Published:
November 1, 2025
Category:
Ideas / Technology
Client:
Oceanthemes
Case Study on Power BI: The Process of Manual Data Mining
Power BI Case Study: The Process of Manual Data Mining
The client represents a mid-sized business that had multiple business functions like sales, finances, operations, and customer management. Consequently, a considerable volume of data has been produced by these functions through their use of different systems, such as ERP software, CRM software, and even spreadsheet tools within departments.
Despite the abundance of data that was available to the organization, its ability to make sense of it was limited. The reporting process was manual and employed spreadsheet analysis. This made it difficult for business users and executives to make sense of varying levels of data that was available.
In order to meet these challenges, the company collaborated with Soft Korner to transform its analytical solutions through Microsoft Power BI.
Challenges of Businesses
There were a few issues for the client prior to the installation of Power BI, affecting efficiency.
Fragmented Data Landscape
The data was spread over many systems, which included ERP and CRM, in addition to many Excel sheets that each department individually managed. There was no standardized data model or single version of the truth in the system. This resulted in many reports containing inconsistent data, making data difficult to trust.
Manual and Labor-Intensive Reporting
There was manual-intensive work involved in submitting activities. There was extensive exporting of raw data by analysts and then transformation and cleaning of data by analysts within spreadsheets. They would then use formulas and generate static reports.
Limited Visibility into Business Performance
These reports were mostly done on a periodic basis, for example, on a weekly or monthly basis. These offered a historical view, but the view was nothing less than real-time. In other words, nothing could be done in real-time, as the view could be likened to a “rear-view mirror,” where the current situation could not be viewed.
Static Reporting with No Interactivity
Currently available reports lacked interactive functionality. The reporting system did not allow drilling down on data details and time trend analysis of data. Such additional analysis would require new report requests.
Reactive Decision-Making
Because the lack of trend analysis and forecasting information was a factor, the decisions were, to a large extent, reactive. The organization was primarily trying to determine what had happened, as opposed to why.
Project Objectives
The main purpose of the initiative was to change the way the organization carried out the analysis and reporting of their data. The main objectives of the initiative were to:
- Crossing over from manual data mining to interactive & automatic analyses
- Creation of a single, trusted source for business data, including sales, accounting
- Providing self-service reporting capabilities to business users
- Enhancing the speed, precision, and reliability of decision-making processes
- Eliminating the efforts of reporting
- Moving focus from data work to insight work
Solution Overview
Soft Korner created a holistic business intelligence solution based on Power BI and planned it in a manner to tackle all challenges of the business of ‘the client’. This was accomplished with a structured implementation plan.
Data Consolidation and Integration
The first stage entailed the consolidation of all the relevant information into a common framework.
The data was integrated from the ERP systems, CRM systems, and other departmental spreadsheets into a common data model. In this series of activities related to data collection and integration, the data was cleansed and standardized. This helped to ensure that the data is accurate. The naming conventions were standardized.
The relationships among the tables were planned very carefully in order to effectively support accurate aggregation and efficient querying. The integrated architecture provided a strong foundation for reporting and analytics.
Data Modeling & Metric Standardization
A comprehensive semantic model was created in Power BI to mitigate technical complexity for end users. Instead of dealing directly with transactional data, end users could use clearly defined business measures and business dimensions.
The custom DAX measures developed enabled analysis of key performance indicators, period-over-period analysis, trending analysis, or operational analysis. With calculations performed directly in the data model, all reports could be done consistently, thereby avoiding the need to perform calculations manually using a spreadsheet tool.
This made possible the creation of structured and reusable knowledge from data through an effort that was trusted within the organization.
Dashboard Development & Visualization
The Power BI dashboard has been crafted to cater to various users. The target audience ranges from management to individuals who operate.
Executive Reporting
The high-level dashboards gave leaders a holistic picture of business activities. The dashboards called attention to key messages, trends, and exceptions, allowing leaders to make informed strategic decisions about the organization.
Reporting System
Detailed dashboards were designed for operation and department-level use. These reports enabled customers to analyze data at an atomized level and track day-to-day business activity without needing IT assistance.
Interactive Analytics
Every business Intelligence Dashboard supported interactivity like filtering, drill-down, drill-through, and time-related trend analysis. These capabilities gave the end-user the freedom to navigate the data on their own and perform additional analysis, thus answering inquiries beyond the original report.
Security and Governance
For secure management of the data, role-based access control was adopted through the Row Level Security feature in Power BI. The users’ access was limited to their respective departments.
Reports were published to a centralized Power BI environment that ensured version control and distribution of appropriate metrics. It ensured that there was no duplication of efforts and that compliance was enhanced.
Results and Business Impact
The deployment of Power BI has yielded outstanding results in many aspects within the organization.
Less Measurement and Reporting Effort
It was estimated that the organization was able to obtain a 60 percent reduction in time spent on manual data extraction and preparation of reports. There was a shift for analysts from manual reporting tasks to more analytical tasks.
Enhanced Data Visibility and Transparency
The leadership was able to get access to near-real-time dashboards. This made it possible to monitor key performance indicators and identify issues and opportunities.
Enhanced Decision-M
Interactive analytics enabled users to go beyond descriptive analysis and examine underlying drivers.
The results of decisions became more data-driven and confident. More Trust in Data By establishing “the single source of truth,” and by using common variables in reporting, they were able to eradicate inconsistencies from reports. There was greater trust in data, and this improved teamwork and alignment.
Scalable Analytics Foundation
The Power BI solution offered an elastic and expandable platform that was capable of accommodating future data sources, KPIs, and analytics.
Conclusion
This particular case study showcases the way in which the structured deployment of power BI can change the way decisions are made in the organization, shifting from a manual data mining practice to a highly analytical decision workplace.
By centralizing the data, creating common metrics, and providing self-service analytics capability, the client was able to turn the data into actionable business intelligence. This provided the benefits of faster insights, increased efficiency, and a solid foundation for future growth.
