Data Warehouse

Data Warehouse explained for Non-Developers

the essentials in brief:

  • A data warehouse collects and structures data from a company's various IT systems to optimise it and make it accessible for business intelligence analyses.
  • The unique data structuring of data warehouses differs from data lake architectures, makes it easier to understand complex data relationships, and supports in-depth business analyses.
  • In this article, you will learn how data warehouses work, how they can save time, and how they can increase the quality of your data and analyses.



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What is a Data Warehouse?

A data warehouse (DWH) is an IT application that centrally collects data from operational IT systems within a company, group of companies, or organisation before structuring it for targeted analyses.

In data warehousing, these evaluations are generally referred to as business intelligence (BI). In addition to allowing users to view current data, the information can be used to compare previous years or more extended periods, which can be used to quickly recognise changing developments and create reliable forecasts.

Advantages of data warehousing for companies

1. Time savings and increased data quality

Data warehousing optimises data structures for analyses so they can be carried out more quickly and at a lower cost.

Extensive analyses of transactional data can involve complex and, therefore, error-prone requests. A DWH systematically avoids this source of error.

2.Data-driven offer optimisation

A DWH is easy to use, making it easy to analyse an existing data source in a myriad of ways. The information and insights gained in this way form a solid foundation for further decision-making, such as determining corporate strategy, seasonal scaling, or adjusting product ranges after impactful market events.

The retail sector is a classic success story in this regard, as the industry systematically analyses customer shopping behaviour and journeys to offer the goods to specific target groups and ensure optimised placement in the shop.

3. Lower operating costs overall

Data warehousing can relieve operational systems, so corporate IT infrastructure doesn’t need to be as powerful. Therefore, overall operating costs are lower, even though the DWH is used to operate another IT system.

How does a Data Warehouse work?

A robust database is always the backbone of a data warehouse. However, the company’s data is structured in a unique way. Most systems focus on optimally supporting individual transactions (Online Transactional Processing, OLTP). Data warehouses, on the other hand, are optimised for query performance and typically support online analytical processing (OLAP) workloads.

The core difference between a data warehouse and a data lake is how the data is condensed. A data lake is also a central repository; however, it contains unstructured data in its native format. Big data, on the other hand, is an explorative approach to deriving new insights from large amounts of data. It is often the driving force behind adopting and implementing data warehouses and data lakes.

In the context of business intelligence (BI) evaluations, it’s not feasible to randomly generate or select the data needed for analysis because of the large number of data points involved. You would need to be able to calculate the average values of millions of potential data records in a fraction of a second. It, therefore, makes sense to summarise data at different levels of granularity (e.g., daily, monthly, quarterly) for reporting and analysis purposes.

Business intelligence can traditionally be divided into two groups of data that complement each other in their application: key performance indicators (or facts) and dimensions.

Key performance indicators (KPIs) are used for calculations. Not all numbers are KPIs. House numbers, for example, are generally not KPIs. Customer numbers also do not fall under KPIs. They are used for aggregations or other calculations.

Dimensions describe key performance indicators. They include all non-calculated values, such as names, addresses, or other business-relevant objects.

The most important data modelling technique in business intelligence is the star schema (star for short). It consists of a central fact table surrounded by dimension tables arranged in a star-like fashion.

Dimension tables are primarily used to store master data that usually rarely or does not change. If dimensions are, in fact, modified, special archiving (historicisation) is required.

The fact table sits at the centre of the star schema and contains quantitative data (facts) that represent business transactions, events, or measurements. The table structure should remain as streamlined as possible, with a limited number of keys and data fields. However, the number of data records can grow quickly.

As mentioned above, online analytical processing (OLAP) performs complex multidimensional data analyses. It uses a data cube, the so-called OLAP cube, as the primary data structure.

The data cube is an abstract form for displaying facts and dimensions in a multidimensional space. It is used in data warehousing because the information in the star scheme is easiest to conceptualise in this way, making even complex, in-depth analyses intuitive.

Dimensions (e.g., time, product, geography) are displayed along each axis, and facts (e.g., sales revenue, profit) are included at the intersection points. Thus, the frontal view of one side of the cube is akin to a pivot table.

The data cube is suitable for better understanding specific data analyses (business intelligence). It allows users to perform drill-down, roll-up, slice, and dice operations to navigate and analyse data along different dimensions.

What pain points does a Data Warehouse solve?

Central data integration from distributed data silos

Modern companies generally rely on many IT systems to support their operational processes. Almost all of them have their own database. Comprehensive evaluations only make sense once all the data has been collected centrally and aggregated for comprehensive assessment. Data warehousing ensures these requirements are met.

Making data accessible for evaluation

Transactional data records alone can only answer simple questions such as “When was item 4711 delivered?”. They are not suitable for in-depth analyses, which need to evaluate large quantities of data records, like: “How has the ratio of first-time-right transactions changed between 2010 and 2022?”.

Data warehousing centrally collates all the data in an organisation without burdening the operating systems. Specific evaluations are also made considerably easier as the data is aggregated. Specialised subsets, i.e. data marts, can be created within the DWH to further simplify evaluations for end users. Data marts are designed to serve the needs of a specific business unit, department, or group within an organisation. They contain focused sets of data relevant to their intended users’ requirements, making it easier for them to access and analyse the information they need for decision-making and business operations.

Overloading internal systems

An organisation’s operational systems trigger, support, and log business operations or business processes. Each step is called a transaction, which is why operational systems are said to be transactionally organised.

BI analyses examining vast numbers of transactions for a specific query would place a heavy load on these transactional systems. However, as the DWH continually consolidates this information, queries can be carried out there, which avoids overloading the operational and transactional systems.

