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Integrated Business Operations & Sales Performance Dataset for Data Science Projects

Dataset Description

The Integrated Business Operations & Sales Performance Dataset is a synthetic yet highly realistic dataset designed for hands-on learning, analysis, and demonstration of business intelligence, data analytics, and DAX concepts. Comprising 200 structured records, this dataset simulates real-world business transactions across multiple regions, industries, customer types, and sales channels, making it ideal for Power BI dashboards, DAX practice, KPI creation, and data storytelling.

Download the Dataset : https://colorstech.net/wp-content/uploads/2026/02/DS46_200_Rows_Dataset.xlsx

Business Operations Dataset (Some Rows to review)

OrderIDOrderDateRegionCityCustomerTypeIndustryProductCategorySalesChannelQtyUnitPriceDisc %CostPriceDel DaysCSAT
DS460012025-01-01NorthDelhiNewITSoftwareOnline51200010800034
DS460022025-01-02WestMumbaiReturningRetailHardwareOffline32500051800055
DS460032025-01-03SouthBengaluruNewEducationSubscriptionOnline1015001590024
DS460042025-01-04EastKolkataReturningHealthcareServicesPartner24000083000073
DS460052025-01-05SouthChennaiNewManufacturingHardwareOffline432000122500064
DS460062025-01-06WestPuneReturningITSoftwareOnline6100000650015
DS460072025-01-07NorthDelhiNewRetailSubscriptionOnline12180020110034
DS460082025-01-08SouthHyderabadReturningHealthcareServicesPartner15000053800083
DS460092025-01-09EastKolkataNewEducationSoftwareOnline7900010600025
DS460102025-01-10WestMumbaiReturningManufacturingHardwareOffline52800072100064
DS460112025-01-11NorthDelhiReturningITServicesPartner33500062600054
DS460122025-01-12SouthBengaluruNewRetailSubscriptionOnline15160018100025
DS460132025-01-13WestPuneReturningEducationSoftwareOnline4110008750034
DS460142025-01-14EastKolkataNewHealthcareHardwareOffline230000102200073
DS460152025-01-15SouthChennaiReturningManufacturingServicesPartner62000051500064
DS460162025-01-16NorthDelhiNewITSoftwareOnline81250012820035
DS460172025-01-17WestMumbaiReturningRetailHardwareOffline52600061900044
DS460182025-01-18SouthBengaluruNewEducationSubscriptionOnline2014002090025
DS460192025-01-19EastKolkataReturningHealthcareServicesPartner24200053100083
DS460202025-01-20WestPuneNewManufacturingHardwareOffline334000102600064

Above are some rows of the dataset.

Dataset Structure (Columns)

🔹 Categorical Fields

  1. OrderID – Unique order identifier
  2. OrderDate – Date of order
  3. Region – North, South, East, West
  4. City – Delhi, Mumbai, Bengaluru, Chennai, Hyderabad, Pune
  5. CustomerType – New, Returning
  6. Industry – Retail, Manufacturing, Education, Healthcare, IT
  7. ProductCategory – Software, Hardware, Services, Subscription
  8. SalesChannel – Online, Offline, Partner

🔹 Numerical Fields

  1. Quantity – Units sold
  2. UnitPrice – Price per unit
  3. DiscountPct – Discount %
  4. CostPrice – Cost per unit
  5. DeliveryDays – Days to deliver
  6. CustomerSatisfaction – Rating (1–5)

Unlike commonly used public datasets such as Superstore, It has been intentionally designed with custom business logic to avoid overused patterns while still reflecting practical decision-making scenarios faced by analysts, managers, and consultants. The dataset balances categorical dimensions and numerical measures, enabling users to perform descriptive, diagnostic, and performance-based analysis without requiring external data enrichment.

At its core, It captures order-level transactional data. Each record represents a single customer order with a unique OrderID and a corresponding OrderDate spanning a continuous timeline. This structure allows analysts to conduct time-series analysis, monthly or daily trend analysis, and performance comparisons over time using DAX functions such as TOTALYTD, DATEADD, or SAMEPERIODLASTYEAR.

The dataset includes multiple geographical dimensions, namely Region and City, enabling geographic segmentation and regional performance comparisons. Regions such as North, South, East, and West are paired with major business cities, making the dataset suitable for region-wise revenue, profit, and operational efficiency analysis. This structure supports visuals such as maps, stacked bar charts, and region-wise KPI cards.

From a customer perspective, It distinguishes between New and Returning customers, allowing analysts to explore customer behavior, retention performance, and revenue contribution by customer type. This is particularly useful for learning cohort-style analysis, customer segmentation, and comparative KPIs such as revenue per customer category.

