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This case study explores the feasibility of implementing a data mining warehouse system within a business organization.

It evaluates the benefits, risks, tools, pricing models, and real-world use cases to support a strategic recommendation for adoption.

 

Data Mining Feasibility Report

 

Proposed by: Jonathan D. Lumé

Submitted to: Department of Business, DeVry University

BIAM300: Managerial Applications of Business Analytics

Professor Randy Stauber

June 4, 2023

 

 

Executive Summary

As with any new business endeavor, making informed, data-driven decisions is critical. This report explores the feasibility of implementing a data mining warehouse system, focusing on profitability, market performance, and strategic innovation.

By applying a feasibility assessment model, this recommendation aims to guide firms in determining the value of investing in data mining infrastructure to support long-term profitability and growth.

Pros & Cons of Data Mining

Data mining offers transformative potential but comes with inherent risks. Key advantages include:

  • Enhanced business intelligence through machine learning and AI.

  • Readily available large-scale data from the internet and IoT devices.

  • Improved processing power and storage at reduced costs.

However, challenges remain:

  • Ethical concerns about customer data privacy.

  • Potential misuse of information, as highlighted by Epstein (2000), who warned of the risks to corporate social responsibility.

Available Tools

Several tools are accessible for data mining exploration:

  • PolyAnalyst: Advanced data preparation and analysis.

  • KnowledgeSEEKER & KnowledgeSTUDIO: Insight discovery.

  • WizWhy: Complex pattern extraction.

  • Bayesware Discoverer and SQL Server 2000: Robust analysis platforms.

Myatt et al. (2009) note that these tools support various stages of the data mining lifecycle, from preprocessing to reporting.

Tool Pricing

Tool selection must align with budget and business scale:

  • PolyAnalyst: ~$18,000/year (per feature).

  • JMP: ~$1,200/year.

  • Rguroo: ~$11.99/year — a cost-effective option.

Prices vary based on functionality, scalability, and support.

Implementation Timeframes

Implementation can be rapid if basic infrastructure and data sets exist. Key points:

  • Software is often user-ready.

  • Training remains the largest barrier.

  • Machine learning reduces processing time (e.g., correlation analysis from hours to minutes).

  • Long-term mastery may take years, but value begins early.

Third-Party Vendor Options

Due to the high entry cost and skills gap, third-party vendors provide:

  • Cloud-based data mining services.

  • Expert implementation and ongoing support.

  • Shorter time to market and risk mitigation.

Khan et al. (2023) project services to outperform hardware/software in revenue, confirming vendor viability for interim solutions.

Market Use Cases

Examples of successful data mining implementation:

  • Twitter: Uses sentiment analysis to manage its brand in real time (Dean, 2014).

  • Starbucks: Analyzes buyer propensities to personalize rewards and localize marketing (AZAT, 2022).

These companies show that actionable insights lead to strategic market positioning.

Final Recommendation

Data mining offers immense value to firms looking to harness customer data for growth. It’s recommended to:

  • Invest in training or hire experienced professionals.

  • Consider hybrid implementation using third-party vendors.

  • Adopt scalable tools suitable for your budget and data size.

Firms with data already on hand can begin benefiting from mining initiatives quickly, gaining a competitive edge in data-driven decision-making.

References

  • AZAT. (2022). Top 11 companies who use Data Mining. TechnoSuggest.

  • Capterra. (2023). PolyAnalyst, Compare Alternatives.

  • Dean, J. (2014). Big Data, Data Mining, and Machine Learning. John Wiley & Sons.

  • Epstein, M. (2000). Morning Edition Interview Transcript. NPR.

  • Khan, S., Borasi, P., & Kumar, V. (2023). Big Data and Business Analytics Market Forecast. Allied Market Research.

  • Myatt, G. J., Johnson, W. P., & Myatt. (2009). Making Sense of Data II. Wiley.

 

Through this feasibility report, I gained a comprehensive understanding of the critical factors involved in implementing data mining solutions, including the evaluation of available tools, cost considerations, and the ethical challenges of managing sensitive customer data.

I also learned how the data mining lifecycle—from preprocessing to deployment—requires careful planning and skilled professionals. Additionally, I explored how third-party vendors can bridge skill gaps and accelerate implementation.

Overall, this deep dive reinforced the importance of balancing technical capabilities with strategic business needs to drive long-term value.

 

In a professional setting, I would leverage this knowledge to guide organizations in selecting the right data mining tools tailored to their budget and objectives, ensuring ethical data usage and compliance with privacy standards.

I would also advocate for investing in employee training or collaborating with expert vendors to overcome skill barriers and shorten time to market.

By applying a structured feasibility assessment, I can help businesses make informed, data-driven decisions that improve profitability, customer insights, and competitive advantage in today’s data-centric economy.

Download the Data Mining Feasibility Report