Data Warehouse: What is it and what are its applications? - Edge1s

Data Warehouse: Your Guide to Data Storage and Analysis

Data plays a key role in today’s world made up of tons of interrelated information that is used to provide relevant products and services, learn about customer needs, study the effectiveness of the organization’s operations, as well as carry out research to predict upcoming changes in various time periods.

Data warehouses come to the aid of collecting, storing and analyzing vast amounts of data in a structured, efficient and purposeful way.

With the growing number of digital information sources, as well as the ongoing digital transformation, data warehouses have become an indispensable tool for organizations that intend to fully utilize the potential of the data collected in their resources.

data warehouse

What is a data warehouse?

A data warehouse is a sophisticated IT system designed to collect, store and manage large sets of data from various sources. A data warehouse is a central repository where all data from different sources is merged, integrated, organized, structured, and then made available in a coherent and orderly manner. The entire process is efficient, allowing for interactive analysis and the storage and sharing of data history, e.g. for running comparisons over time.

Characteristics of data warehouses include thematic orientation, high level of integration, durability and integrity of data, as well as collection of history (archived data). Thanks to these characteristics, data warehouses are widely used in decision-making processes at tactical and strategic levels, significantly supporting key decision-making for the organization.

The concept of data warehouses (dwh for short) first appeared in the 1980s, and thorough research and the first implementations began in the 1990s. Since then, data warehouses have been steadily gaining popularity and have been used by more entities in every sector of the economy.

Data warehouses are central repositories where organizations can store various types of data, such as operational data, transactional data, customer data, sensor data, social media data and more. Collecting this data from various sources and integrating it in one place enables organizations to get a consistent and holistic view of their operations.

The main goal of building a data warehouse is to provide fast, easy and intuitive access to data while enabling quick search, analysis, data comparison and report generation. Data warehouses are designed to handle a wide range of data and analytical scenarios, while classic databases are optimized for use in a given application or ecosystem.

Data warehouses are modern and sophisticated IT systems increasingly supported by artificial intelligence, machine learning and neural network algorithms that enable the integration of data from various elements of the IT infrastructure such as:

  • operational databases;
  • text files;
  • data streams;
  • CRM systems;
  • ERP systems;
  • IoT (Internet of Things) devices;

Once the various elements of the IT infrastructure are integrated into the data warehouse, they are subjected to a transformation process to be integrated with one another. As a consequence, they can undergo analysis and comparison.

Appropriately aggregated data from data warehouses is deposited in data repositories, which are prepared for specific use and intended for a particular audience. The information stored there is highly aggregated and denormalized which facilitates its analysis.

Data warehouse basics

Data warehouses are built utilizing a number of basic and essential elements to function. These include a central database, a data integration process, metadata and data warehouse access tools.

The central database includes all data retrieved from external sources. These can include relational or non-relational databases of IT systems, CRM, ERP, SAP systems, office suite files or analytical data extracted from dedicated tools (e.g. Google Analytics).

The data integration process uses specialized ETL software – Extract, Transform, Load – to unify the stored data. It is properly prepared through a process of cleaning, segregating, removing duplicates and unifying formats.

The process of data integration is combined with the proper assignment of metadata to the information held. They enable easy identification of the source of the information, its value, its connection to other data, and assigning it to a category to facilitate information retrieval.

The element most closely residing with the user are the tools that allow access to the data warehouse. This is specialized software enabling interaction with the data in the data warehouse. What it allows you to do is generate queries, reports, enable data mining and data modification.

How is a data warehouse built?

The way in which a data warehouse is built is strongly dependent on the specific business requirements of organizations implementing data warehouses in their IT infrastructure.

The process of building a data warehouse begins with defining business objectives. It is necessary to distinguish key performance indicators (KPIs) and questions that the data warehouse is supposed to help find answers to.

The next step is to analyze the technical requirements of the data warehouse. It boils down to identifying the data sources needed, the scope of the data, its format, the frequency of updates, as well as identifying the availability and performance needs of the entire system.

Based on the requirements analysis, the data warehouse architecture is designed. At this stage, it is necessary to decide whether to use a top-down (dimensional modeling) or bottom-up (relational modeling) approach. The design includes defining the structure of the data, the relationships between tables, as well as how the data will be processed and stored. You should prepare for a data warehousing process that allows for data systematization.

Having designed a data warehouse architecture, it is necessary to choose the right tools and technologies to build it. These include databases, ETL, metadata management, reporting and data analysis tools. Their number and functionality depends on the needs and capabilities of the organization implementing the data warehouse.

In the process of building a data warehouse, it is necessary to obtain data from various sources that will be included in the data warehouse.

Once the necessary data has been collected, the process of creating the data model can begin. Once the ETL tools responsible for data acquisition, transformation and loading are in place, the data to be analyzed is imported into the data warehouse. ETL processes must be consistent with the designed data model and take into account data quality requirements.

Once the data warehouse is built, testing is carried out to ensure that the data is correctly extracted, transformed and stored. Verifying the data and comparing the results with business expectations is key to ensuring the quality of the data warehouse.

It is worth noting that the process of building a data warehouse is an ongoing process that requires constant maintenance. After successful testing, the data warehouse is deployed in a production environment. It is necessary to monitor and maintain the data warehouse, manage security, optimize queries and adjust the data model as the business grows.

