The Positive Impact of Data Mining:  
Other Areas
The Positive Impact of Data Mining:  
Other Areas
  

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.  In addition to these top five
areas, data miners described positive impact examples in a diverse set of areas.  
Below is the full text of the 44 positive impact examples they shared in these other
topic areas:  


  • Better prediction than classical statistics methods for rhino horn
    fingerprinting.   Can help busy people develop improved prediction
    models that can handle non-linearity and complex interactions.

  • We have applied data mining in government to better estimate the
    social and economical return of funding programs (loans, grants, and
    so on) using credit scoring and data mining in general.  By our
    estimations, these techniques translate into more jobs created, more
    funding programs, and in general a virtuous circle.

  • I work for a charity, which is using data mining to increase the level of
    donation to appeals, reduce attrition in regular giving and identify likely
    candidates for major giving, bequests etc.

  • Data mining offers tools that can improve our knowledge about risk
    factors in adolescent substance use.

  • In our case, predicting academic performance help us detect at risk
    students in order to help them, so they will not quit their studies.  We   
    will also detect the high performance students to get the best of them.

  • Data mining can be used to improve retention amongst at-risk
    populations of college students as well as improve the overall delivery
    of collegiate education.

  • Improve college student success and retention.

  • I think if properly aligned, Data mining will make a good impact in
    education.

  • Educational surveys.

  • 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.

  • In the near future, conclusions from data mining will yield results that  
    will be used on the witness stand.  While this will be helpful in both  
    evidence collection and criminal prosecution it is also a challenge to
    the industry.   Hopefully by the time that becomes common, the  
    industry has a set of standards to clearly evaluate whether the claims
    that are being made are credible.

  • 1) Recommendation systems for pretty much anything. You can't
    buy/use etc. something you don't know about.  2) Social networking.      
    3) Fraud, criminal and terrorist detection.  4) Improving on human  
    errors (auto correcting human spelling in searches, suggesting other
    queries etc.)  5) Anomaly detection.

  • Operational / quality efficiency improvement is an area with NO
    personal privacy concerns to society.  Crime prevention is another  
    huge positive impact without concerns.

  • Detection fraud.  Detection criminal activities.

  • Currently: mitigating terrorism, and ultimately: defeating terrorism

  • Recommendation  Fraud Detection and crime prevention

  • Crime and terror prevention.

  • Terrorism detection

  • Decreasing criminality

  • Better and proactive Risk management,  Economic forecast and
    improved agriculture output.

  • Better credit scoring, credit risk measurement

  • New brand predictions for market and volume shares - based on
    markovian analysis    brand price elasticities  segmentation of brands -
    based on cluster   Perceptual mapping - factor analysis  trending -
    forecasting

  • Data mining for petroleum discovery and production in deep waters.

  • Cost optimization

  • It solves the basic fundamental question of economics -how to better
    allocate resources.  This will ultimately benefit customers, companies
    and the society as a whole: Focus on facts to generate value and  
    reward productivity.

  • Data mining search terms tells us much about society in the free world.
    The information gained from it can be used to increase productivity for
    anyone and improve governments policy making.

  • Research of relationships in the omics fields, in the scientific
    experiments (I think colliders), in streaming data dependent fields.

  • Predictive analytic to forecast poverty ratio/scores for better capacity
    planning.

  • Combustion optimization.  No other information can be given at this
    time.

  • Helping bring down insurance costs through accurate rate setting.

  • In early 2009, the Office of the Comptroller of the Currency (OCC), a
    regulatory arm of the Treasury Department, launched a project to
    assemble a database of performance data on over 70% of the
    mortgage loans on the books in the US today.  This project, which
    involved monthly contributions of data from the nine largest mortgage
    servicers in the country, has given the OCC insight into how well efforts
    to mitigate the real estate collapse have performed.  In an environment
    where everyone has preconceived ideas of what caused the crash of
    2008, this database is an invaluable source for understanding what is
    really going on.  Quarterly reports are published on the web by the OCC.

  • Academic research

  • Identifying predictive information from unstructured data, such as e-
    mails, blogs, online news, internal notes.

  • I recently analyzed a database built using car accident reports. Inside
    the DB, we have, for instance: speed of cars before accident, state of
    intoxication(alcohol), time of day, road class, relative position of cars,
    weather, etc. I did a predictive model to predict if the accident was lethal
    or with a severe injury. The most important variables are, in decreasing
    importance: overtiredness, reduced visibility, road class (is it a
    highway?), state of intoxication(alcohol),... What's interesting is that:  1.
    "speed" is never used (but the "road class" variable, that also encodes
    the speed, is always used instead).  2. overtiredness is more important
    than the "state of intoxication(alcohol)".  Why are these 2 factors ("road
    class" and "overtiredness") never part of any advertisement campaigns?

  • The beneficials are vast: for instance: these days pattern-recognition
    algorithms approach a level of accuracy no human beings could ever
    achieve.

  • Results that can not be gained with "usual" BI methods (SQL, OLAP).

  • Suitable training material of data mining applicable to different
    disciplines.  Rigorous training.

  • Better predictability capacity, which may lead to better decision-making
    in all areas.

  • Data (mining) gives a better understanding of the world we're living in.

  • Data mining process bring the possibility to apply science instead of
    using the rule of thumb.

  • Give more accurate answers.

  • Scoring Point / Tesco

  • A smarter way to do anything you do.

  • Understanding better how things work.
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