The best Data Warehouse (DWH) software providers compared

Data Warehouse Definition: What is a data warehouse, what are data warehouse systems, and what are they needed for?

A data warehouse, literally translated and generally defined, is a data storage. This refers to a storage location for various types of information that typically arise in companies and can be used for different purposes and as a basis for decision-making, such as in product development or (online) marketing, for example. A data warehouse system generally includes such a storage location. Essentially, it forms a medium through which information can be brought into the warehouse or distributed out to other software (especially specific analytics tools) and, depending on the scope of the solution, includes various tools for data management within the warehouse. Therefore, data warehouse tools are often referred to as data management software or data management platforms. The main goal when using a data management tool is to centrally bring together all data relevant to a company and to analyze and evaluate them with regard to specific corporate concerns or measures that support them. Without such an application, efficient recording and effective use of the many data that come together today would practically be impossible. On the other hand, by using data "correctly" for their purposes - key word "big data" - or by deploying data management software, and in detail through the insights and data-based decisions to be achieved, companies can gain significant competitive advantages.

How Do Data Warehouse Systems Work?

The functional process of a data warehouse system can typically be grouped into four different areas:

• Source systems
• Data staging
• Data presentation
• Data access

Information flows from various source systems into the data warehouse. Users can merge data from their website, their app, and practically all other platforms they use. Data staging loads, extracts, structures, and transforms the data from the different systems. They thus arrive in the actual database of the data warehouse. The database, the so-called data presentation area, is practically a parallel storage platform to the source systems, which normally also secure and provide the information. It enables data access for applications and downstream systems. Access to the information is via various data access tools on different levels, the data marts. As a rule, the data warehouse uses relational databases, which can be read out using SQL queries. Particularly large amounts of data (Big Data Warehouse or Big Data Warehouse Solutions) are organized on OLAP databases. A data warehouse is usually regularly equipped with new data. It ensures that the information is processed and enables specific analyses (in real time) or also allows these to be carried out via third-party systems. The corresponding insights can in turn be used in a variety of ways.

What are the advantages and disadvantages of data management systems?

• Improved Business Intelligence (BI): Companies primarily use data warehouses to support their analysis and BI requirements. Data warehouses enable centralized data storage with fast and easy access, which in turn has a positive impact on BI implementation, thus allowing generally more effective analyses and better decision making. So data warehouse systems not only help to gain fast, accurate and relevant insights into data, but also support optimal BI structures as a whole.
• Increased Return-on-Investment (ROI): Companies can often save costs and still significantly increase their turnover through data-based decisions. The use of data warehouse solutions forms an important key. A data management tool also contributes to improving operational efficiency and productivity.
• Competitive advantages: The use of a data warehouse enables fast and easy access to data and saves a lot of time in deriving insights. This gives those in charge the opportunity to identify important business opportunities or even dangers perhaps before the competition by means of specific business data analyses and to act accordingly advantageously.
• Optimized operational workflows: The data in a data warehouse is usually processed or transformed and cleaned before it is available for further processing. This ensures that the data used is of good quality and the insights finally gained from it are reliable. In turn, such well-founded insights or decisions derived from them can greatly improve operational efficiency.

Companies really don't have disadvantages from using a data management tool - as long as they choose it suitably for their purposes and use it correctly. Nevertheless, appropriate software solutions can present a number of challenges.

• On-premise efforts: On-premise or on-site data warehouse solutions require the management and maintenance of hardware and software infrastructures in-house. Companies need special teams for the implementation and long-term smooth operation of these applications. With a data warehouse on cloud or data warehouse as a service, companies are relieved of these challenges.
• Data quality: The data in data warehouses comes from various sources within the company. Inconsistent information, such as duplicates and missing facts, should ideally be eliminated but can lead to errors. Poor or error-prone data quality can result in inaccurate reports, insights, and decisions. Therefore, those responsible should ensure that the source systems provide high-quality data and opt for a data management system with strong processing functions when choosing one.

How to choose the right data warehouse software or what to pay particular attention to?

Data and their maximally purposeful use for decisions at different operational levels are indeed becoming absolutely relevant for more and more companies today. In order to be able to assess at the very base whether a data warehouse is really a sensible investment, those responsible should first ask themselves the following questions:

• Should all business-critical data be stored centrally?
• Should data from the website, mobile applications, CRM systems, and other applications be analyzed together in one place?
• Is it desired or necessary to gain deeper and more comprehensive insights than individual analysis tools allow?
• Should multiple people or tools be granted simultaneous access to a large amount of data?

If just one of these questions is answered with "yes", it is indeed appropriate to give some thought to the purchase of a data management tool. Once it is clear that data management software is helpful or necessary for your own company, there are several important factors to consider when selecting it. Particular attention should be paid to the following aspects.

