threat landscape, particularly in the domain of fraud detection and cybersecurity. Telecom operators are frequent targets of sophisticated fraud schemes, including SIM card cloning, subscription fraud, and network intrusion attempts. Graph databases excel at identifying hidden patterns, relationships, and anomalies within massive datasets, enabling telecom companies to detect and mitigate fraud in real time. The ability to perform advanced analytics on interconnected data sets is empowering telecom operators to proactively safeguard their networks, reduce financial losses, and comply with stringent regulatory requirements. As the complexity of cyber threats intensifies, the adoption of graph database solutions for security and fraud prevention is expected to surge, further fueling market growth.

The growing emphasis on customer-centricity and personalized service delivery is also propelling market expansion. Telecom operators are leveraging graph databases to gain a 360-degree view of customer journeys, preferences, and interactions across multiple touchpoints. This holistic understanding facilitates targeted marketing, churn prediction, and tailored service offerings, which are essential for customer retention and revenue growth in a highly competitive market. The convergence of telecom networks with emerging technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT) is amplifying the need for graph-based analytics, as these technologies rely on real-time, context-aware insights derived from complex data relationships. As a result, the integration of graph databases into telecom network architectures is becoming a strategic imperative for industry leaders.

From a regional perspective, North America currently leads the global graph database for telecom networks market, accounting for the largest revenue share in 2024. The region’s dominance is attributed to the early adoption of advanced analytics technologies, robust digital infrastructure, and the presence of major telecom and technology companies. Asia Pacific is emerging as the fastest-growing region, driven by massive investments in 5G networks, expanding mobile subscriber base, and increasing focus on digital transformation across telecom operators. Europe is also witnessing significant adoption of graph database solutions, particularly in the context of regulatory compliance and network optimization. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, supported by ongoing telecom sector modernization and rising demand for advanced data analytics. The global market outlook remains highly promising, with all regions poised to contribute to sustained growth over the forecast period.<b

Analyzing Coffee Shop Sales: Excel Insights 📈

In my first Data Analytics Project, I Discover the secrets of a fictional coffee shop's success with my data-driven analysis. By Analyzing a 5-sheet Excel dataset, I've uncovered valuable sales trends, customer preferences, and insights that can guide future business decisions. 📊☕

DATA CLEANING 🧹

• REMOVED DUPLICATES OR IRRELEVANT ENTRIES: Thoroughly eliminated duplicate records and irrelevant data to refine the dataset for analysis.

• FIXED STRUCTURAL ERRORS: Rectified any inconsistencies or structural issues within the data to ensure uniformity and accuracy.

• CHECKED FOR DATA CONSISTENCY: Verified the integrity and coherence of the dataset by identifying and resolving any inconsistencies or discrepancies.

DATA MANIPULATION 🛠️

• UTILIZED LOOKUPS: Used Excel's lookup functions for efficient data retrieval and analysis.

• IMPLEMENTED INDEX MATCH: Leveraged the Index Match function to perform advanced data searches and matches.

• APPLIED SUMIFS FUNCTIONS: Utilized SumIFs to calculate totals based on specified criteria.

• CALCULATED PROFITS: Used relevant formulas and techniques to determine profit margins and insights from the data.

PIVOTING THE DATA 𝄜

• CREATED PIVOT TABLES: Utilized Excel's PivotTable feature to pivot the data for in-depth analysis.

• FILTERED DATA: Utilized pivot tables to filter and analyze specific subsets of data, enabling focused insights. Specially used in “PEAK HOURS” and “TOP 3 PRODUCTS” charts.

VISUALIZATION 📊

• KEY INSIGHTS: Unveiled the grand total sales revenue while also analyzing the average bill per person, offering comprehensive insights into the coffee shop's performance and customer spending habits.

• SALES TREND ANALYSIS: Used Line chart to compute total sales across various time intervals, revealing valuable insights into evolving sales trends.

