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.

This resource, a MS Excel refresher, extends the level for this Data Nugget. Students are given an Excel workbook with the data and asked to graph and calculate diversity using Excel functions (rather than drawing graphs by hand as in the original data nugget). The data set used is the same. I use this activity in an upper division Environmental Science course for majors that focuses on Restoration Ecology. The simplicity of the data set and the comparisons of reptile diversity among urban, non-urban and urban rehabilitated lend for a great example for doing calculations in spreadsheets.

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 Data Integration Platform Market Outlook

According to our latest research, the global graph data integration platform market size reached USD 2.1 billion in 2024, reflecting robust adoption across industries. The market is projected to grow at a CAGR of 18.4% from 2025 to 2033, reaching approximately USD 10.7 billion by 2033. This significant growth is fueled by the increasing need for advanced data management and analytics solutions that can handle complex, interconnected data across diverse organizational ecosystems. The rapid digital transformation and the proliferation of big data have further accelerated the demand for graph-based data integration platforms.

The primary growth factor driving the graph data integration platform market is the exponential increase in data complexity and volume within enterprises. As organizations collect vast amounts of structured and unstructured data from multiple sources, traditional relational databases often struggle to efficiently process and analyze these data sets. Graph data integration platforms, with their ability to map, connect, and analyze relationships between data points, offer a more intuitive and scalable solution. This capability is particularly valuable in sectors such as BFSI, healthcare, and telecommunications, where real-time data insights and dynamic relationship mapping are crucial for decision-making and operational efficiency.

Another significant driver is the growing emphasis on advanced analytics and artificial intelligence. Modern enterprises are increasingly leveraging AI and machine learning to extract actionable insights from their data. Graph data integration platforms enable the creation of knowledge graphs and support complex analytics, such as fraud detection, recommendation engines, and risk assessment. These platforms facilitate seamless integration of disparate data sources, enabling organizations to gain a holistic view of their operations and customers. As a result, investment in graph data integration solutions is rising, particularly among large enterprises seeking to enhance their analytics capabilities and maintain a competitive edge.

The surge in regulatory requirements and compliance mandates across various industries also contributes to the expansion of the graph data integration platform market. Organizations are under increasing pressure to ensure data accuracy, lineage, and transparency, especially in highly regulated sectors like finance and healthcare. Graph-based platforms excel in tracking data provenance and relationships, making it easier for companies to comply with regulations such as GDPR, HIPAA, and others. Additionally, the shift towards hybrid and multi-cloud environments further underscores the need for robust data integration tools capable of operating seamlessly across different infrastructures, further boosting market growth.

From a regional perspective, North America currently dominates the graph data integration platform market, accounting for the largest share due to early adoption of advanced data technologies, a strong presence of key market players, and significant investments in digital transformation initiatives. However, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by rapid industrialization, expanding IT infrastructure, and increasing adoption of cloud-based solutions among enterprises in countries like China, India, and Japan. Europe also remains a significant contributor, supported by stringent data privacy regulations and a mature digital economy.

Component Analysis

The component segment of the graph data integration platform market is bifurcated into software and services. The software segment currently commands the largest market share, reflecting the critical role of robust graph database engines, visualization tools, and integration frameworks in managing and analyzing complex data relationships. These software solutions are designed to deliver high scalability, flexibility, and real-time proces

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

Snapshot img

Graph Database Market Size 2025-2029

The graph database market size is valued to increase by USD 11.24 billion, at a CAGR of 29% from 2024 to 2029. Open knowledge network gaining popularity will drive the graph database market.

Market Insights

North America dominated the market and accounted for a 46% growth during the 2025-2029.
By End-user - Large enterprises segment was valued at USD 1.51 billion in 2023
By Type - RDF segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 670.01 million 
Market Future Opportunities 2024: USD 11235.10 million
CAGR from 2024 to 2029 : 29%

Market Summary

The market is experiencing significant growth due to the increasing demand for low-latency query capabilities and the ability to handle complex, interconnected data. Graph databases are deployed in both on-premises data centers and cloud regions, providing flexibility for businesses with varying IT infrastructures. One real-world business scenario where graph databases excel is in supply chain optimization. In this context, graph databases can help identify the shortest path between suppliers and consumers, taking into account various factors such as inventory levels, transportation routes, and demand patterns. This can lead to increased operational efficiency and reduced costs.
However, the market faces challenges such as the lack of standardization and programming flexibility. Graph databases, while powerful, require specialized skills to implement and manage effectively. Additionally, the market is still evolving, with new players and technologies emerging regularly. Despite these challenges, the potential benefits of graph databases make them an attractive option for businesses seeking to gain a competitive edge through improved data management and analysis.

