How Data Governance and Data Management are Interdependent

The amount and importance of data for companies is growing day by day. However, the mere collection and storage of data is not enough to secure competitive advantages. In order to achieve added value from the data, it is necessary to control the data by means of Data Governance and Data Management – from the process of creation to the provision of a reliable basis for decision-making. Many companies are therefore currently faced with the task of building up this data competence.

Data Gover­nance as a Central Guideline

Data Governance defines cross-organisational roles and responsibilities and associated decision rights and guidelines for quality-orientated control of Data Management. This ensures that Data Governance is anchored in the concrete business processes.

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.

The Challenges

The implementation of a Data Governance program usually involves a realignment. In order to optimise Data Management across the company, the data is placed in the centre. In addition, a Data Governance organisation will be established and given authority to issue directives. Accordingly, we repeatedly encounter the following main challenges with our clients:

Initialisation: Despite different strategic drivers, the initialisation of a company-wide and cross-divisional Data Governance program is often challenging and requires management support.
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: Building up the necessary data transparency about the company data is often costly. The knowledge about the data is distributed in the company in various systems, processing procedures and in the heads of the employees.
Data Quality
Data Quali­ty: The quality of the data is often not transparent and comprehensible. 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.

Data Gover­nance – It's Nothing without Data Quality

We implement Data Governance strategies, concepts and processes in your SAP system landscape in such a way that the quality of your data is permanently ensured by means of IT automation.

Data Governance in SAP Analytics

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.

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.


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.

Premium Consultancy for Data Governance & Data Manage­ment

From many client projects, we know the strategic drivers for Data Governance programs. We have also advised on setting up the Data Governance organisation and are at home in the disciplines of Data Management. Furthermore, we have developed our own product, Q-THOR, to ensure data quality in companies.

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