• Data types: What kind of data is to be stored in the data warehouse - is the targeted solution ideally set up for this?
• Scope and scaling: What (future) amount of data is to be stored - does the targeted system offer suitable (scalable) capacities?
• Performance: How fast must data be processed - can the software meet the requirements in specific contexts?
• Maintenance: How much effort can the operation of the data management program bring with it?
• Cost: What budget is available for the data warehouse (long-term)?
• Interfaces: How strongly must the data management system be connected with other important tools and/or services - are the appropriate interfaces available?

Those interested should remember that many of the factors listed directly influence each other and compromises may be necessary. For example, deciding to scale down can affect performance but is generally less expensive. Special attention must always be paid to the features that are necessary or in some way advantageous to meet the respective company requirements. Therefore, a list of typical features of data warehouse solutions follows that should always be considered at least.

• Connections to data sources: Data warehouses usually draw information from different sources, such as websites, apps, but also from spreadsheets, banking systems, or other software. With appropriate connections, users can retrieve exactly the data they want to use in the decision-making process - provided the right interfaces are offered.
• Segmentations: Data warehouses are usually divided into individual sections. These segmented storage locations are generally relevant for individual teams or departments. Data warehouse solutions allow those responsible to carry out free segmentations.
• Scaling: Scaling allows companies to expand the storage capacity and functionality of their virtual data warehouse if requirements change in terms of data volume and/or analysis details over time.
• Automation: While many tools allow administrators to control scaling manually, autoscaling features help to reduce the manual aspects. Automatic scaling of services and data as needed provides more convenience and, not least, functional reliability. In general, modern data warehouse automation tools can take over more and more tasks completely on their own.
• Shared use of data: Data sharing features offer new collaborative opportunities. Thus, users can work together on data at the base efficiently and without errors - because it is centralized.
• Data discovery: Search tools provide the ability to sift through large datasets to quickly find relevant information in a specific case.
• Data modeling: Data modeling tools help users structure and edit data in such a way that they can quickly and accurately gain insights. They also support the translation of raw data into a more readable format.
• Compliance: Compliance functions monitor data stocks and implement security policies in dealing with them. Such features have become particularly important following the enforcement of the GDPR.
• Data staging: Data staging areas are used for normalizing and structuring information. These transitional storage areas are used in extraction, transformation, and loading processes (ETL) where information is prepared and finally exported.
• Presentation tools: Once the data in the staging area has been cleaned and normalized, it is transferred to data marts to give users access. They can be exported at this time or linked to BI tools for further visualization and data analysis.
• Integration tools: Integration tools are used both in collecting data from the various sources and in outputting it after it has been normalized or modeled. These tools facilitate the use of data stored in a data warehouse.
• Data transformation: This function creates the ability to clean, deduplicate, validate, compress data, and more. Data transformation is required, for example, to convert the information into a format that can be used by BI tools.
• Real-time analytics: Real-time analytics functions provide information or insights immediately and always up to date and inform users as soon as something important changes. Hence, data sets and analytics do not need to be constantly updated manually.

What does modern data warehouse software cost - is there also data management software freeware?

The two main factors determining the price of a data management program are the environment in which the solution is hosted and, of course, its range of functions. Not to forget: There is indeed also data management software as freeware. However, these are usually temporarily limited trial versions, functionally heavily restricted tools that are of very limited use for business purposes, or open-source systems without often important additional services. So, back to the central cost factors: As with most enterprise technologies, interested parties can opt for on-premise software, which you buy or lease and maintain yourself, or for a data warehouse via the cloud. A cloud-data warehouse is provided online as software-as-a-service (SaaS). Data management solutions in the cloud operate without in-house hardware resources being created, without the need for ongoing in-house maintenance, and reduce the demand for specialists. All of this significantly adds to the costs of using an on-premise software, increasing costs far beyond the mere purchase price of the application. The use of the cloud practically means that companies are already saving money. Data warehouse systems in the cloud can be used from about 15 to 85 euros per terabyte per month, while on-site solutions quickly cost up to 1,000 euros per month. Nevertheless, there are some good reasons to opt for an on-premise system instead of a cloud-based data warehouse. In terms of speed and functional safety, on-site software is often ahead of cloud solutions. Data is immediately available - because it is in your own system and not on servers at multiple locations. The application will also run without an internet connection. In addition, companies have more control and flexibility with a local solution - especially if it is an open-source data warehouse. As for the functions as a cost aspect: Standard functions do not form big price differences between data warehouse providers. It is - as expected - mainly the more special features that make the difference. For example, data warehouse software with an integrated business intelligence solution may cost around 250 Euros per month instead of around 50 Euros.