The Positive Impact of Data Mining:  
Business Improvements
The Positive Impact of Data Mining:  
Business Improvements
  

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.
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