Typical usage of a data warehouse

Data warehouses allow for data analysis, including data from a dozen or so years back. The collected data is arranged and archived in a central database which facilitates control over access to data, thereby ensuring the security of the stored data. As time goes by, IT needs arise as a natural result of the company’s development, which is why Data Warehouse end-users should maintain constant contact with BI specialists to verify current business needs.

There are several ways to analyze the collected data, depending on actual business needs, however the most common ones are:

  • periodical, standard fixed-structured reporting, with enabled filtering,
  • defining reports and queries by means of SQL or generating automated reports,
  • static analysis of trends and predictions,
  • multi-dimensional and interactive analytical processing (OLAP),
  • data mining to predict business and market trends,
  • business modelling to predict the results of business operations.

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Business Intelligence specialists from JCommerce have implemented numerous data warehouses, which is why we know how helpful it is for users to have ordered and archived data.

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Data Warehouse – OLAP

Online Analytical Processing (OLAP) is an approach which provides swift responses to multi-dimensional analytical (MDA) queries. It presents a multidimensional structure of the company. This technique constitutes a fast, consistent and interactive way to provide key information for the company.

OLAP tools enable users to interactively analyze data complexity from multiple perspectives. Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time, due to which the results are transparent and clear. Furthermore, OLAP tools allow for smooth navigation through database structures as OLAP functions enable a user to create cross-sections of particular dimensions and move them using pivot tables. The user also benefits from the possibility of creating queries, presentations and visualizing results in the form of diagrams and charts.

The following operations make it possible to provide the highest quality information:

  • Data mining – delivery of detailed data on the feature which is being tested, from general to specific.
  • Aggregation – analysis of data from a wider perspective.
  • Selection – selection of data features for analysis.
  • Ranking of data – lineup of attributes.
  • Arithmetic, statistical, and econometric operations as well as selection of value, sorting and generating indicators.

Data warehouse – data marts

data mart is the access layer of the data warehouse environment which is used to deliver data to the users. It is usually oriented to a specific business line or team, i.e. marketing or accounting.

The data should comply with the main warehouse, which is relevant as this makes the warehouse a trustworthy source for data marts. This is where ETL processes play a key role, since they allow for the integration of data from different sources, data transformation and loading it to the due database.

Separation of areas in the warehouse lets users:

  • speed up the operations without placing an additional burden on the main data warehouse,
  • analyze data from different sources,
  • store data in a more detailed way to facilitate and accelerate the work,
  • download the data in a more efficient manner,
  • comprehension of the structure of the data by enterprise users – they save time spent on understanding complex data structures.

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