What data could do without analytics? Nothing, it would have just been a data collection with no meaning. Analytics defined data using data mining. Google defines data mining as an analytic process designed to explore data (usually large amounts of data – typically business or market related – also known as “big data“) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns. Analytics can be used in data mining process to the extreme content.
Here are the Data mining classes of tasks:
- The identification of unusual data records or data errors that require further investigation.
- Searching for relationships between variables.
- Discovering groups and structures in the data that are in some way or another “similar”
- Generalizing known structure to apply to new data.
- Regression – to find a function this models the data with the least error.
- Compact representation of the data set.
Now let’s look into each step and understand how analytics play a vital role in data mining.
The first two steps are the most crucial ones. Depending upon the data, you have options of straightforward predictors for a regression model, to elaborate exploratory analysis. This is the basic yet most important step in data mining. If this goes wrong, the complete analysis would have an incorrect basis.
The next two stages incorporates the technique of applying the correct once choice. It involves various models and predicting the best one. The variables you choose are independent and predictive analysis comes in play for these steps. You may have to apply different techniques on the same data set and get the most desired output.
Now let’s talk about the final two stages of data mining which produces results. The model which you have selected in the previous two stages would be applied to the new data and produce predictions. What future could react to our stimulus is predicted in this step. Errors are eradicated to the maximum and if the desired results are not generated, re-evaluation is done.
Almost every field in business requires data mining. Custom relationship management is one such area wherein customer-related data is scanned and explored to get into the minds of customers. The return on investment is high and this is profitable as you get to know what your business would be most likely to behave to changes. Science, engineering, social business, everything encompasses this beautiful tool.