Data Mining How-Tos: Techniques, Tools and Best Practices

For a process so sophisticated as data mining, there are different approaches to consider and a few techniques you can try to make the most out of it.  

A data-driven enterprise — this is the future we’re looking at. According to a McKinsey insight, by 2025, organizations are bound to use data in every area of the business to optimize what they do. And with data being used in these approaches, it’s crucial to learn the proper way to harvest information and then later translate them as actionable insights. 

Depending on the goal of your business, there are different data mining practices you can apply to come up with a model that corresponds to your desired outcome. Here are your must-knows on how to make your data mining start and be successful.

Data Mining Process 

Like any other business activity, there is an existing process to be followed before data mining is fully deployed. Here is the step-by-step guide on how it's done. 

1. Having a business understanding 

   Do a research about the business. What are its goals? Do they have the resources to make them happen? How are they working on it internally? Based on the company’s perspective, assess the business’ current situation and other significant factors and possible constraints that may undermine the achievement of the data mining initiative. 
    

2. Acquiring data understanding 

   This step entails the collection and filtering of data from different sources (e.g. software, CRMs, databases, website, etc.), with the purpose of ensuring the data quality before it undergoes mining. Verifying the data’s attributes can lead into an efficient process as potential issues are identified before it proceeds to data integration, setting the seal of anomaly-free and top-shape information. 
     

3. Preparing the data

   Data preparation takes up a considerable chunk of the time in the entire data mining process because of its iterative nature. This is because it encompasses cleaning, formatting, constructing and integrating phases, devising the final form of data sets ready for analysis.
   
   
   1. Transforming the data
      Nesting under the data preparation step, data transformation is divided into five sub-stages. 
      • Aggregation — the pooling of data from different sources  
      • Smoothing — noise is removed from the data  
      • Generalization — converting low-level data to high-level data features 
      • Normalization —  defining data within a specific range 
      • Discretization —  changing data attributes into sets of intervals
         

4. Building a model

   This is the part where data mining comes in, and is repetitive as well. Through the data mining techniques and tools that we will discuss in the next section, analysts get a good grasp of the trends, patterns and correlations in the data sets that lead to a better analysis.  

   It’s also worth noting that the preceding step may be repeated as some of the data models need to be processed only in a particular format. Your goal in this stage is to identify the approach that yields the best results by applying different models to your prepared data set. Once ready, you can do a quality check on the model by generating a test (depending on the data mining technique you used) and if it works, you can proceed with building the data model. 
   

5. Validating the results 

   Closely evaluate the results to validate the accuracy of the results. Does it meet your data mining objectives? If not, go back a step. 
    

6. Deploying the model 

   In response to the newfound analysis, executives can now take action by applying the discovery into your business. You can now strategically maneuver your next steps to efficiently achieve your business goals. 

Related: What is Data Mining and its Applications in Business Intelligence? 

Data Mining Techniques  

For data mining to work, algorithms and techniques are injected into the formula. This helps in achieving a model that best elaborates a given data set and draws a unique insight for each business problem.   

Here are the best data mining practices and techniques you can try: 

• Classification analysis

  In this technique, data is assigned to pre-defined groups or categories. Relevant attributes of data are collected to form these classes, which are then used to classify data into different clusters. 

• Association rules

  Association rules posit that there is a causal link between different variables, thus driving the idea that one data-driven occurrence is because of the presence of another. Hidden patterns are uncovered by identifying the relations and interdependencies of data that are otherwise unclear in large databases.  

• Anomaly or Outlier detection

  From the term itself, outlier detection determines anomalies and irregularities in your data. These outliers are presented in form of noise, deviations, spikes, aberrations and errors, among others. Such a technique is usually used to uncover fraudulent cases or to pinpoint what caused a peculiar happening in a business. 

• Clustering  

  Clustering is a counterpart of the classification analysis in a way that the former groups objects according to similarities while the latter classifies data into pre-set categories. 

  This routine aims to organize data with the highest associations because of their similar attributes, then ensures to make each cluster is dissimilar from the rest of the group. 

• Regression analysis

  Widely used for planning and forecasting, regression analysis’ main goal is to recognize the nature of relationship between two or more variables in a given data set. It gauges how changes in one variable affect another variable, defining either a casual or correlational link between them. 

• Predictive analysis

  Predictive analysis overlaps regression technique, as they are both used to provide a forecast but are done by different means. Prediction is a straightforward method. It uses historical and current data to project the following trends and what will likely happen based on previous actions taken. 

Maximizing Data Mining Opportunities 

As we see data-driven initiatives gain more attention today, we also understand how a variety of disciplines come together to empower business analytics efforts, just like data mining. 

Learning the how-to's of data mining would significantly augment a business’ efforts to tell meaningful insights and stories through the data they have. With the proper techniques, practices and tools, you have the power to draw insights and use them to craft a strategic solution to your business problems. 

Want to get a better hold of your available data? We can lend a hand.  

D&V Philippines provides CFOs and small businesses with business analytics and reporting solutions to help you shift your approach to a data-driven enterprise. You can grab a copy of our whitepaper The Rising Frontier: Harnessing The Power Of Business Analytics to learn more about business analytics or you can also schedule a free consultation with us to know how we can serve you better.