INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025

www.ijltemas.in Page 33

A Real Time Dashboard for Asset Monitoring in Power BI: An

Overview

Shreya Issar,

Priya Issar,

Jai Bhaskar,

Anu Sharma

1,3

Department of Computer Science & Engineering, University College of Engineering and Technology, Bikaner Technical

university, Bikaner, Rajasthan, India

Department of Computer Science & Engineering, WILP, BITS, Pilani, Rajasthan, India

Department of HEAS (Chemistry), University College of Engineering and Technology, Bikaner Technical university,

Bikaner, Rajasthan, India

DOI: https://doi.org/10.51583/IJLTEMAS.2025.140500006

Received: 12 May 2025; Accepted: 14 May 2025; Published: 29 May 2025

Abstract- This paper explores the development and analysis of two methodologically aligned dashboards: the HR Analytics

Dashboard and the Coffee Sales Dashboard. Both dashboards were developed to demonstrate the transformative power of data

analytics. This improvs decision-making across diverse domains. The HR Analytics Dashboard focuses on workforce metrics for

example attrition rates, recruitment efficiency, and employee performance etc. The Coffee Sales Dashboard focuses on sales

performance, customer trends, and inventory management. This paper includes data collection methods, preprocessing

techniques, analytical models, visualization strategies, insights derived, and the potential for cross-domain applications. This

integrated approach highlights the versatility of data analytics tools, particularly Power BI, in managing and interpreting complex

datasets.

Keywords: HR Analytics, Coffee Sales Dashboard, Data Visualization, Power BI, Business Insights, Data Analytics

I. Introduction

The dashboard used for displaying the different types of visual data at one place. These are useful for monitoring, measuring, and

analyzing relevant data in various areas such as human resource data, web analytics, marketing performances, sales information,

logistic information etc. They provide a platform to us and make better, more informed, data-driven decisions because they are

dynamic, interactive, and show near real-time data. A dashboard meets most of the user’s informational needs. In today’s scenario

organizations, industries, companies rely on data-driven strategies to optimize performance, enhance efficiency, and future

growth. The ability to harness data from different domains and transform it into actionable insights is critical for informed

decision-making.

There are various tools which are used to visualize and analyze within dashboard such as Google Analytics, Looker, Sisense,

Domo, Infogram, Qlik, Tableau, Spotfire, ThoughtSpot, and Microsoft Power BI etc. This paper is focus on Microsoft Power BI.

It is a smart business intelligence tool which convert simple data into refined representations. It is a user-friendly interface,

integrating data from various sources, vast range of interactive visualizations and has ability to connect to diverse data sources. It

also provides an easy to use drag and drop functionality.

Our studies and findings are based upon trends in employee attrition based on demographic factors, impact of training programs

on key performance improvement, recruitment efficiency, time-to-hire metrics, best-selling products and seasonal sales trends,

regional performance variations and customer preferences and inventory turnover rates and stock optimization insights. In this

digital era Power BI recognize as a vital tool for improving operational efficiency, finding market trends, and running startups or

business growth. This research work focuses on two independent dashboards that is HR analytics the coffee sales analytics.

Although these dashboards serve different purposes, the underlying analytical principles, methodologies but the visualization

techniques are comparable. In this paper we are discussing the process of creating, managing, and analyzing data demonstrate the

broader applications of analytics and developing an interactive Analytics & Insight Dashboard that empowers users to explore

key metrics in Human Resources (HR) and Sales domains. With the help of AI-driven predictions, automation, and real-time

analytics there is transformation in business, future academic or professional work.

II. Literature Review

This literature review explores the existing literature on real time dashboards and empirical studies to understand the current

research in this field.

D. Dowding et.al. were introduced a dashboard for easy work in business sector and clinical dashboard which is beneficial in the

medical field. P. Singh and their coworkers investigated the dashboard for identification of challenges faced by education

providers. R. Matheus et. al. designed a dashboard for smart cities development. Based on a cross-sectional survey, Reinking et

al. proposed a theoretical model of dashboards to improve the individual and organizational performance gains. M. Raje et.al.,

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025

www.ijltemas.in Page 34

used Power Bi for data analysis. They studied Power Bi desktop has the feature to use various sources of data and create reports

for business intelligence by using source data. T. Susnjak et.al. and Psarommatis F. and their team also worked in the same field.

M. D. Jadhav et. al. created a sales dashboard by using power BI for displays monthly sales performance. C. T. Goncalves and

coworkers studied the impact of business intelligence tools in sales marketing. Many other research papers were based

investigation of Power BI Marketing Dashboard with advertising data and predicted the expected sales through investment inputs

of different marketing channels using multiple linear regression in Python and visualized that in the Power BI Interactive

Dashboard.

