BIConcepts module
“Data Warehouse”
The basis of all tools that support management in its decision-making processes.
A data warehouse is a topic-oriented, chronologized and persistent collection of data with numerous applications:
- Integration of data from differently structured and distributed databases in order to enable a global view of the source data and thus comprehensive evaluations
- Finding hidden relationships between data through data mining
- Fast and flexible availability of reports, statistics and key figures, for example to identify relationships between the market and the range of services
- Transparency over time on business processes, costs and use of resources
- Provision of information, for example for the creation of product catalogues
3 major advantages of the
BIConcepts data warehousing
One data source
Through the data warehouse (DWH) the part of the data acquisition is centralized and provided with a uniform set of rules. This creates a common data basis for all analyses and evaluations. This prevents the risk of different interpretations of key figures in the company.
Cost reduction
In the BIConcepts Data Warehouse, data structures are intelligently modelled. It thus forms the basis for covering different analytical applications. In the present and in the future. The choice of a stable and flexible architecture concept as well as a stringent naming are essential.
Knowledge networking
The DWH module from BIConcepts creates the best basis for generating new knowledge and making it available to various business units and processes. The data is brought together semantically correct from various sources and can thus be analysed across topics.
BIConcepts data warehouse concepts based on new technologies
In addition to the classic approach of a traditional DWH in your own company environment (on-premises), there are now numerous other options with which the various advantages of new technologies and infrastructure offers – such as cloud solutions – can be implemented according to the individual requirements of a company.
BIConcepts supports you in the selection of the most suitable architecture for your company and accompanies you in all further steps, such as the design of the layer concept and the data model as well as the conception and implementation of the interfaces.
Data warehouse – the options
Additional information
Why data warehouse and data lake complement each other perfectly
BIConcepts DWHs and data lakes represent important components of the data processing and evaluation infrastructure:
These are complementary approaches and by no means mutually exclusive alternatives:
Data lakes emerged out of the need to have comprehensive data stocks like big data and to use the raw, granularly structured and unstructured data for machine learning. Data lakes are therefore a technical solution, while DWHs are a business solution. In addition, data lakes allow a first inexpensive look at new data before a decision is made about which of these data will be transferred to DWH structures and also made available to business users.
Without a data lake, analysts need to know beforehand all of the questions they want to ask and answer. A date lake also supports data scientists in situations in which they are not yet familiar with the questions. This gives data scientists the opportunity to experiment with the data and to find unknown connections. With these creative and innovative approaches, companies get a verifiable competitive advantage.
New ways in data analysis: integration of AI
Databases have the property of growing continuously. Many companies have now recognized that data storage and data usage are important success factors. The level of efficiency in database use has developed enormously in recent years — the main reason for this is the use of “Artificial Intelligence“ (AI) and ”Machine Learning“ (ML).
The targeted use of these future technologies allows companies to switch from reactive action to a pro-active and strategic mode: The use of algorithms for routine tasks ensures the exact implementation and allows patterns to be recognized in advance. These algorithms support crisis management and take on tasks that humans would only take a long time to complete.
This is only possible if the focus is on an essential aspect of the data analysis: data quality. The greater the proportion of raw, unstructured data in the company, the more important it is to use AI and ML in the preparation and analysis of the data, as well as the know-how of the data scientists. As part of data management, it must be ensured that the structure of the data corresponds in terms of scope and quality and that it is also trustworthy and can be found systematically.
Which data warehouse deployment is right for you?
01 | On-premises
Provision of a DWH on-premises in your company or in your data center.
02 | Cloud environment
Use of Platform as a Service (PaaS) or Infrastructure as a Service (IaaS) environments. Optionally also available in a rental license model as Software as a Service (SaaS).
03 | Hybrid environment
The best of both worlds: the hybrid provision includes an on-premises DWH supplemented by cloud components.
We would be happy to advise you and look forward to it!