Data Warehousing and Data Mining: 6 Critical Differences

on Data Analytics, Data Driven, Data Warehouse, Data Warehouses, ETL • June 9th, 2021 • Write for Hevo

Data Warehousing and Data Mining

Deriving actionable insights from data and making them part of the business decision-making process is a key ingredient to success for businesses in modern times. This is made possible by sophisticated data platforms that accumulate data from various sources and analytics teams that dig through this data to derive insights. This article talks about Data Warehousing and Data Mining.

Data Warehousing and Data Mining are two integral parts of this data-driven decision-making approach. Data Warehousing deals with having unified storage for all kinds of data in an organization. This requires data from various aspects of the business to be formatted into a form suitable for analysis and easy access.

Once the data is in such a format, analysts or automated pattern matching algorithms dig through the data to derive insights. This process is called Data Mining. This article will help you understand the key differences between Data Warehousing and Data Mining.

Table of Contents

What is a Data Warehouse?

Data Warehousing and Data Mining: Data Warehousing Process
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A Data Warehouse can be defined as a Database or a collection of Databases used to centralize an enterprise’s historical business data. These data sources could be the Databases of various Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, and other forms of Online Transactional Processing (OLTP) systems.

Data Warehouse is the most preferred form of data storage today due to its ability to scale storage requirements up or down as per the business and data requirements. This means that a Data Warehouse is capable of providing unlimited storage to any business.

Data Warehouses are required simply because businesses today rely on data-driven decision-making to plan their business strategies. For any analysis to be performed successfully, the data from all data sources must be loaded into the Data Warehouse in a form suitable for analysis. 

To learn more about Data Warehouse, visit here.

What is Data Mining?

Data Warehousing and Data Mining: Data Mining Logo
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Data Mining can be defined as the process of analyzing large volumes of data to derive useful insights from it that can help businesses solve problems, seize new opportunities, and mitigate risks. It can be leveraged to answer business questions that were traditionally considered to be too time-consuming to resolve manually.

By using a range of statistical techniques to analyze data in different ways, businesses can seamlessly identify patterns, relationships, and trends. For example, the world’s most popular streaming platform, Netflix, has approximately 93 million active users per month. The data pipeline of Netflix captures more than 500 billion user events per day. This includes data on various things such as video viewing activities, error logs, performance reports, etc.

The storage of this data requires approximately a storage space of 1.3 Petabytes (1 Petabyte = 1,000,000 Gigabytes) per day. The advantages of having such high volumes of data are as follows:

  • It allows Netflix to plan their future releases by analyzing the kind of content viewers like.
  • It allows Netflix to understand how they can make the user experience on their website and Android/iOS applications better by analyzing user behavior on these services.

To learn more about Data Mining, visit here.

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Key Benefits of Data Warehouse

  1. Data Consistency: Data Warehouse takes care of data consistency and data quality. You don’t need to worry about data integrity issues as a Data Warehouse stores data in an analysis-ready format.
  2. Data Integration: A Data Warehouse is capable of integrating, or in simple words, combining data from multiple heterogeneous sources such as Relational Databases, Flat Files, etc.
  3. Time-Variant: Data Warehouse provides information from data with respect to a particular time period. It also provides more timely data access.
  4. Non-volatile: Non-volatile means residing data in a Data Warehouse is not erased when new data is ingested in it.

Key Benefits of Data Mining

  1. Pattern Discovery: Automatic pattern discovery is a strategic advantage, and this technique helps in modeling and predicting future behavior.
  2. Trend Analysis: Understanding trends keeps you up-to-date with current developments in the industry, and helps reduce costs and timeliness to market.
  3. Fraud Detection: Data Mining techniques help in fraud detection by discovering anomalies in datasets. This is used to detect which insurance claims, credit card purchases, etc., are likely to be fraudulent.
  4. Forecasting in Financial Markets: Data mining Techniques are extensively used to model financial markets and predict likely outcomes.