III. Methodology

The data are collected from relevant resources which are related to their sales and KPIs. The next step to understand that the

available data are accurate and reliable and could be transformed into valuable information. Thus, data cleaning is essential. This

procedure is crucial. During this process duplicate or unnecessary data are eliminated. The Power Query Editor which is a data

transformation and cleaning tool within Power BI is used for this. After the data cleaning procedure these data are used for Power

BI analysis. If any incomplete or missing data is found in the second phase few statistical techniques such as mean imputation or

regression imputation are used. If there are many missing data then they are omitted and the conflicting data are sent in to third

stage. The irregularities are spotted by comparing data from various sources or by analyzing data from a single source. The

standardization, categorization, and normalization are used to handle data inconsistencies. At the last stage verification of data

integrity and accuracy is carried out. In case of any inaccuracies the anticipated range validation techniques including cross-

validation and outlier detection are used to resolve it. Hence the process of data analysis is start with data cleaning from which we

get accurate and consistence obtained data are used for analysis. After that we transformed the cleaned data for analysis using

Power BI's.

IV. Studies and Findings

Our studies and findings are based upon trends in employee attrition based on demographic factors, impact of training programs

on key performance improvement, recruitment efficiency, time-to-hire metrics, best-selling products and seasonal sales trends,

regional performance variations and customer preferences and inventory turnover rates and stock optimization insights. The

foundation of this paper is built upon previously gathered data, methodologies, and analytical techniques from distinct domains,

treating each completed segment as a building block. This approach has been used to consolidate insights from both the HR

Analytics Dashboard and the Coffee Sales Dashboard.

HR Analytics Dashboard developments include

A. Data Collection: For the Data is collected from various online websites & YT sources. The data covers /include these

domains-

Human Resources Information System (HRIS): For employee demographics (age, gender, education, department, job role),

tenure, salary, performance ratings, training records, etc.

Applicant Tracking System (ATS): For recruitment data like applications, hires, time-to-hire, recruitment sources.

Learning Management System (LMS): For training completion data.

Exit Interview Data: For insights into reasons for employee attrition.

Employee Engagement Surveys: For satisfaction scores. After data collection, data is stored in Excel.

B. Data Collection and preprocessing:

Handling Missing Values:

Removing Duplicates

Data Type Conversion

Outlier Handling in Excel & Power BI

New column Creation using formulas in Power BI

C. Data Modeling (in Power BI):

Connecting to Data Sources: Establishing connections between Power BI and the various HR data sources.

Creating Relationships: Defining relationships between different tables based on common fields (e.g., linking an "Employee"

table to a "Department" table using a "Department ID"). This allows for data from different sources to be combined and analyzed

together.

D. Dashboard Design and Visualization:

Selecting Appropriate Visuals: Choosing charts and graphs that effectively communicate the insights for each KPI and

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025

www.ijltemas.in Page 35

dimension. Examples from your HR dashboard:

KPI Cards: For displaying key metrics like "Overall Employees," "Attrition," "Attrition Rate," "Active Employees," and

"Average Age."

Pie Chart: For showing the proportion of attrition by department.

Bar Chart: For comparing the number of employees by age group and attrition by education field.

Table: For detailed job satisfaction ratings.

Donut Charts: For visualizing attrition rate by gender across different age groups.

E. Layout and Navigation: Arranging the visuals in a logical flow, often starting with high-level summaries and allowing for

drill-down into more detail. Filters (like the "Month" filter in the Sales dashboard) and slicers (not explicitly visible in your HR

dashboard image but common) enable interactive exploration.

F. Insights Generation and Analysis:

Identifying Trends and Patterns: Analyzing the visualized data to identify key trends (e.g., increasing attrition in a specific

department, lower job satisfaction in certain roles).

Comparing Data Points: Examining relationships between different variables (e.g., attrition rates across different age groups

and genders, the impact of education level on attrition).

G. Deployment and Sharing (via Power BI Service):

Publishing the generated by Power BI.

The methodology for the Coffee Shop Sales Dashboard follows a similar structure, but with different data sources and KPIs:

Data Collection:

Data is collected from online websites.

The data contains information about -Data on stock levels, inventory turnover, and potential stockouts. Information on

campaigns, promotions, and their impact on sales. Data on customer behavior and preferences of loyalty members.

B. Data Cleaning and Preprocessing:

The steps followed are similar to HR dashboard process.

Data Modelling (in Power BI):

Creating relationships between tables (e.g., linking "Transactions" to "Products," "Locations," and potentially "Customers").

Creating calculated columns (e.g., calculating "Total Revenue" per transaction, extracting the day of the week or hour from the

timestamp).

Creating measures (using DAX) for key sales metrics visible on the dashboard:

Total Sales: Sum of revenue.

