In the 5th Annual Survey (2011) data miners shared examples of situations where data mining is having a positive impact on society. A summary of the top five positive impact example topic areas is available. Below is the full text of the positive impact examples they shared in the topic area of Business Improvements:

• We have been using data mining techniques to improve electricity network forecasts which in turn help manage network capital expenditure costs which in turn reduces electricity bills. We have also been developing new tariff scenarios and identifying potentially energy vulnerable groups within the community.
• Using DM to develop a model to optimize the scheduling of oil shipping tankers (reduced cost to the company and positive environmental impact). Due to client confidentiality agreements I couldn't share the details of the project.
• Determining patterns associated with successful call center agents and applying those learnings in recruitment and training of agents led to increased employee, organization and customer satisfaction. That was fulfilling.
• We have used data mining for employee attrition. This will generate long term ROI of millions of dollars.
• Predictive Maintenance: the ability to maintain a machine or prevent accidents from happening. Predictive Maintenance could have prevent the tragedy in the Gulf of Mexico.
• I have seen within our own company the general interest and trust improve when looking to sound, critical analytic methods and projects. There's still a lot of 'gut' management going on even when there's data that obviates the need to use your gut but it's getter better.
• Predictive Churn In Telco Customer Segmentation
• Failure analyses, prediction, and prevention in manufacturing machines, planes, power plants, etc.
• When true data-driven decisions are made it creates a transparent management style and a cohesive team with local buy-in.
• Operational / quality efficiency improvement is an area with NO personal privacy concerns to society.
• Data mining can provide the "brain" of the "business organism" to provide companies with intelligent proactive business operations.
• Optimizing complex industrial processes that can be modeled by experimental testing. This helps companies to use energy more efficiently while making more money.
• It's very "green" ... targeting results in lower mail volumes. It's very profitable ... it increases the bottom line. It's good for customers ... they are more satisfied when properly implemented as part of a larger customer relationship strategy.
• Good tool to reduce waste in a climate of cost reduction and stretched budgets.
• Starting to build our own scorecards based on predictive modeling, and it is working very well.
• The way to model knowledge data driven applied to the E&P industry. It's useful to upstream / downstream process monitoring and optimizing. The potential to be used on different areas like the Petroleum industry is very high.
• Improved production efficiency -> reduced waste and pollution. Improved understanding of bio-genetics and related immunology. Improved understanding of sociopolitical and socio-economic phenomena and trends. Improved security.
• Over the course of the year, I have [become] more intrigued about the data visualization. With so much data now available, the key will be on how we present and share this data to provide context and a greater understanding of realms that have not been explored. This can be used to provide better products for consumers, improved experiences and advancement of health care.