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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!