• PEAK HOUR ANALYSIS: Leveraged Clustered Column chart to identify peak sales hours, shedding light on optimal operating times and potential staffing needs.

• TOP 3 PRODUCTS IDENTIFICATION: Utilized Clustered Bar chart to determine the top three coffee types, facilitating strategic decisions regarding inventory management and marketing focus.

*I also used a Timeline to visualize chronological data trends and identify key patterns over specific times.

While it's a significant milestone for me, I recognize that there's always room for growth and improvement. Your feedback and insights are invaluable to me as I continue to refine my skills and tackle future projects. I'm eager to hear your thoughts and suggestions on how I can make my next endeavor even more impactful and insightful.

THANKS TO: WsCube Tech Mo Chen Alex Freberg

TOOLS USED: Microsoft Excel

DataAnalytics #DataAnalyst #ExcelProject #DataVisualization #BusinessIntelligence #SalesAnalysis #DataAnalysis #DataDrivenDecisions

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

Graph Database Platform Market Outlook

According to our latest research, the global graph database platform market size reached USD 2.5 billion in 2024, demonstrating robust demand across various sectors. The market is projected to expand at a CAGR of 22.7% from 2025 to 2033, reaching an estimated value of USD 19.1 billion by 2033. This impressive growth is primarily attributed to the increasing need for advanced data analytics, real-time intelligence, and the proliferation of connected data across enterprises worldwide.

A key factor propelling the growth of the graph database platform market is the surging adoption of big data analytics and artificial intelligence in business operations. As organizations manage ever-growing volumes of complex and connected data, traditional relational databases often fall short in terms of efficiency and scalability. Graph database platforms offer a more intuitive and efficient way to model, store, and query highly connected data, enabling faster insights and supporting sophisticated applications such as fraud detection, recommendation engines, and social network analysis. The need for real-time analytics and decision-making is driving enterprises to invest heavily in graph database technologies, further accelerating market expansion.

Another significant driver for the graph database platform market is the increasing incidence of cyber threats and fraudulent activities, especially within the BFSI and e-commerce sectors. Graph databases excel at uncovering hidden patterns, relationships, and anomalies within vast datasets, making them invaluable for fraud detection and risk management. Financial institutions are leveraging these platforms to identify suspicious transactions and prevent financial crimes, while retailers use them to optimize customer experience and personalize recommendations. The versatility of graph databases in supporting diverse use cases across multiple industry verticals is a major contributor to their rising adoption and market growth.

The rapid digital transformation of enterprises, coupled with the shift towards cloud-based solutions, is also fueling the graph database platform market. Cloud deployment offers scalability, flexibility, and cost-effectiveness, allowing organizations to seamlessly integrate graph databases into their existing IT infrastructure. The growing prevalence of Internet of Things (IoT) devices and the emergence of Industry 4.0 have further increased the demand for platforms capable of handling complex, interconnected data. As businesses strive for agility and innovation, graph database platforms are becoming a strategic asset for gaining competitive advantage.

From a regional perspective, North America currently dominates the graph database platform market, driven by the presence of leading technology providers, early adoption of advanced analytics, and substantial investments in digital infrastructure. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid economic development, expanding IT sectors, and increasing awareness of data-driven decision-making. Europe also holds a significant market share, supported by strong regulatory frameworks and widespread digital transformation initiatives. The market landscape is highly dynamic, with regional trends influenced by technological advancements, regulatory policies, and industry-specific demands.

Component Analysis

The graph database platform market is segmented by component into software and services. The software segment holds the largest share, as organizations increasingly deploy advanced graph database solutions to manage and analyze complex data relationships. These software platforms provide robust features such as data modeling, visualization, and high-performance querying, enabling users to derive actionable insights from connected data. Vendors are continuously enhancing their offerings with AI and machine learning capabilities, making graph database software indispensable for modern data-driven enterprises.
</p&g