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power bi dashboard microstrategy

Sales Dashboard

​This dashboard uses a combination of charts, graphs, and maps to present complex sales data in an easily digestible format. The color scheme and design elements are consistent, enhancing the visual appeal and aiding in data interpretation. Overall, it's a strategic tool for sales teams and managers to track sales KPIs, understand market performance, and make informed business decisions.

  • Sales vs Target: This section compares the actual sales revenue against predefined sales targets, giving immediate insight into the company's performance relative to its goals.

  • Cost vs Target: It measures the actual costs incurred against the budgeted or targeted costs, indicating how well the company is managing expenses relative to its sales efforts.

  • Sales Trend: This area showcases the sales revenue trend over a specified time period, which is useful for identifying patterns, growth, or decline in sales over the years.

  • Yearly Sales Comparison: A detailed comparison of yearly sales figures against targets for each year, allowing users to quickly assess which years met or exceeded targets and which did not.

  • Revenue Distribution by Category: A circular chart that breaks down total revenue by product or service category, providing a quick visual representation of which segments are the largest contributors to revenue.

  • Sales by State: A geographical map representing sales distribution across different states, which can be used to identify strong and weak market areas and to strategize regional sales efforts.

  • Interactive Filters: The filters for year, customer, and state allow users to refine the data displayed, tailoring the information to their specific analysis needs.

  • Performance Insights: The dashboard provides textual insights that summarize the data, highlighting the highest and lowest performing periods and offering a narrative context to the visual data.

  • Usability Features: There's a toggle for turning on or off the labels on the charts, improving the readability and customizability of the information presented.

Power bi services MicroStrategy

Occupancy Cost Analysis

The dashboard below provides a comprehensive view of occupancy costs across different divisions, countries, and sites for a business. Here's a breakdown of how it addresses various business concerns:

  1. Occupancy Cost by Division: This section allows a business to analyze the distribution of costs across different divisions like Factory, General, and Construction. It provides insights into which divisions are more cost-intensive, allowing for targeted cost management strategies.

  2. Occupancy Cost by Country: This feature helps to compare the costs of occupancy across different countries where the business operates. It highlights where the company might be incurring higher costs, potentially due to local economic factors or inefficiencies.

  3. Occupancy Cost Trend: It shows the fluctuation of costs over time on a monthly basis. This trend analysis is crucial for identifying patterns, predicting future costs, and budgeting.

  4. Top 5 Sites by Occupancy Cost: Here, the business can quickly identify which sites are incurring the most significant costs. This can help in prioritizing cost optimization efforts.

  5. Occupancy Cost by Site: This offers a detailed comparison of sites and changes in costs year over year. It's instrumental for site-level analysis and helps in deciding if a site is becoming more or less cost-efficient over time.

Overall, the report provides strategic insights for cost optimization, budget allocation, and identifying operational inefficiencies. It aids decision-makers in understanding where to focus their efforts to reduce costs and improve the company's bottom line. It also enhances transparency, as stakeholders can easily access and interpret this financial data.

The image shows a data flow diagram, which represents the process of Extract, Transform, Load (ETL) using Azure Data Factory (ADF) to move data from an SAP system to a Snowflake cloud data platform. Here’s a step-by-step breakdown of the process:

  1. Source (SAP): The data originates from an SAP system, which is typically used for enterprise resource planning (ERP) and contains business operation data.

  2. ETL Process (ETL by ADF): Azure Data Factory is used to perform the ETL process. "Extract" involves reading data from the SAP system, "Transform" refers to cleansing, aggregating, or otherwise preparing the data for analysis, and "Load" is the process of writing the data to the target data store.

  3. Target (Snowflake): Snowflake is a cloud-based data warehousing service, where the processed data is stored. It is designed for large-scale data storage and analytics.

  4. Distribution: From Snowflake, the data is then distributed to two different endpoints:

    • End Users: These are the business users who utilize the data for reporting, analysis, decision-making, etc. The chart icon suggests they might be using data visualization tools or business intelligence software.

    • Mainframe: This is somewhat unusual in modern architectures since mainframes are generally considered legacy systems. However, this indicates that the processed data is also sent to a mainframe system, possibly for operational processes or because some legacy applications still require it.

Power BI MicroStrategy

Asset Management Cost Analysis

A commercial real estate management costs dashboard provides a clear and detailed overview of all expenses associated with managing commercial properties. This dashboard details operating expenses: Detailed breakdown of operating costs such as utilities, cleaning, security, landscaping, and repairs. 

The dashboard below designed to analyze resolution efficiency for a company, possibly named "Assetly" which deals with facility management or maintenance services. Here's how it solves various business problems:

  1. Category and Problem Management: Filters like category name, problem name, and facility manager allow for a granular view of issues. This helps in identifying problem areas and assigning resources efficiently.

  2. Avg. Days to Resolve (Top 10) by Task Type: This bar chart indicates the average number of days taken to resolve different types of tasks. It identifies bottlenecks in the process and tasks that require more attention due to longer resolution times.

  3. Avg. Days to Resolve by Vendor: Similar to the task type chart, this provides insights into which vendors have longer resolution times. This can inform contract negotiations, vendor management, and performance reviews.

  4. Tickets Resolution Donut Chart: Displaying the proportion of on-time versus late tickets, this visual helps to quickly assess the efficiency of the resolution process and adherence to service level agreements (SLAs).

  5. Number of Tickets by Facility Manager: This bar and line chart combo shows the number of tickets each facility manager is handling and the percentage of late tickets. It's useful for workload distribution and performance evaluation.

  6. Size-%Late Tickets Scatter Plot: This plot relates the number of tickets to the average days to resolve and indicates the percentage of late tickets by size. It's a strategic tool for visualizing efficiency, workload, and punctuality by manager.

  7. Hours Worked by Work Order Priority: Shows the distribution of hours worked across different priority levels. This indicates how much effort is spent on routine, urgent, or emergency tasks, which is key for understanding resource allocation.

Overall, the dashboard provides actionable insights into operational efficiency, vendor performance, staff workload, and service quality. It empowers decision-makers to optimize workflows, improve time management, and enhance overall service delivery.

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