Data Quality Management
We offer comprehensive solutions for Data Quality Management that keep your corporate data at the highest level. With our expertise, we ensure that your data serves as a reliable foundation for informed decision-making. Our services include tailored solutions that safeguard and continuously improve data quality—from implementing efficient processes to automating your DQM workflows.
Why Data Quality Is the Key to Business Success
In today’s data-driven business world, the quality of your data is crucial for success. With the steady rise in data volumes and the increasing importance of Artificial Intelligence (AI) as a business factor, having valid and reliable data is becoming more essential than ever. Faulty or incomplete data can not only lead to poor decisions but also significantly impair the effectiveness of AI systems. Data Quality Management (DQM) encompasses all measures to ensure data quality—from identifying and correcting errors to standardization, and continuous monitoring and maintenance of your data. With a well-thought-out DQM strategy, you not only safeguard the integrity of your data but also create a solid foundation for all future business processes and decisions.
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Recognize the opportunities of a well-designed DQM strategy and lay the foundation for informed decisions and efficient business processes.
Our Tailored Solutions for Your Data Quality
Our DQM services are tailored to the specific needs of your company:
Process Optimization
We analyze and enhance your existing data processes to ensure that data is captured and processed accurately and consistently.
Data Monitoring and Control
We provide continuous monitoring of your data quality to ensure that your data consistently meets the highest standards.
Automation of DQM Processes
We integrate automation solutions to make your data maintenance more efficient and error-free.
Advanced Tools for Seamless Data Quality
Our implementations are based on advanced tools and technologies that ensure the highest data quality. The preferred tool for this is Q-THOR, which offers comprehensive features for automated data validation, real-time analysis, and seamless integration into existing systems. With Q-THOR, we can continuously monitor your data quality, automate ticket creation, send results via email, and efficiently integrate analytics tools. Additionally, we utilize proven SAP solutions for data management and integration that ensure smooth integration into your IT landscape.
We understand the strategic drivers for Data Governance programs and know that a successful Data Governance process starts with the business units and IT. We have geared our organisation to this: our business analysts and data/application architects take the requirements, conceptualise them into a target architecture and implement the requirements using the latest standards. This ensures an end-to-end process from the creation of a Data Governance framework to the quality assurance of the data.
Initialisierung: Trotz unterschiedlicher strategischer Treiber gestaltet sich die Initialisierung eines unternehmensweiten und bereichsübergreifenden Data Governance Programms oftmals als herausfordernd und bedarf der Unterstützung des Managements.
Anchoring: Data Governance means change. Data and its benefits are brought into focus and applied across all areas of Data Management. This goes hand in hand with corresponding guidelines and new powers of direction, which can meet with resistance.
Transparency: Establishing the necessary data transparency for company data is often a complex process. The knowledge about the data is distributed in the company in various systems, processing procedures and in the heads of the employees.
Data Quality: The quality of the data is often not transparent and traceable. This means that the necessary reliability of the data for decision-making is not given, so that decisions are made on the basis of gut instinct.
Our Project Approach
Business Analysis
Our business analyst team speaks the language of the business units and understands the meaning of the data, the flow of the processes and the structure of the organisation.
Architecture
Our architects are experts in building system and data architectures in the SAP Business Intelligence world and advise our clients' IT on strategic IT issues. Together with our business analysts, you develop the functional and technical concept for the target architecture and hand over the results into the hands of our implementation experts.
Data Provisioning & Integration
The implementation is holistic, starting with the collection and consolidation of the data by our Data Provisioning & Integration team, which has years of experience in the SAP Data Provisioning Tools SDI, SDA and SLT.
SAP Analytics
Our SAP BW experts model the data in the clients' SAP BW system and ensure an optimised update of the data so that it is made available by our SAP Analytics team in dashboards and reports in the SAP Analytics Cloud (SAC) according to the clients' requirements.
Application Development
Experience shows that clients have additional requirements, such as interfaces to other systems. These are implemented by our developers in the Application Development team according to requirements.
Data Quality
Data Quality is the focus of all data-driven companies. For this reason, we recommend that our clients set up a data quality layer, which all data must pass through in their processing, as early as the conception of the target architecture. The core component of this layer is our DQ product Q-THOR, which sustainably ensures data quality through real-time quality checks.
A Data Governance program focuses on data and its value for the company, so organisational, process and system changes must result from this.
Automated Data Validation – Enhanced Efficiency for Your Business
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BIG.Cube Data Governance Framework
Companies are often not completely at the beginning in terms of Data Governance. In the different departments of the company, activities have already been established that can be assigned to Data Governance. The problem here is often the lack of transparency and the lack of overarching coordination of these activities.
The Goal in Sight
Accordingly, the aim of Data Governance programs is to create an overarching and transparent structure to optimise Data Management in the company. Derived from this goal, a large number of frameworks have emerged on the market that set different focal points in relation to the fields of action of Data Governance. The BIG.Cube follows the framework described above when advising and implementing Data Governance programs for our clients.