Differences between Data Warehousing and Data Mining

Data Warehouse vs Data Mining: Differences
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The key differences between Data Warehousing and Data Mining are as follows:

1) Objective

The main objective of Data Warehousing is to create a centralized location where data from various sources can be stored in a form that is easily explorable. An ideal Data Warehouse has the following qualities:

  • It should be able to handle a large amount of data without much cost.
  • It should be able to scale up without having to take up massive migration jobs as the data volume increases.
  • It should be able to store metadata or add metadata on the fly to the stored data.

On the other hand, the primary objective of Data Mining is to explore the data stored in Data Warehouses and derive valuable insights from it that can directly affect the revenue or costs of any business. This requires a tool that can provide quick answers about the data or, in the ideal case, a tool that can ask questions independently. 

2) Methodology

Next up is the methodology deployed by Data Warehousing and Data Mining solutions. Data Warehousing methodology is based on Extract, Transform and Load (ETL) jobs. In a nutshell, this means there are scheduled jobs that extract data from various sources, transform them into different formats, and load them into a Data Warehouse.

With the advent of databases with excellent transformation abilities, an alternate pattern called Extract, Load, Transform (ELT) has also evolved. This pattern exploits the excellent built-in data processing capabilities of modern Data Warehouses. 

Data Mining involves using human intelligence or statistical and mathematical techniques to derive rules between data. It involves finding correlations between events, detecting outliers, or in the simplest case, even deriving a metric that can accurately estimate customer satisfaction.

It is an iterative process with a lot of trial and error involved. Data Mining efforts generally start from a specific objective such as improving profitability, reducing costs, improving net promoter score, etc.

3) Data Sources

The data sources for Data Warehousing can be virtually anything that gives some information about the company’s fortunes. The sources can be On-premise or Cloud-based services. In some cases, there is a Data Lake involved between the actual sources and the Data Warehouse.

Some of the typical sources are transactional data from an On-premise database, customer data from a Cloud-based Customer Relationship Management (CRM) software, data from a Social Media Marketing campaign, etc.

The data source for a Data Mining operation is usually a Data Warehouse where all data regarding a company is kept. In some cases, it can even be a Data Lake where unformatted raw data is kept. In short, Data Mining happens on data that has already been collected in some form. 

4) Tools

Various tools are required to perform both, Data Warehousing and Data Mining, let’s discuss them.

Data Warehousing requires a scalable data storage area that can be explored. A Hadoop-based data platform with Hive, Presto, or Spark is a typical choice for organizations that build everything On-premise. Completely Cloud-based tools like AWS Redshift, Snowflake, etc., are alternatives for organizations that embrace the Cloud paradigm.

While the above tools handle the storage and processing part, there is also the need for ETL tools to facilitate the transformation and load jobs. Tools like Hevo, Pentaho, Talend, Apache Nifi, etc., fill this void.

More information about ETL and the best tools in the market can be found here.

Data Mining requires tools that can answer questions regarding data quickly or even ask questions on their own. Tools like Microsoft PowerBI, Tableau, etc., help analysts visualize the data and derive valuable insights from it. AWS QuickSight and Google Data Studio are Cloud-based Business Intelligence tools that can be used for this purpose.

All these tools offer Machine Learning capabilities that can understand basic patterns without much human intervention. That said, in the hands of a skilled analyst, even the SQL layer of a Data Warehouse is a good enough tool to derive insights.

5) Skillset

Data Warehousing requires more engineering skills when compared to Data Mining. It requires programming ability in languages like Python, Java, or Scala, along with a good knowledge of SQL. Good knowledge of frameworks that can facilitate the operations and monitor the activities is also a much-needed skill.

Data Mining requires analytical skills and domain knowledge. Skills in SQL and the ability to use visualization tools like Tableau, Microsoft PowerBI, etc., are a must. A background in mathematics and statistics is a great skill to have in the modern Data Mining world where everything will eventually point to Machine Learning. 

6) Customers

The end customers of Data Warehousing applications are usually Data Scientists, Business Analysts, etc. Such roles are broadly classified under the realm of Data Mining. 

The end customer of a Data Mining operation is usually senior management responsible for decision making. The derived patterns and insights are usually used to decide how businesses can improve their operations to ensure maximum profit. 