What will be the size of the Graph Database Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market is an evolving landscape, with businesses increasingly recognizing the value of graph technology for managing complex and interconnected data. According to recent research, the adoption of graph databases is projected to grow by over 20% annually, surpassing traditional relational databases in certain use cases. This trend is particularly significant for industries requiring advanced data analysis, such as finance, healthcare, and telecommunications. Compliance is a key decision area where graph databases offer a competitive edge. By modeling data as nodes and relationships, organizations can easily trace and analyze interconnected data, ensuring regulatory requirements are met. Moreover, graph databases enable real-time insights, which is crucial for budgeting and product strategy in today's fast-paced business environment.
Graph databases also provide superior performance compared to traditional databases, especially in handling complex queries involving relationships and connections. This translates to significant time and cost savings, making it an attractive option for businesses seeking to optimize their data management infrastructure. In conclusion, the market is experiencing robust growth, driven by its ability to handle complex data relationships and offer real-time insights. This trend is particularly relevant for industries dealing with regulatory compliance and seeking to optimize their data management infrastructure.

Unpacking the Graph Database Market Landscape

In today's data-driven business landscape, the adoption of graph databases has surged due to their unique capabilities in handling complex network data modeling. Compared to traditional relational databases, graph databases offer a significant improvement in query performance for intricate relationship queries, with some reports suggesting up to a 500% increase in query response time. Furthermore, graph databases enable efficient data lineage tracking, ensuring regulatory compliance and enhancing data version control. Graph databases, such as property graph models and RDF databases, facilitate node relationship management and real-time graph processing, making them indispensable for industries like finance, healthcare, and social media. With the rise of distributed and knowledge graph databases, organizations can achieve scalability and performance improvements, handling massive datasets with ease. Security, indexing, and deployment are essential aspects of graph databases, ensuring data integrity and availability. Query performance tuning and graph analytics libraries further enhance the value of graph databases in data integration and business intelligence applications. Ultimately, graph databases offer a powerful alternative to NoSQL databases, providing a more flexible and efficient approach to managing complex data relationships.

Key Market Drivers Fueling Growth

The growing popularity o

Industrial Knowledge Graph Platform Market Outlook

Based on our latest research, the global Industrial Knowledge Graph Platform market size was valued at USD 1.23 billion in 2024, with a robust compound annual growth rate (CAGR) of 25.8% expected through the forecast period. With this trajectory, the market is projected to reach USD 9.08 billion by 2033. This exponential growth is fueled by the surge in industrial digitalization, the increasing need for contextual data integration, and the adoption of artificial intelligence (AI) and machine learning (ML) across industrial sectors. The market’s rapid expansion is underpinned by the critical role that knowledge graph platforms play in unifying disparate data sources, driving operational efficiency, and enabling advanced analytics for enterprise decision-making.

One of the primary growth drivers for the Industrial Knowledge Graph Platform market is the escalating demand for real-time, context-rich insights across industrial operations. As industries such as manufacturing, energy, and automotive embrace Industry 4.0 principles, the volume and complexity of data generated from interconnected devices and systems have increased dramatically. Knowledge graph platforms excel at integrating structured and unstructured data from diverse sources, enabling organizations to create a comprehensive, interconnected view of their assets, processes, and supply chains. This capability is crucial for enhancing operational transparency, optimizing resource allocation, and supporting predictive analytics, which collectively contribute to improved productivity and reduced downtime.

