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What is data mining? How analytics uncovers insights

Bob Violino | Aug. 28, 2017
Data mining is the automated process of sorting through huge data sets to identify trends and patterns and establish relationships

Dozens of vendors provide data mining software tools, some offering proprietary software and others delivering products via open source efforts.

Among the key vendors that offer proprietary data-mining software applications are Angoss, Clarabridge, IBM, Microsoft, Open Text, Oracle, RapidMiner, SAS Institute, and SAP.

Organizations that provide open source data mining software and applications include Carrot2, Knime, Massive Online Analysis, ML-Flex, Orange, UIMA, and Weka.


The risks and challenges of data mining

Data mining comes with its share of risks and challenges. As with any technology that involves the use of potentially sensitive or personally identifiable information, security and privacy are among the biggest concerns.

At a fundamental level, the data being mined needs to be complete, accurate, and reliable; after all, you’re using it to make significant business decisions and often to interact with the public, regulators, investors, and business partners. Modern forms of data also require new kinds of technologies, such as for bringing together data sets from a variety of distributed computing environments (aka big data integration) and for more complex data, such as images and video, temporal data, and spatial data.

Getting the right data and then pulling it together so it can be mined isn’t the end of the challenge for IT. The cloud, storage, and network systems need to enable high performance of the data mining tools. And the resulting information from the data mining needs to be presented clearly to the wide range of users expected to act on and interpret it. You’ll need people with skills in data science and related areas.

From a privacy standpoint, the idea of mining information that relates to how people behave, what they buy, what websites they visit, and so on can set off concerns about companies gathering too much information. That affects not just your technological implementation but your business strategy and risk profile.

Beyond the ethics of tracking individuals so thoroughly, there are also legal requirements about how data can be gathered, identified to a person, and shared. The United States’ Health Insurance Portability and Accountability Act (HIPAA) and the European Union’s General Data Protection Directive (GDPR) are among the best known.

In data mining, the initial act of preparation itself, such as aggregating and then rationalizing data, can disclose information or patterns the might compromise the confidentiality of the data. Thus, it’s possible to inadvertently run afoul of ethical concerns or legal requirements.

Data mining also requires data protection every step of the way, to make sure data is not stolen, altered, or accessed secretly. Security tools include encryption, access controls and network security mechanisms.


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