Criteria for selecting the right DWH for your company

Choosing a data warehouse (DWH) has a significant influence on your company’s data strategy. Various factors can play a part in identifying the right solution.

Scalability and performance

Scalability is a key concern. Your DWH should be able to grow with your company without impacting performance. Consider the amount of data and the complexity of the data to be processed. Some data warehouse solutions offer flexible scalability that allows you to adapt to changing requirements.

Cost structure

Think beyond the acquisition costs – remember to check the ongoing operating costs as well. Cloud-based data warehouses such as Amazon Redshift or Google BigQuery often have pay-as-you-go models that give you flexible control of the costs.

Data security and compliance

Make sure that the relevant security standards are met and that the tool complies with data protection legislation such as GDPR. Some providers focus on security features, special certifications or a European server.

Integration and compatibility

Check whether the DWH is compatible with your current data formats and applications and if it can be seamlessly integrated into your existing system. A data integration solution such as Lobster_data can help here.

User-friendliness and support

An intuitive interface and competent Technical Support team can significantly streamline the onboarding process. When choosing the right DWH for your business, look for onboarding and training resources and responsive and knowledgeable customer service.

Monitoring and analysis functions

A powerful DWH should offer advanced analytical capabilities. This includes data retrieval and reporting, as well as analytics tools for data mining, process mining, and predictive analytics.

Real-time data processing

Some companies need to be able to process data in real-time. Check whether the DWH supports real-time streaming and analytics for fast, data-driven decision-making.

Challenges when integrating a DWH into existing IT systems

Integrating a data warehouse into existing IT systems is a complex process that requires careful planning. One of the biggest challenges is ensuring data consistency. Different data sources and formats must be harmonised to create a uniform database. This often requires extensive data cleansing and transformation.

A high level of system stability is also essential. A DWH solution must interact seamlessly with existing applications and databases, so it is necessary to precisely define and test interfaces (APIs) and data flows. Poorly integrated systems can significantly impact performance, so scalability and efficient data processing are integral.

Lastly, do not forget data security and protection. The tool must be integrated according to the relevant data protection regulations, including securely transmitting and storing confidential information and implementing access controls.

Best practice for using and maintaining a data warehouse

To harness the full potential of a data warehouse, be sure to follow the below best practice tips:

  1. Ensure adequate data quality: having access to high-quality data is essential to any DWH strategy. Regular checks and cleansing are crucial to avoid inconsistencies and duplicates. To do this, automated tools that recognise and report data anomalies are used.
  2. Performance optimisation: performance depends heavily on the structure and indexing of the data. Regularly optimise the database indices and monitor query performance. Slow queries should be identified and optimised to increase efficiency.
  3. Scalability and flexibility: a scalable data warehouse adapts to growing data volumes and changing requirements. Periodically review whether the current infrastructure meets the needs of your organisation. Consider using cloud solutions to increase flexibility and scalability.
  4. Strengthen security concepts: data security is a key concern. Implement robust security protocols, regular backups, and a disaster recovery plan. Also, train your team to minimise human error.
  5. Documentation and compliance: comprehensive documentation facilitates maintenance, promotes understanding of data structures, and supports adherence to compliance regulations. Keep a complete record of changes, processes, and data sources.
  6. Regular audits and reviews: conduct audits regularly to verify compliance with best practices. Use this opportunity to evaluate and adapt the data architecture and strategies.
  7. Training and support: a skilled team is integral to a successful DWH. Provide regular training and support to ensure all users can use the system effectively.
  8. Preventative maintenance: don’t wait for problems to occur. Proactive maintenance helps identify and rectify potential vulnerabilities early on. Use monitoring tools that monitor system statuses and automatically raise the alarm.
  9. Integration and automation: a DWH must be seamlessly integrated within your existing system landscapes and rely on automated processes to be efficient. Make sure your data warehouse works effortlessly with other systems and that repetitive tasks are automated. Follow these best practices to ensure your data warehouse becomes an even more powerful and reliable resource for your company.

DWH integration and process optimisation from a single source

Companies use Lobster products for various use cases, such as integrating data within existing DWHs. The tools can also be used to enhance operational processes based on BI analyses without having to purchase a dedicated data warehouse system.

BI analyses with Lobster_pro

The Lobster_pro process automation software creates a star data structure using no-code configurations. The solution then populates it using the Lobster_pro database and third-party systems and exports the star for evaluation and visualisation. It is even possible to create several different stars to support various business areas and processes.

Lobster_pro can even generate an OLAP cube for queries and flexible analyses. Although this is no substitute for a “true” DWH, which precalculates and persists cubes, i.e., stores them in a database for handling much larger data volumes, the solution can still directly support a company’s operational applications and business processes.

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Filling a data warehouse with Lobster_data

Populating a DWH with data from a company’s operational systems (ERP, TMS, ECM, CRM, etc.) relies on interfaces between these systems and the DWH. Conveniently, Lobster_data is a data integration platform for setting up interfaces quickly and efficiently. The no-code solution is also able to monitor data transfers within the company reliably.

The Lobster ETL/ELT module is specifically designed to create DWH interfaces and support typical ETL processes with their characteristic requirements, such as high-volume transactions.

Visualising data from a data warehouse

If data from a DWH is used for operational processes, Lobster_pro is the ideal tool for setting up the corresponding web application. This means targeted analyses and visualisations can be shared with external partners who do not have direct access to the DWH.

Lobster_data, as a fully integrated system, is the ideal tool for providing the necessary protocols and data (formats), as Lobster_pro’s API engine, so to speak. Get in touch and arrange a no-obligation consultation or a personal demo today!

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