  • Classification/Regression: Classification/regression is the implementation of a data model that is capable of mapping objects into classes or suitable values. It finds its application in classifying customers for credit approval or selective marketing, performance prediction, diagnosing illness from known symptoms, etc.
Data WarehousingData Mining
A centralized location where data from various sources can be stored in a form that is easily explorable.Explores the data stored in Data Warehouses and derives valuable insights from it.
Based on Extract, Transform and Load (ETL) jobs.Requires human intelligence and mathematical techniques to derive rules between data.
Supports all kinds of Data Sources from CRMs to Data Lakes.Data Warehouse acts as a source for Data Mining operations.
ETL and Cloud-based tools are required to facilitate data transformation and loading.Business Intelligence, Data Visualization, and Machine Learning tools are required to derive actionable insights.
Requires engineering and programming skills.Requires analytical skills and domain knowledge.
End customers are usually Data Scientists, Business Analysts, etc.The end customer is usually senior management responsible for decision-making.
Data Warehouse vs Data Mining

This brings us to the end of our comparison between Data Warehousing and Data Mining. You can also check here the list of Data Warehousing and Data Mining solutions.

Now that you’re familiar with the differences between Data Warehousing and Data Mining, let’s discuss some important aspects for both of them.

Data Mining Principles

Below are the 3 basic principles of Data Mining.

  • Information discovered must be previously unknown: Data Mining is the process of extracting and discovering valuable information from large data sets. Hence, the information discovered in Data Mining should be unique and far from obvious. Otherwise, what’s the use of Data Mining if the information can be hypothesized in advance.
  • Information discovered must be valid: Accuracy and validation are important aspects of Data Mining. Data Miners should aim to achieve the highest possible accuracy and should avoid statistically or empirically invalid results.
  • Information discovered must be interesting or actionable: While discovering patterns and correlations within large datasets, there are high chances that thousands or millions of those patterns are mostly redundant or irrelevant. Data Miners should try to refine their results and find interesting patterns for the business’s goals based on statistical measures like frequency and subjective analysis.

Common Data Mining Analysis and Their Business Applications

  • Association Rules: Association Rules state that the objects that satisfy a condition X are likely to satisfy another condition Y. Association Rules find their applications in market basket analysis, financial forecasting, cross-selling, store layout, diagnosing likelihood of illness, etc.
  • Sequential Pattern: Sequential Pattern is the discovery of frequent subsequences in a pool of sequences, treating sequences with a different order separately. It finds its application in marketing funnel analysis, prediction of natural disasters, web traffic analysis, DNA analysis, etc.

How do Data Warehousing and Data Mining Work Together?

Although this article talks about the differences between Data Warehousing and Data Mining, some organizations leverage Data Warehousing and Data Mining techniques together. Large organizations usually perform Data Mining on top of data stored in the Data Warehouse. This is a general process that is usually followed by large enterprises.

  1. Data Collection: Data Engineers load relevant data from multiple sources into the Data Warehouse.
  2. Data Selection: Data Engineers filter the collected data and select relevant data sets after removing irrelevant data.
  3. Data Preparation: Data Preparation is done by Data Engineers to clean and improve the data quality to make the data ready for further analysis.
  4. Data Transformation: Data Engineers transform the data into a suitable format for Machine Learning analysis.
  5. Data Mining: Data Engineers run the data through one or more Machine Learning or NLP models to extract relevant insights.
  6. Analysis of Results: Data Miners study the results and fine-tune the data models to determine validity and business relevance.
  7. Reporting & Visualization: Data Miners make actionable reports and visualize data to explain the insights derived.

Conclusion

This article helped you understand the key differences between Data Warehousing and Data Mining. Both these processes are vital ingredients for the success of any modern business.

The key element of effectively deriving value from your data platform is to have access to a great ETL tool. We hope this article helped you get a comprehensive understanding of Data Warehouse vs Data Mining.

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Integrating data from various sources and loading it into Data Warehouses can be a complicated task. Enterprises can either choose to make their ETL solution in-house or use existing platforms like Hevo.

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Share your experience of understanding Data Warehousing and Data Mining in the comments section below.

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