Tips For an Effective Data Governance Model

Do you want to realize the full potential of data in your business? Then there needs to be a way around a data governance model. It ensures seamless data flow between all parts of your business and information quality, availability, usability, and security.

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Before relying on analytics for all or part of your strategic decision-making, you must first put the right processes in place. Here are some actionable tips for developing an effective data governance model effectively.

Review Your Company’s Databases

To get the most out of data, stakeholders need to know how to select, collect, store and use it effectively. Consider all the data available in your company and identify the different sources, such as administrative systems, websites, social media, and marketing and advertising campaigns. Then identify the points of friction where poor data quality results in loss of value.

Implement A Consistent Data Governance Strategy

All parts of the company should be involved in the use of data, from senior management to team leaders and corporate and field teams. All employees must understand the challenges and benefits of sharing high-quality data.

Set strategic objectives for the whole company or individual business units. Also, define all corporate performance indicators, so everyone understands their role in the data governance model.

Choosing The Suitable Data Governance Model

When starting a data governance project, avoid falling into the trap of trying to answer all the technical, organizational, and legal questions at the same time. It takes time to achieve the first tangible results. Draw up a detailed roadmap with milestones to evaluate efforts and results.

Remember that different data governance frameworks exist. Choose the solution that best suits your environment, your needs, your human and financial resources, and the maturity of your data.

Choosing A Data Analyst

Appoint a data analyst responsible for data governance within your organization. They will approve and prioritize projects, manage the budget, recruit project staff and ensure complete documentation. Ideally, data analysts report directly to the Executive Director. If your company is smaller, you can assign this role to another manager on a comparable level.

Elimination Of Data Silos

Once the data governance project is ready, it can be put together, and strategic decisions can be made about implementing it in different business areas. This body approves the data governance policies and takes care of all data management, security, and quality issues. It also holds regular meetings where you can give feedback.

Ensure that the data relevant to the project is collected on a data management platform that ensures data reliability and connectivity. All team members must be aware of the existence of a central data warehouse. This creates a shared vision.

Project Documentation and Resources

To successfully implement a data governance project, standardized procedures must be established, and a common language must be found within the organization. Give your teams a data warehouse that allows them to define databases, storage, and processing methods. In this way, it makes data accessible and understandable to all employees.

The data warehouse consists of an enterprise glossary that contains a precise definition of all terms related to the data in circulation. A template also shows the structure of the company’s data and provides information on how the data is stored. A data flow diagram should also be provided. The data folder should include a section on the format of the different types of data and information on access and conditions of use.

Data Quality Assurance

Data underpins most decisions, such as the type and timing of advertising or communication campaigns, target audience segmentation, website or mobile app customization, or feature additions. Rely on the quality of the data. Quality data can have severe consequences for your business, such as lower revenue, traffic blocked by ad blockers, or overestimated conversions due to poor feed performance.

To mitigate these risks, you must be vigilant at every stage of the data lifecycle – from the critical point of collection. Any modification or update to the site or tracking poses a risk to the quality of the record. Implement effective methods and tools to monitor and document the process.

First, ensure that the labels in the labeling plans are applied correctly. Check them regularly and thoroughly, preferably with automated acceptance testing, as manual implementation is very time-consuming and increases the risk of errors.

Ensuring Data Consistency

Since the General Data Protection Regulation (GDPR) introduction, companies have been aware of the importance of protecting users’ data on different digital platforms. Not only can you face penalties in the event of a breach, but it can also damage your brand image and cause a loss of customer trust.

This is why you must ensure that visitors’ consent is correct, voluntary, and up-to-date on your websites and mobile apps. That’s why you must choose a service provider that handles data strictly and complies with all legal requirements.

Use Internal Data Efficiently

Democratizing data within your organization is a key element of your data governance approach. All information and resources are available to employees who need them to perform their tasks and create value. Several measures can help, such as defining the purpose of data, where it is stored, and how it is accessed. In practice, the appointment of data analysts to help users in their daily lives has proved to be a good idea.

Finally, a specific support program should be set up. For example, you can organize data governance training and internal workshops to familiarize users with the practical use of the tools and the use of data for specific topics. The team can create checklists for specific activities by encouraging staff to use the data.

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