Building a data warehouse is an ongoing process – as the organization implementing the data warehouse grows, its requirements evolve, and the data warehouse very frequently undergoes modifications and expansions.

Data warehouse architecture and components

Three basic data warehouse architectures can be distinguished on the market:

  • single-tier architecture
  • two-tier architecture
  • three-tier architecture

The most basic are data warehouses built on a single-tier architecture. Data is first uploaded to the single-tier architecture, where it is properly converted to a universal format. After this process, data analysis is possible. Single-tier architecture, also called single-threaded architecture, is cost-optimal and simple to implement, but it does not enable real-time data processing. Single-tier architecture often uses a star diagram also called a star model – it involves the creation of a central database (fact tables) to which individual dimension tables are connected.

In a two-tier data warehouse, the analytical process is separated from the business process. This allows for a greater level of control and efficiency. A two-tier system also provides a better understanding of the data and allows for more informed decision-making. The two-tier model is an ideal compromise between efficiency, capability, and cost-effectiveness.

The three-tier architecture includes a centralized repository and an OLAP (On-Line Analytical Processing) server. The solution extends the two-tier data warehouse architecture with an end-user view of the database, which helps the scalability and performance of the entire system.

A data warehouse contains elementary data, historical data, aggregated data, as well as metadata. Data warehouses have their life cycles, which begin with feeding the data warehouse with information, aggregating it, and then archiving it. In specific cases, data deletion may occur.

A number of operations are used when analyzing data collected in data warehouses. Some of the most popular include:

  • rotation – changing the perspective of the data in the data warehouse
  • selection – selecting only interesting and useful data
  • slicing – extracting relevant information from large data sets collected in the data warehouse.
  • data convolution – the process of combining and reducing data sets to a higher level of the hierarchy to produce more general, summarized results
  • data ranking – arranging data in order from highest to lowest value against a specific metric or criterion

The architecture of a data warehouse is closely related to its components, which include a central database, ETL tools, metadata (describing the structure and meaning of data, aggregations, data sources, as well as the history of data use), and tools to access the database.

Online Analytical Processing (OLAP) technology is responsible for advanced data analysis in an interactive, dynamic and multidimensional way. It allows the presentation of multidimensional views of data, interactive creation of queries and analysis, calculation of aggregates, conducting statistical analysis, trends, forecasting or modeling. Modern OLAP tools are characterized by high scalability and speed.

Typical OLAP tools include – ranging from the simplest – spreadsheets, to off-the-shelf tools for building analytical applications, to dedicated solutions that respond to given problems.

Data warehouse applications

Data warehouses, due to their complexity and high degree of personalization, have a very wide range of applications in various fields and industries. What are examples of data warehouse applications? Among them we can include:

  • Business analytics – With data warehousing, you can analyze financial results, performance metrics, sales trends, customer preferences, user behavior and many other business aspects. This knowledge allows you to make better strategic and tactical decisions.
  • Customer segmentation and targeting – Data warehouses are used to segment customers based on various criteria, such as buying behavior, preferences, geographic location, etc. This allows organizations to target their marketing efforts more effectively and personalize their offerings to different customer groups.
  • Market trend research and forecasting – Data warehouses are used to analyze trends and predict future behavior. By analyzing historical data, organizations can identify patterns, detect changes and forecast future market trends, product demand or customer preferences. This allows for better planning and decision-making.
  • Support for CRM systems – Data warehouses support customer relationship management by storing and analyzing customer-related data. They enable the collection of information about customers, interaction history, transactions, preferences, etc. As a result, organizations can better understand their customers, customize communications and provide a more personalized experience.

Data warehouse benefits and challenges

Implementing a data warehouse in an organization comes with many benefits, as well as several challenges that must be addressed in the process of building the entire solution.

Benefits of data warehousing implemented in organizations include:

  • Data integration
  • Easy access to data
  • Higher quality of the data held
  • Enhanced analytical tools
  • Consistent and uniform flow of information
  • Easy forecasting and market analysis

Organizations implementing data warehousing in their IT infrastructure sometimes face challenges that include:

  • The problem with acquiring enough data and extracting it from a fragmented IT infrastructure
  • Time and cost required to build a data warehouse
  • Ensuring data format compatibility
  • Securing the data warehouse from cyber-attacks
  • Adapting the data architecture as the organization’s business goals grow and change
  • Modeling problems – the difficulty in determining the business purpose of the data warehouse
  • Data volatility – evolution of the data warehouse schema, dictionaries, and changes in database schemas
  • Data warehouse performance problems

How to effectively use a data warehouse?

Effective use of a data warehouse involves transforming the collected data into valuable business information and knowledge. Key aspects to effectively use data warehouses include:

  • Effective definition of business objectives
  • Selection of appropriate tools and technologies
  • Maintaining and constantly updating data to ensure that it is up-to-date and of high quality
  • Providing easy access to the data
  • Using advanced analytical techniques to uncover new relationships between the information held
  • Monitoring and optimizing the performance of the data warehouse
  • Continuously improving and adapting the data warehouse to the current requirements of the organization

Effective use of data warehousing requires continuous commitment, monitoring and improvement. Data warehousing is an ongoing process. It is critical that the data warehouse be updated to accommodate changing business requirements and use new analytical technologies to get maximum value and insight from the data warehouse.

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