Business Intelligence: Data Mining Techniques

Study Notes

Data mining is the process of examining large data sets to identify patterns that can help in further analysis.
E-commerce platforms use data mining to establish correlations between customer demographics, such as gender, age, and preferences, to personalize recommendations and marketing messages.
Data mining is suitable for fields that rely on transactional data, such as retail, finance, healthcare, and hospitality.

Dashboarding and visualization tools enable users to access real-time insights and observe trends.
Ready-made templates with industry-standard metrics can generate customized reports easily.
These tools eliminate the need for a skilled data professional to draw insights from the data.
Visualization capabilities bring to light insights that numbers don't.
Reporting tools provide concise summaries and reports to monitor business performance, such as employee information, sales figures, and expense details.

Performance management involves tracking progress against pre-determined goals and objectives within defined timeframes.
BI techniques help companies monitor current progress, predict future trends, and make informed data-driven decisions.
BI can analyze past sales data to suggest realistic sales targets, helping plan the current budget and predict future scenarios.

Benchmarking compares an organization's performance metrics, practices, and processes with those of its competitors or industry peers.
Companies must align their metrics with industry benchmarks to stay competitive.
BI can track metrics such as financial metrics, operational efficiency, customer satisfaction, and product quality.

BI tools utilize high volumes of customer data to identify customers with high-conversion potential segments.
Customer data includes customer demographics, customer behavior, customer conversion rates, and other customer metrics.
Advanced analytics enable accurate sales forecasts, helping companies optimize their selling strategies and timing.
BI software tracks the effect of marketing campaigns, enabling the marketing team to get better insights and tweak their marketing strategies.

Advanced statistical analysis helps in predictive modeling based on multiple factors.
It uses complex computations to establish relationships between various parameters and reveal trends that help decision-making in business.
Companies can improve operational efficiency, build better customer targeting and service strategies, and identify new business opportunities by studying trends and patterns.

Data warehousing helps collect and store data from various sources in a centralized database.
Data warehouses are usually expansive and scalable, keeping the data ready for further analysis and extraction of insights.
A great application of data warehousing is in the airline industry, where it enables airline operators to predict future demand, make decisions, and standardize processes.