Data Governance

And suddenly everyone pulls together.

Setting up and establishing the rules and guidelines for data use in companies is an immensely important, but often not prioritized step. Data Governance Module from The Data Institute enables the company to roll out the processes and workflows designed and formalized in the module across the company.

The starting point for this is the Data Governance Institute (DGI for short) framework, which is used by large, multinational corporations as well as medium-sized companies.

Our process

Analysis

Analysis of existing data governance measures and any documentation
Evaluation of the effectiveness, completeness, and comprehensibility of current frameworks
Identification of specific priorities regarding data governance such as data quality, compliance, processes, structure, etc.

‍

Data Governance Framework

Development of a phased implementation plan for data governance measures in the selected focal areas
Development and implementation of dedicated roles and responsibilities as well as from access and authorization concepts
Development of standards for example, for creating dashboards and processes as well as for data quality management

‍

Transformation

Step-by-step implementation of data governance measures through accompanying implementation or implementation of use cases, tools, roles or similar initiatives
Ongoing support when implementing the recommended measures and initiatives and their continuous development
Escort further initiatives such as KPI alignment, data quality checks, etc.

‍

Why do companies need data governance?

Identifying offensive aspects of data governance

Aspects of data governance include democratizing data, promoting efficiency through data use, or developing data-based business models. It is about the positive aspects and the company's future-oriented measures, made possible by governance. Aspects suitable for the company are worked out together.

Identifying defensive aspects of data governance

Defensive aspects of data governance are measures aimed at preventing unwanted events. These may include fines, compensation claims, refunds or other negative consequences (within or outside the company) resulting from failure to comply with or prioritize compliance, ethics and data protection.

Guidelines for data usage throughout the data life cycle

Certain guidelines are required to determine the use of data along the data life cycle and to enable all employees to use data and work with it successfully. This includes defining roles and access rights as well as designing specific processes and workflows. These are defined by way of example.

Rough concept and enablement of the organization

The data governance module enables the employees and specialist departments involved to roll out the roles, processes and guidelines defined in the module to the entire organization. The developed rough concept helps to make data governance available, understandable and visible to all other employees and departments as well.

This is how we go about establishing data governance

From assigning roles to connecting systems to enabling all employees, we are a strong partner at the side of our customers.

Phase 1

Assign

The first step of the data governance module is to define and assign the various data governance roles (data owner, data steward, data consumer and system owner). These are neither fixed nor final, but exemplary. This allocation enables the organization to independently assign all other employees and stakeholders to these roles later on. This definition also includes the board structure.

Phase 2

Specify

In the next step, the most relevant processes for data governance are identified and designed on the basis of the Data Governance Institute (DGI) framework. This includes, for example, the introduction of new systems, the connection of data sources, the processing of new data, the distribution of data, archiving to optimize costs and performance, and deletion due to the retention policy.

Phase 3

Splitting

The workflows and processes previously identified and suited to the company are now divided between the various data governance roles on the basis of RACI matrices. This is done clearly in task packages and visualized in such a way that new processes can be worked out based on these requirements even after the module has been completed. This creates a rough data governance concept.

Phase 4

Stimulate

The resulting rough concert is now formulated in comprehensive guidelines and scheduled, both in terms of roles and organization, processes and workflows, but also categorization and access rights. This is the basis for enabling stakeholders up to the target model.

Phase 5

Enable

The aim of the data governance module is for participants to be able to continue data governance within the company and roll it out across the company. For this reason, the roles are only defined as examples and only work in one department of the company. In the last step of the module, the necessary next steps are derived to achieve this goal.

Grafik des Frameworks mit dem Data Institute arbeitet.

Data Governance in our framework

We always work with the organization, culture and architecture framework.

Because in our opinion, these 3 areas are the most important factors for successfully anchoring data in the company in the long term.

Data governance is also about these three areas. Establishing guidelines for the use of data is immensely important in order to build the organization around data and to create a smooth and unproblematic architecture.

And also for the culture data governance is an important factor. This is because clear regulations and open systems help employees use data and engage with their dashboards.

How does our framework work?

The Data Institute — the strong partner in the use of data governance

We want companies to quickly get an impact and see what they can do with data. At the same time, we have an overall view of the company and would like to take a long-term view Strategies Implement, which enables employees to work with data independently.

Learn more about us

What is data governance anyway?

Data governance is also jokingly referred to as “police” in the data scene. The analogy isn't entirely wrong, but it doesn't describe enough of what data governance means. Because this area is not just about checking whether guidelines are being complied with, but also about setting these guidelines.

It also involves processes, roles, standards and key figures — all with the aim of using data effectively and thus driving the achievement of corporate goals.

In effective data governance, the activities are centred on the Data Governance Board, which includes the roles of data owner, data steward, data consumer and system owner. Processes related to data use are then precisely defined so that all responsible parties and data users can comply with them.

The goal of data governance is therefore transparent and secure data monitoring throughout the organization.

Data governance focuses on data protection and compliance with relevant legal requirements (e.g. the GDPR), sustainability, but also general compliance issues, such as retention periods. The goal of data governance is therefore to comply with laws, guidelines and regulations in the company — by introducing processes and regulations that make it easy for all affected parties to comply with them.

Compliance and implementation of these regulations results in both a reduction of risk and an increase in efficiency and effectiveness at strategic, tactical and operational levels.

Who needs data governance?

All companies must comply with regulations that apply to companies from “outside” (e.g. political). Data governance, and thus the implementation of these regulations through appropriate roles and processes, is therefore also relevant for all companies. But for a certain size, it makes sense to establish their own data governance department and/or seek strategic support to implement this issue internally.

The effective use of data governance can ultimately ensure that business goals are achieved faster!

Data governance is also essential when migrating content to the cloud.

The services provided by The Data Institute are aimed primarily at companies from the following industries media, logistics, and finances, but also on eCommerce Companies that want support in complying with guidelines and using data effectively.

Who is involved in data governance?

Data governance fundamentally affects all people who are connected to data — because they are the data consumers. When developing role and process concepts, The Data Institute refers to the Chief Data Officer or Head of Data, the Chief Information Officer, the Data Protection Officer and the Compliance Officer.

Through the Collaboration, the Roles of Data Owner, Data Stewards, Data Consumer and System Owner are established, which are then rolled out to the entire company.

The persons concerned are worked with both in various 1:1 meetings and in joint workshop sessions to ensure that the various stakeholders align.