The Important Role of Data Mining in the World of Business
By digitizing their documents, document conversion companies help businesses take the first step towards data mining and analytics for better decision making.
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The goal of data mining is to build learning models to automatically extract knowledge from huge amounts of complex data. This model can be used to perform predictions on unseen data. To analyze these data and build models, business should have all critical data in its digital format. Many businesses rely on an experienced document conversion company to digitize with ease. Data mining is about looking for hidden, valid and potentially useful patterns in huge sets of data. The process reveals unsuspected or unknown relationships within the data. It can help businesses discover information about customer behaviour, allowing them to make more accurate business decisions and earn more profits. It is a multidisciplinary skill that uses machine learning, artificial intelligence and database technology. It shows hidden correlations that others do not see. However, even a minor error can lead to incorrect results. Here are some common data mining mistakes to watch out for:
Ignoring quality checks
Most data miners develop predictive models but they do not review data for quality. Failing to detect or correct the quality of data can lead inaccurate results.
If two variables in a data set, A and B, show a similar trend, it Thinking that a pattern in the data shows cause and effect relationship
Working on results that don’t make sense
could be because one influences the other. It could also happen another variable, which hasn’t been considered, is influencing A and B. It is not easy to understand the underlying reason for these trends.
Data mining methods are informal and not supported by any scientific method or theory. Therefore, the results have to make business sense. But if there is no practical explanation for the results, it would be pointless to waste time on it.
Do not think that the relationships you observe in a data set will Stretching conclusions too far •
occur in all circumstances.
For instance, if your data was
collected in cool environment, the results may not match with those collected when the temperature rises.
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Following only one modelling method
Using data mining to address all data analysis
Putting a model into practice without enough testing
There is no single data mining model that fits all situations. So it would be a mistake to use the same approach for every situation.
While data mining has tremendous value, some applications require rigorous data collection methods, formal statistical analysis and scientific methods. Also, do not ignore traditional methods of data mining.
Predictive models use current information to predict future possibilities. However, make sure that you test a predictive model before you use it.
Data mining and data analytics are helping organizations improve their operations increase revenue, decrease costs, and identify customers. Let’s take a look at the various techniques and tools used for data mining. Data Mining Techniques and Tools Techniques ➢
Classification: This analysis is used to retrieve important information about data and meta data and also classify data into different class.
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Regression: This method identifies and analysis the relationship between two variables and also identifies the likelihood of a specific variable, given the presence of other variables.
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Association rules: This technique helps identify the association between two or more items.
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Outer detection: This method involves the observations of data items in the dataset that do not match an expected pattern or behaviour. It is used in various domains such as intrusion, detection, fraud or fault detection, etc.
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Clustering: This analysis is used to retrieve data that are similar to each other. This helps understand the differences and similarities between data.
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Sequential patterns: This approach can identify similar patterns or trends in certain transaction periods.
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Prediction: Prediction combines other data mining techniques like trends, sequential patterns, clustering, and classification to analyze past events or instances and predict a future event.
Data Mining Tools R language: It is an open source tool for statistical computing and graphics. It has statistical, classical statistical tests, time-series analysis, classification and graphical techniques that offer effective data handling and storage facility. Oracle data mining: It is a module of Oracle Advanced Analytics Database that allows data analysts to generate detailed insights and make predictions. It helps predict customer behaviour, develop customer profiles, and identify cross-selling opportunities. How Data mining is used in Various Industries ✓
In the communications industry, data mining is used to predict customer behaviour and provide a highly targeted and relevant campaign.
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In the manufacturing industry, data mining helps manufacturers predict wear and tear of production assets. They can anticipate maintenance which helps them to minimize downtime.
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In the banking industry, data mining provides a view of market risks, helps manage regulatory compliance, and identify probable defaulters to decide whether to issue credit cards, loans, etc.
Enormous amounts of data are generated everyday and businesses are using big data analytics to process it and extract valuable information. Analyze the industry you are working in, your consumer’s behaviour, what they want, what they buy, where they are located, and other factors influencing buying behavior. Discovering patterns and relationships in data can help you make better business decisions and develop better marketing campaigns. To apply data mining techniques, data has to be digitized. Partnering with a document conversion company is the best option when it comes to digitizing large volumes of documents.
www.managedoutsource.com
(800) 670 2809