Industry and ProductCategory columns introduce a business vertical and product mix dimension. Industries like IT, Retail, Education, Healthcare, and Manufacturing are paired with product offerings such as Software, Hardware, Services, and Subscriptions. This combination enables profitability analysis, product performance comparisons, and identification of high-margin industries or product lines.

The SalesChannel field (Online, Offline, Partner) adds another strategic layer, making It well-suited for channel performance analysis. Analysts can evaluate how different channels contribute to revenue, profit, delivery efficiency, and customer satisfaction, which mirrors real-world omnichannel business scenarios.

Numerical measures form the analytical backbone of this dataset. Fields such as Quantity, UnitPrice, DiscountPct, and CostPrice allow users to calculate derived metrics including Revenue, Cost, Profit, and Profit Margin using DAX. The presence of discounts introduces realistic pricing complexity, enabling analysts to assess the impact of discounting strategies on profitability.

Operational efficiency is represented through DeliveryDays, which supports logistics and service-level analysis. Users can build KPIs such as average delivery time, delayed orders, or service performance by region or channel. CustomerSatisfaction scores further enhance the dataset by enabling experience-focused analysis, correlation studies between delivery speed and satisfaction, and service quality dashboards.

Overall, This Dataset is purpose-built for learning, teaching, and demonstrating analytics concepts. It is suitable for Power BI beginners learning basic measures as well as advanced users practicing complex DAX calculations, KPI modeling, and dashboard design. Whether used for classroom training, interview preparation, portfolio projects, YouTube tutorials, or corporate workshops, It provides a flexible and realistic foundation for business analytics exploration.

KPIs and DAX Formulas for a Business Operations Analytics Dataset

Effective Power BI dashboards are built on well-defined KPIs and carefully designed DAX measures. This article presents a practical set of four KPI measures and six analytical DAX measures that can be applied to a business operations dataset containing sales, cost, delivery, and customer experience information. Each formula is designed for real-world reporting scenarios and can be directly implemented in Power BI.

Assumption:
The fact table in the Power BI model is named Data.

KPI Measures (Single-Value Cards)

Total Revenue

Total Revenue represents the net sales value after discounts are applied. This measure reflects the actual income generated by the business and is suitable for executive dashboards.

Total Revenue =
SUMX(
    Data,
    Data[Quantity] *
    Data[UnitPrice] *
    (1 - Data[DiscountPct] / 100)
)

Total Cost

Total Cost captures the full operational cost associated with all completed orders.

Total Cost =
SUMX(
    Data,
    Data[Quantity] * Data[CostPrice]
)

Total Profit

Profit is calculated by subtracting total cost from total revenue, providing a clear view of business sustainability.

Total Profit =
[Total Revenue] - [Total Cost]

Average Customer Satisfaction

This KPI measures overall customer experience by averaging satisfaction scores across all transactions.

Average Customer Satisfaction =
AVERAGE(Data[CustomerSatisfaction])

DAX Measures for Visual Analysis

These measures are intended for charts, tables, and comparative visuals that explain performance trends and patterns.

Profit Margin Percentage

Profit Margin indicates how much profit is earned for every unit of revenue.

Profit Margin % =
DIVIDE(
    [Total Profit],
    [Total Revenue],
    0
)

Revenue from Returning Customers

This measure isolates revenue generated by repeat customers.

Revenue – Returning Customers =
CALCULATE(
    [Total Revenue],
    Data[CustomerType] = "Returning"
)

Revenue from New Customers

This measure highlights revenue contributed by first-time buyers.

Revenue – New Customers =
CALCULATE(
    [Total Revenue],
    Data[CustomerType] = "New"
)

Late Orders Count

Late Orders counts the number of transactions where delivery time exceeded five days.

Late Orders =
CALCULATE(
    COUNTROWS(Data),
    Data[DeliveryDays] > 5
)

Average Discount Percentage

This measure tracks discounting behavior across orders.

Average Discount % =
AVERAGE(Data[DiscountPct])

Revenue by Sales Channel

Used with the SalesChannel dimension to compare channel performance.

Revenue by Sales Channel =
[Total Revenue]

Conclusion

By combining these KPI measures with analytical DAX formulas, analysts can build dashboards that move beyond surface-level reporting. Revenue and profit provide financial context, delivery metrics reveal operational efficiency, and customer satisfaction highlights experience quality. Together, these measures enable clear, actionable insights suitable for real-world business decision-making.