Another key factor propelling market growth is the widespread adoption of AI and ML technologies within industrial environments. Industrial knowledge graph platforms serve as foundational infrastructure for advanced AI applications by providing a semantic layer that contextualizes data relationships. This semantic enrichment empowers AI-driven solutions to deliver more accurate predictions, uncover hidden patterns, and automate complex decision-making processes. As organizations strive to achieve greater agility and resilience in the face of global supply chain disruptions and evolving regulatory requirements, knowledge graph platforms are increasingly seen as indispensable tools for digital transformation and competitive differentiation.

Furthermore, the growing emphasis on asset management, risk mitigation, and process optimization is fueling the adoption of industrial knowledge graph platforms. These platforms facilitate holistic visibility into asset lifecycles, maintenance schedules, and operational risks by connecting siloed data repositories and enabling cross-domain analytics. Industries such as oil & gas, pharmaceuticals, and chemicals, which operate in highly regulated environments, benefit significantly from the ability to trace data lineage, ensure compliance, and proactively manage risks. The integration of knowledge graphs with existing enterprise systems, including ERP, MES, and SCADA, further enhances their value proposition by streamlining workflows and supporting real-time decision-making.

Regionally, North America leads the global market, driven by early technology adoption, strong presence of key vendors, and significant investments in industrial IoT and AI initiatives. Europe follows closely, supported by robust manufacturing and automotive sectors, as well as stringent regulatory standards that encourage data integration and transparency. The Asia Pacific region is witnessing the fastest growth, propelled by rapid industrialization, government-led digitalization programs, and the proliferation of smart manufacturing initiatives in countries such as China, Japan, and South Korea. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as local industries increasingly recognize the value of knowledge graph platforms for operational excellence and risk management.

Component Analysis

<br

Knowledge Graph Market size was valued at USD 7.19 Billion in 2024 and is expected to reach USD 4.1 Billion by 2032, growing at a CAGR of 18.1% from 2025 to 2032.

Knowledge Graph Market Drivers

Enhanced Data Integration and Analysis: Knowledge graphs excel at integrating and analyzing data from diverse sources, including structured, semi-structured, and unstructured data. This enables organizations to gain a holistic view of information and make more informed decisions. Improved Search and Information Retrieval: Knowledge graphs provide a more semantic understanding of information, enabling more accurate and relevant search results. Instead of just keyword matching, knowledge graphs understand the relationships between entities and provide more contextually relevant information. Personalized Experiences: Knowledge graphs can be used to personalize user experiences by understanding individual preferences, interests, and behaviors. This is crucial for applications like personalized recommendations, targeted advertising, and customer service. AI and Machine Learning: Knowledge graphs are essential for powering AI and machine learning applications, such as chatbots, recommendation systems, and fraud detection. They provide a structured representation of knowledge that AI/ML models can easily understand and utilize. Business Intelligence and Decision Making: Knowledge graphs can help businesses gain deeper insights into their customers, markets, and operations. They can be used to identify trends, predict future outcomes, and make more informed business decisions.

Analyzing sales data is essential for any business looking to make informed decisions and optimize its operations. In this project, we will utilize Microsoft Excel and Power Query to conduct a comprehensive analysis of Superstore sales data. Our primary objectives will be to establish meaningful connections between various data sheets, ensure data quality, and calculate critical metrics such as the Cost of Goods Sold (COGS) and discount values. Below are the key steps and elements of this analysis:

1- Data Import and Transformation:

• Gather and import relevant sales data from various sources into Excel.
• Utilize Power Query to clean, transform, and structure the data for analysis.
• Merge and link different data sheets to create a cohesive dataset, ensuring that all data fields are connected logically.

2- Data Quality Assessment:

• Perform data quality checks to identify and address issues like missing values, duplicates, outliers, and data inconsistencies.
• Standardize data formats and ensure that all data is in a consistent, usable state.

3- Calculating COGS:

• Determine the Cost of Goods Sold (COGS) for each product sold by considering factors like purchase price, shipping costs, and any additional expenses.
• Apply appropriate formulas and calculations to determine COGS accurately.

4- Discount Analysis:

• Analyze the discount values offered on products to understand their impact on sales and profitability.
• Calculate the average discount percentage, identify trends, and visualize the data using charts or graphs.

