Agility with data warehouse automation
By Data Warehouse (abbreviated "DWH", meanwhile also the German term "Datenlager" is established) one understands a central database system, which is optimized for analysis purposes. The data comes from external, mostly heterogeneous sources and is merged in the data warehousing process. The data collection is usually topic-related, whereby the data is permanently stored in structured relational and denormalized databases.
An essential feature of DWH is the historization of data records - these change continuously, whereby existing data is not constantly overwritten, but stored in versioned form. This makes it possible to compare data within different time periods (example: quarterly sales figures).
The objectives of data warehousing
Data warehouse systems were introduced to control and manage business processes across all company departments. Customer-relevant information is the backbone of the value chain and, in some circumstances, it can result in the generation of products. Available customer data is used to obtain information about both customers and the market, and to generate actions such as the development of a product, a service or a marketing campaign.
The introduction of SQL-based databases and digital data storage made it possible to organize data into one or more tables and establish logical connections between those tables. These relationships make the targeted evaluation of ever-growing data volumes possible. That’s what DWH is all about.
Without data warehousing a comprehensive analysis of data would be impossible because evaluations would be restricted to individual data sources.
The everyday applications are varied. Hidden associations are easily revealed, and statistics and reports can be quickly produced in all imaginable formats. Energy supply companies use data warehouses as repositories for consumption data and billing data. Based on that data, they can implement target group-oriented analyses and evaluations by region, household type or age group.
How to modernize your data warehouse with automation
DWH in its original form uses methods and tools such as SQL database systems or Oracle real application clusters. Classic manual database modelling can quickly involve a substantial number of developers because of the very scale and data volume involved in modern-day projects. In many cases, software development problems will additionally slow down data warehouse projects.
Automation and an agile data warehouse are the solution. Efficiency-enhancing methods are used to make all integrated DWH processes more effective.
Our biGenius® tool is an innovation enabling the fast and simple development of data warehouse solutions. biGenius® assists in various scenarios, from business requirements engineering to the development of all the necessary sub-components. It has integrated data quality assurance functions and facilitates the continuous optimization of data load flow control.
«I’m convinced that the tool reduces data warehouse development costs by at least 50%.»
Mag. Markus Tanzer, Head of IT, Austria Tabak GmbH/JTI
- Very short time-to-market
- High time and cost savings
- High quality through standardization
- Minimal implementation risks
- Fast implementation
- Best practices and standards
How to get the most out of your DWH with analytical data management
Data warehouse solutions have to offer increasing flexibility and adaptability while remaining cost-effective in their implementation and operation. This represents a challenge that is too difficult to master with traditional approaches and methods. There are also growing requirements of combining unstructured or semi-structured data with the classic data from the data warehouse in order to maximize the benefits of data from both internal and external sources.
The continuous addition of data to analytical databases makes it increasingly challenging to ensure a satisfactory level of data quality. We have developed solutions for all the pressing challenges in the field of analytical data management. They are based on the extensive experience we have gained in numerous implementation projects, on the one hand, and on innovative architectures developed using big data technologies, on the other.
- Agile data warehousing and DWH automation
- Hybrid architectures and unified analysis
- Data quality and business intelligence test management
How biGenius can simplify your objectives and help you achieve them faster
The Trivadis Business Intelligence (Bl) Service Framework biGeniusTM is the connecting link between requirements and the productive BI solution. biGeniusTM is both a method and a tool, providing seamless support in all phases of BI projects, from requirements analysis to technical implementation and operation.
- Fast implementation
- High potential cost savings
- Best practices and standards
«Using the new BI solution, we were able to cut down our annual operating and development costs by more than 50%.»
Stephan Ischer, SAP system manager at HACO AG
Is your data quality up to scratch?
Data quality and test management delivers answers
The quality of the data in the data warehouse is the decisive factor governing user acceptance of that data. If any justified mistrust arises in just one area due to the inferior quality of the data, this will quickly put a question mark over the data warehouse solution as a whole. It is therefore absolutely essential to establish the appropriate processes and procedures for permanently ensuring high data quality. We help you to achieve this goal with solutions for not only automating data quality routines during ongoing operation, but also for development and change management scenarios.
Our automated test routines are suitable for development and operations testing and our test management delivers effective test management and monitoring, and test data generation.
Consistent quality at every level
Our Trivadis BI Service Framework biGeniusTM incorporates components ensure consistent data quality assurance at all levels of your business intelligence and data warehouse solution. Development and operation processes are automated to attain an appropriate degree of efficiency.
- Ongoing data quality assurance
- Low costs through automation
- Consistency across all levels
For IT experts:
Get the best of both worlds with hybrid DWH and big data solutions
When it comes to scalability and flexibility, the capabilities of Hadoop, NoSQL and Co. are already far superior to those of relational and multidimensional databases. But this alone does not by any means qualify them as a substitute for the traditional data warehouse world. Instead, the aim is to achieve the most seamless interaction possible and thus derive benefits from both systems.
In hybrid architectures, roles and responsibilities are shared between the available technologies as effectively as possible according to technical suitability and cost considerations. Appropriate connectors and data integration tools are used to link the different systems and share the data.
We can help you to achieve both structure and flexibility
Colossal amounts of fine-grained data can be stored on Hadoop, whereas less detailed and professionally processed information is kept in relational and multidimensional data warehouses. Both data sources are then used together as required. Correspondingly, highly structured data can be offered in relational databases for maximum user convenience, while data scientists run their analysis platform on Hadoop and NoSQL for reasons of flexibility. This scenario is a good way for all kinds of enterprises to embrace new technologies. We’re your implementation partner!
- Flexibility through big data technology
- Investment security for your established DWH solution
- Launch pad for new analytical possibilities