Data & Integration Architecture
Manage data like assets and meet legal requirements more easily.
Data is like water. The world is covered in it but only 3% is currently usable.
Increasing compliance requirements and the growing importance of corporate data as an asset (data is the new gold) pose enormous challenges for management, business departments and IT organizations.
On the one hand, increasingly complex IT landscapes with heterogeneous systems and a growing number of interface technologies (API, streaming, middleware) are often not very transparent and thus frequently a gray area with regard to data processing. On the other hand, companies often lack the legal expertise to translate data protection laws into IT requirements.
In addition, increasingly complex analytics scenarios (e.g., real time analytics) require a more precise understanding of the definitive data sources to be tapped (leading systems or golden sources) as well as a high measurable data quality. However, in many heterogeneously grown IT landscapes, overlapping data sources are common, which leads to inconsistent data as well as that the data used for decision making does not describe the “real world” correctly.
Data & Integration Architecture
Data modeling as the basis
It is common, that the responsibility for data models, data quality and data integration is delegated to the IT department. Data is mistakenly viewed as technical constructs (e.g., databases). However, the responsibility for the content of data and its quality, i.e., timeliness, completeness, consistency, and relevance, lies equally with the business department and IT.
Accordingly, it requires a team that bridges the gap between business and IT and brings strong methodological and technical knowledge to the table. Data architects can be these bridge builders. Typically, data architects, together with EAM, have a good overview of the processes, applications, data, interfaces and technologies in the company and uses an EAM tool and/or a data catalog tool that covers the common use cases of data architecture management.
Data & Integration Architecture
Unsere Services
Together with you, we create a logical enterprise data model and map it to systems and physical data models. This is the only way to systematically make decisions on consolidating or defining leading systems.
We create transparency about the use of your data. Using the CRUD methodology (Create, Read, Update, Delete). This makes redundant data processing or the risk of inconsistent data visible and allows targeted measures to be taken.
Data protection requirements are becoming increasingly stringent, particularly as a result of GDPR/DSGVO, requiring a classification of data according to personal data and ideally a determination of the need for protection. Together with you, we develop a record of data processing activities and derive organizational and technical action plans. Well prepared, inquiries from customers or audits become child's play.
Estimates by well-known IT analysts assume that a large part of project budgets will be used for integration tasks in the future, on the one hand for process and data integration, but also for user and thing integration (IoT, telematics, sensors). Unfortunately, in many transformation projects integration still takes place on the side, potentially leading to redundant, technically not ideal or poorly documented solutions. Together with you, we develop reusable integration standards and patterns that are derived from the business requirements. In doing so, we are guided by widespread industry standards such as SAP ISA-M.
In addition to methodological consulting and architecture work within the scope of your projects, CTI Consulting also offers EAM tool consulting. This is how we ensure that methodology and transparency across the IT landscape go hand in hand.
Use Cases
Data & Integration Architecture
Advantages
- Establish a common language: Enterprise data models create a common cross-functional and enterprise-wide language and understanding.
- Enable access to data: Managed through a clear governance process, data should be made accessible to facilitate process and data integration.
- Improve data quality: Data quality is improved through clear master data definitions and structures across systems and countries.
- Creating transparency: Enterprise data models and data architecture management transparently shows use and flow of data across the enterprise.
- Strengthen compliance: Data architecture and models enable the tracking of compliance with laws and guidelines relating to data, as well as the derivation of organizational and technical measures.
- Complexity reduction: A standardized data and integration architecture based on guidelines sustainably lowers costs in operation and reduces the commitment of critical technical expertise.
- Time to market: A well-documented architecture and traceable standards enable reuse and overall shortened development times.
EAM-Tool Services
More information about EAM tool consulting:
Dr. Dietmar Gerlach
Head of IT Management Consulting
