Unlock the Mysteries of Data Warehousing: Data Mining Explained

A data warehouse is a place where a company or other organization stores electronic information in a safe way. A data warehouse is meant to be a treasure trove of historical data that can be retrieved and analyzed to learn useful things about how an organization works.

A data warehouse is a vital component of business intelligence. This broader term includes the information infrastructure that modern companies use to keep track of their past successes and failures and make decisions about the future.

How Data Warehousing Works

As companies started to use computers to make, store, and find important business documents, the need to store data grew. Barry Devlin and Paul Murphy, two IBM researchers, came up with the idea of data warehousing in 1988.

Data warehousing is designed to enable the analysis of historical data. A comparison of consolidated data from multiple heterogeneous sources can provide insight into the performance of an enterprise. A data warehouse is made so that its users can look up and analyze historical data from transactional sources.

Data warehousing is the process of collecting, organizing, and managing data from multiple sources for analysis and reporting.

Bill Inmon, Father of Data Warehousing

The data added to the warehouse does not change and cannot be altered. The warehouse is the source from which analyses of past events are run, with a focus on changes over time. Stored data must be stored securely, reliably, easily retrievable, and easily manageable.

Maintenance of a Data Warehouse

To maintain a data warehouse, certain steps must be followed. One of them is data mining, which involves collecting large amounts of data from multiple sources. After the data is collected, it goes through a process called “cleaning,” which involves looking for mistakes and fixing or getting rid of them.

The cleaned data is then converted from a database format to a warehouse format. Once stored in the warehouse, the data is sorted, consolidated and summarized for ease of use. Over time, more data is added to the warehouse as the various data sources are updated.

A key book on data warehousing is W. H. Inmon’s Building the Data Warehouse, a practical guide that was first published in 1990 and has been reprinted several times.Today, companies can invest in cloud based data warehouse software services from companies such as Microsoft, Google, Amazon and Oracle, among others.

Data Mining

Companies store data primarily for data mining. This is to look for patterns of information that will help them improve their business processes.

A good data warehousing system makes it easy for different departments in a company to access each other’s data. For example, a marketing team can evaluate data from the sales team to make decisions on how to adjust their sales campaigns.

The 5 Steps of Data Mining

The data extraction process is divided into five steps:

1. An organization collects data and loads it into a data warehouse.
2. The data is then stored and managed, either on internal servers or in a cloud service.
3. Business analysts, management teams and IT professionals access and organize the data.
4. Application software sorts the data.
5. The end user presents the data in an easy to share format, such as a graph or table.

What is Data Mining?

The data warehousing concept was introduced by two IBM researchers in 1988.

Data Warehousing Architecture

The design of a data warehouse is known as its “data warehousing architecture,” and, depending on the needs of the warehouse, it can be presented in several tiers. Typically, there are tier one, tier two, and tier three architecture designs.

Single layer architecture: Single layer architecture is rarely used in the creation of data warehouses for real time systems. They are typically used for batch and real time processing to process operational data. A monolayer design consists of a single layer of hardware with the goal of keeping the data footprint to a minimum.

Two tier architecture: In a two tier architecture design, the analytical process is separated from the business process. The objective is to increase levels of control and efficiency.

Three tier architecture: A three tier architecture design has a top, middle and bottom layer; these are known as the source layer, reconciled layer and data warehouse layer. This design is suitable for systems with long life cycles. When changes are made to the data, an additional layer of data review and analysis is completed to ensure that no errors have occurred.

Regardless of the tier, all data warehousing architectures must meet the same five properties: separation, scalability, extensibility, security and manageability.

Data Warehousing vs Data Base

A data warehouse is not the same as a database:

• A database is a transactional system that monitors and updates data in real time so that only the most recent data is available.
• A data warehouse is programmed to aggregate structured data over time.

For example, a database may have only the most recent address of a customer, while a data warehouse may have all customer addresses for the last 10 years.

Data mining is based on the data warehouse. The data in the warehouse is sifted to obtain information about the company over time.

