The Positive Impact of Data Mining:
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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