5- Sales Metrics:

• Calculate and analyze various sales metrics, such as total revenue, profit margins, and sales growth.
• Utilize Excel functions to compute these metrics and create visuals for better insights.

6- Visualization:

• Create visualizations, such as charts, graphs, and pivot tables, to present the data in an understandable and actionable format.
• Visual representations can help identify trends, outliers, and patterns in the data.

7- Report Generation:

• Compile the findings and insights into a well-structured report or dashboard, making it easy for stakeholders to understand and make informed decisions.

Throughout this analysis, the goal is to provide a clear and comprehensive understanding of the Superstore's sales performance. By using Excel and Power Query, we can efficiently manage and analyze the data, ensuring that the insights gained contribute to the store's growth and success.

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

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

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.

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

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 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

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

Graph Database for Security Market Outlook

According to our latest research, the global graph database for security market size reached USD 2.1 billion in 2024. This dynamic sector is expanding rapidly, supported by a robust compound annual growth rate (CAGR) of 22.7% from 2025 to 2033. By the end of the forecast period in 2033, the market is expected to attain a value of USD 16.3 billion. This impressive trajectory is primarily driven by escalating cyber threats, the proliferation of complex digital ecosystems, and the increasing demand for advanced analytics in security operations.

One of the most significant growth factors for the graph database for security market is the exponential rise in cyberattacks and sophisticated threat vectors targeting organizations worldwide. As digital transformation accelerates across industries, enterprises are generating vast volumes of interconnected data, creating new vulnerabilities and attack surfaces. Traditional relational databases struggle to effectively manage and analyze such complex, highly connected datasets. In contrast, graph databases excel at mapping relationships and patterns, making them invaluable for identifying suspicious activities, tracking threat actors, and correlating diverse security events in real-time. The ability to visualize and traverse connections at scale empowers security teams to detect advanced persistent threats, insider attacks, and fraud schemes that would otherwise go unnoticed.

Another pivotal driver is the increasing regulatory pressure and compliance requirements faced by organizations in sectors such as BFSI, healthcare, and government. Regulations including GDPR, HIPAA, and PCI DSS demand robust data protection, rigorous access controls, and comprehensive audit trails. Graph database technologies enable organizations to model complex access hierarchies, monitor user behaviors, and ensure compliance with evolving legal frameworks. By providing granular visibility into user roles, permissions, and interactions, these solutions facilitate proactive risk management and timely incident response. The integration of artificial intelligence and machine learning with graph databases further enhances predictive analytics and automation in security operations, reducing the burden on human analysts and improving overall resilience.

The rapid adoption of cloud computing, IoT devices, and remote work models is reshaping the security landscape and fueling demand for graph database solutions. As organizations migrate workloads to multi-cloud and hybrid environments, the complexity of managing identities, access rights, and network flows increases exponentially. Graph databases provide a unified view of assets, users, and their interdependencies, enabling security teams to identify misconfigurations, detect lateral movement, and enforce zero-trust principles. The scalability and flexibility of cloud-based graph database offerings are particularly attractive to enterprises seeking to modernize their security infrastructure without incurring significant capital expenditures. Strategic investments in research and development, partnerships with cybersecurity vendors, and the emergence of managed graph database services are further propelling market growth.

Regionally, North America dominates the graph database for security market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of major technology providers, high cybersecurity spending, and early adoption of advanced analytics solutions. Europe follows closely, driven by stringent data privacy regulations and a strong focus on digital sovereignty. The Asia Pacific region is witnessing the fastest growth, supported by rapid digitalization, government initiatives, and increased awareness of cybersecurity risks. Latin America and the Middle East & Africa are emerging as promising markets, although challenges such as limited infrastructure and skills gaps persist. Overall, regional dynamics are shaped by varying regulatory landscapes, industry maturity, and investment levels in digital security.

Component Analysis

The graph database for security market is segmented by component into software and services, each playing a critical role in the adoption and effectiveness of graph database solutions. The software segment comprises graph database management systems, visualization tools, analytics engines, and integration platforms. Thes

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.

Source data used to make the graphs in Figure 1 of Kusick et al., 2020