Data Warehousing vs Data Lake

Both data warehouses and data lakes contain data for a variety of purposes. The main difference is that a data lake holds raw data whose purpose has not yet been decided. A data warehouse, on the other hand, contains refined data that has been filtered to be used for a specific purpose.

Data lakes are primarily used by data scientists, while data warehouses are typically used by business professionals. Data lakes are also more accessible and easier to update, while data warehouses are more structured and any changes are more costly.

Data Warehousing vs Data Mart

A data mart is nothing more than a scaled down version of a data warehouse. A data mart collects data from a small number of sources and focuses on one subject area. Data marts are faster and easier to use than data warehouses.

Data marts typically function as a subset of a data warehouse to focus on an area for analytical purposes, such as a specific department within an organization. Data marts are used to help make business decisions through analysis and reporting.

Advantages and Disadvantages of Data Warehousing

The point of a data warehouse is to give a company an edge over its competitors. It gives the company a source of useful information that can be tracked over time and analyzed to help it make better decisions.

It can also use up a lot of the company’s resources and make it hard for the people who are already working there to do their jobs because they have to do so many routine tasks to feed the warehouse machine. Other disadvantages include the following:

• Considerable time and effort is required to create and maintain the repository.
• Gaps in information, caused by human error, can take years to surface, impairing the integrity and usefulness of the information.
• When multiple sources are used, inconsistencies among them can cause information loss.
• Provides fact based analysis of past company performance to inform decision making.
• Serves as a historical archive of relevant data.
• Can be shared among key departments for maximum utility.
• Provides fact based analysis of the company’s past performance to inform decision making.
• It serves as a historical archive of relevant data.
• It can be shared among the main departments for maximum utility.
• Creating and maintaining the warehouse is resource intensive.
• Input errors can damage the integrity of archived information.
• Using multiple sources can lead to data inconsistencies.
• The creation and maintenance of the warehouse is resource intensive.
• Data entry errors can damage the integrity of the archived information.
• The use of multiple sources can lead to data inconsistencies.

What is a Data Warehouse and what Is It For?

A data warehouse is a place to store information from the past that can be looked at in many different ways. Companies and other organizations use the data warehousing to learn about past performance and to plan improvements in their operations.

What is an Example of a Data Warehousing?

Let’s think about a company that manufactures fitness equipment. Its best selling product is an exercise bike, and it is thinking of expanding its line and launching a new marketing campaign to support it.

Go to your data warehouse to learn more about your current customer base. You can find out if your customers are predominantly women over 50 or men under 35. You can learn more about which retailers have been most successful in selling your bikes and where they are located. You can access internal survey results and find out what your former customers have liked and disliked about your products.

All this information helps the company decide what kind of new bike models it wants to produce and how it will market and advertise them. This is solid information, not spur of the moment decision making.

What are the Steps Involved in Creating a Data Warehouse?

Creating a data warehouse consists of at least seven steps, according to ITPro Today, an industry publication. These include:

• Determine business objectives and their key performance indicators.
• Collect and analyze the right information.
• Identify the core business processes that provide the key data.
• Build a conceptual data model that shows how the data is displayed to the end user.
• Locate the sources of the data and establish a process for feeding the data to the warehouse.
• Establish a tracking duration. Data warehouses can become unwieldy. Many are built with archive levels, so older information is retained in less detail.
• Execute the plan.

Is SQL a Data Warehouse?

SQL, or Structured Query Language, is a computer language used to interact with a database in terms that the database can understand and respond to. It contains a series of commands such as “select”, “insert” and “update”. It is the standard language of relational database management systems.

A database is not the same as a data warehouse, although both are stores of information. A database is an organized collection of information. A data warehouse is an archive of information that is continually being built from multiple sources.

What is ETL in a Data Warehouse?

“ETL” stands for “extract, transform and load”. ETL is a data process that combines data from multiple sources into a single data storage unit, which is then loaded into a data warehouse or similar data system. It is used in data analytics and machine learning.

Wrap Up

The data warehouse is where a company stores information about what it has done and how well it has done over time. It was made with input from employees in each of the company’s key departments. It is used to analyze the company’s past successes and failures and to make decisions.

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