Total Orders: Count of unique transactions.

Total Quantity Sold: Sum of the number of items sold.

Sales Trend over the period: Sales aggregated over time (days, weeks, months).

Sales by Weekday/Weekend: Comparing sales performance on different days of the week.

Sales by Product Category: Analysing sales contribution of different product types.

Sales by Store Location: Comparing performance across different branches.

Sales by Day | Hours: Examining sales patterns throughout the day.

Dashboard Design and Visualization:

KPI Cards: Displaying "Total Sales," "Total Orders," and "Total Quantity Sold" as key performance indicators. The variance

indicators (e.g., "-31.8% | +37.0K vs LM") show comparisons to a previous period (likely last month - LM).

Line Chart: Visualizing the "Sales Trend over the period" to show performance over time.

Donut Chart: Showing the breakdown of "Sales by Weekday/Weekend."

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025

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Bar Charts: Comparing "Sales by Product Category" and "Sales by Store Location."

Heatmap/Table: Displaying "Sales by Day | Hours" to identify peak sales times.

Filters: The "FILTER PANEL" with "MONTH" allows users to focus on specific time periods. The calendar visual also acts as a

filter.

Insights Generation and Analysis:

Identifying best-selling products and categories.

Understanding sales trends over time and identifying seasonality.

Comparing the performance of different store locations.

Identifying peak sales hours and days.

Analyzing the impact of promotions (if that data is integrated).

Deployment and Sharing (Power BI Desktop):

Similar to the HR dashboard, publishing to Power BI Service

Fig. 1: Steps of Creating Dashboards

V. Result & Discussion

This tool is used to make several dashboards with the help of market survey and various KPIs of organization and business sector.

We used Microsoft Power BI to visualize and analyze the data. The process involved to create both dashboard is data collection,

cleaning, visualization, and interpretation. Both dashboards demonstrated the ability to identify key trends, forecast future

outcomes, and support data-driven decision-making. The flexibility of Power BI in handling different datasets highlight its value

as a comprehensive analytics tool.

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025

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Fig. 2: HR Dashboard and Coffee Dashboard

Future Scope

The scope of this integrated analytics is wide and have significant opportunities for growth and development. In this digital era

data-driven decision-making continues to evolving, thus both the HR Analytics and Coffee Sales Dashboards can be enhanced

with predictive analytics and machine learning capabilities to forecast trends and automate insights. This field has potential for

real-time analytics integration, enabling dynamic updates and immediate decision-making support. The dashboards are also

covering other business areas such as finance, marketing, and supply chain management and capable to provide a more

comprehensive view of organizational and business performance. Integration of cross-domain data will uncover hidden

correlations between HR metrics and sales performance and leading to holistic organizational and business strategies. Cloud-

based deployment and accessibility mobile will further increase the dashboards usability. Hence these are the valuable tools for

digital India seeking agile and informed decision-making processes.

Furthermore, as outlined in the future scope, the potential for integrating advanced analytics, real-time data, and cross-domain

insights promises to further enhance the value of such dashboards, transforming how organizations leverage data to drive strategic

initiatives and achieve a competitive advantage in the digital era. The future development of such dashboards, incorporating

advanced analytical techniques and addressing the challenges of data integration and governance, holds significant promise for

enhancing organizational agility and data-driven culture.

Potential Challenges

In new technology implementation there are some pros and cons. If the goals, KPI’s, purpose of organization or business are not

clear means objectives are not defined then dashboards do not produce right outputs. The quality of input data is not accurate then

misleading results are obtained and it may contain inaccuracies, incomplete information, duplicate errors etc. When data security,

system access are not secured this leads to the improper data handling. The poor knowledge of data analyst team is the main

barrier to the success of Power BI in long term. The main challenge is choosing right visualization and interactivity between

visuals. Performance of this tools is affected by large and complex datasets. However, these challenges are addressed properly

organizations, business sectors can leverage the full potential of Power BI dashboards to gain valuable outputs.

IV. CONCLUSION

This paper explored the development and application of two distinct yet methodologically similar dashboards that is one for HR

analytics and the other for coffee sales. The process of data collection, cleaning, modeling, visualization, and analysis, facilitated

by the capabilities of Microsoft Power BI, highlights the broad applicability of data analytics principles across diverse business

functions. The insights derived from both dashboards underscore the importance of visual communication in making data

accessible and understandable for effective decision-making. A dashboard is used for presenting a real-time status of business or

organization. This is the best platform for monitoring their performance. An effective dashboard facilitates the decision-making

process and delivers the information to end users rapidly. Now days the sales department facing challenges so it requires a visual

communication platform. Microsoft power BI is a best tool to visualize and display sales data through dashboard.

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MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025

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