Data Mining: Turning raw data into useful information

How raw data is converted into useful information
Data mining and raw data.


The many phases to identify the data, clean and ship it into useful information is a part of data mining that has become a trendsetter and will be the next big consistent skill to develop in the times to come with a promising and progressing career.

Techniques and methods to data-mine have been rapidly improving in the last decade due to the progressing power and speed. This helps the data miners to move beyond manual, tedious, and time-consuming practices to quick, easy and automated data analysis.

Data Mining is typically the process to identify anomalies, patterns and correlations within large data sets to predict outcomes. Companies and brands implement this practice with a wide range of additional techniques to boost revenue, improve customer relationships, business investment risks, and much more.

Data Mining relies on the three significant scientific disciplines: statistics (the numeric study of data relationships), artificial intelligence (human-like intelligence displayed by software and or machines), and machine learning (algorithms that can learn from data to make predictions).  

Why is Data Mining important?

With a huge lurch in the data volume that has been multiplying over the past few years, it is imperative to structure the unstructured data that dominates 90% of the digital universe. Therefore, data mining will help you structure the data on the basis of your demand, quality, and quantity.

With the help of multiple buckets created within the data, it is easier to evaluate outcomes and accelerate your pace to make informed decisions.

Types of data

Before learning the tools and techniques for data mining, one must have a clear understanding of the dynamic categories of data that can be cleaned and converted to key information.

  • Relational Databases
  • Data Warehouses
  • Advanced DB and information repositories
  • Object-Oriented and Object-Relational Databases
  • Transactional and Spatial Databases
  • Heterogeneous and Legacy Databases
  • Multimedia and Streaming Database
  • Text Databases
  • Text Mining and Web Mining

 What are the techniques of Data Mining?

Now that you have learned the types of data, one must comprehend the various techniques to mine it and make it more relevant to serve a larger purpose.

  • Classification: Through this technique, you can retrieve crucial and relevant information about data and metadata. This method will help you in classifying and sub-dividing the data into different class formats.
  • Clustering: This technique helps you to identify data that are alike. The process helps to understand the differences and similarities between the data.
  • Regression: It is the data mining method of identifying and analysing the relationship between variables. It is used to note the likelihood of a specific variable, given the presence of other variables.
  • Association Rules: This data mining technique helps to find the association between two or more Items and can discover a hidden pattern in the data set.
  • Outer Detection: This technique also called Outlier Analysis or Outlier mining refers to the observation of data items in the dataset that do not match an expected pattern or the behaviour. The technique can be used in a variety of domains, such as intrusion, detection, fraud or fault detection, etc.
  • Sequential Patterns: This technique helps to discover or identify similar patterns or trends in transaction data for a certain period.
  • Prediction: This technique uses a combination of the other data mining techniques like trends, sequential patterns, clustering, classification, etc to analyse past events or instances in the right sequence for predicting a future event.

How to implement the data?

The next stage is understanding of the data implementation which can be categorized as follows:

  • Business Understanding: Evaluate and research the current data mining scenario. A good data mining plan is very detailed and should be developed to accomplish both business and data mining goals.
  • Data Understanding: In this stage, you need to sanitize the data and evaluate to check whether it’s appropriate for the data mining goals in which you collect the data from dynamic resources and platforms available. This is a complicated process as data from various sources is unlikely to match easily. These data sources may include multiple databases, flat filer,s or data cubes. Here, Metadata should be used to minimize the errors in the data integration process.
  • Data Preparation: This phase allows you to make the data production-ready which consumes about 90% of the time of the project as it involves selecting, cleaning, transforming, formatting, anonymizing, and constructing procedures.
  • Data Transformation: This phase primarily contributes to the success of the mining process which includes the following:
  1. Smoothing: It helps to remove noise from the data.
  2. Aggregation: Summary also called aggregation operations are applied to the data.
  3. Generalisation: Low-level data is replaced by higher-level concepts with the help of concept hierarchies. For example, the city is replaced by the country.
  4. Normalisation: It is conducted when the attribute data is scaled up or scaled-down.
  5. Attribute Construction: Attributes are constructed and included in the given set of attributes which help in further data mining.
  • Modeling: Based on the project objectives, appropriate modeling techniques should be shortlisted for the prepared dataset.
  • Evaluation: This is a go or no-go decision phase in which the decision to move the model in the deployment should be taken. One has to identify patterns that are evaluated against the business objectives.
  • Deployment: In this phase, you ship your data mining discoveries to everyday business operations. A final project report is created with lessons learned and key experiences during the project.

Career Outlook

In today’s data-driven world, companies are relying on data scientists to provide relevant data to stay in the forefront with impeccable consistency. The demand for individuals with these skills is witnessing a consistent surge. As the demand for skilled professionals to fill these positions increases, the salaries offered are also likely to increase. According to a Glassdoor report, the average salary of a Data Scientist is $108,224 per year and the salaries range from $79,000 to $145,000 depending upon the knowledge and expertise the candidate brings to the table.

Leading tech giants like Oracle, Apple, Microsoft, Walmart are always on a constant lookout for data scientists.

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Related Articles:
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Prerequisites to pursue a career in Data Science

  • Machine Learning:
    : Algebra, ML Algorithms, Statistics
    Tools: Spark MLlib, Mahout, Azure, ML studio
  • Mathematical Modeling
  • Statistics
  • Computer Programming like Python
  • Data Analysis:
     R, Python, Statistics
    Tools: SAS, Jupyter, R studio, MATLAB, Excel, RapidMiner
  • Data Warehousing:
    ETL, SQL, Hadoop, Apache Spark
    Tools: Informatica, Talend, AWS Redshift
  • Data Visualisation:
    Skills: R, Python libraries
    Tools: Juptyer, Tableau, Cognos, RAW

To read more about data and machine learning, read here.