Nine tips on how to build an effective data governance model

Before you rely on analytics for all or part of your strategic decision-making, you must first implement appropriate processes. This ensures that data flows smoothly through all business departments and that its quality, accessibility, usability and security are maintained. Here are nine tips for building an effective data governance strategy.

1. check data stocks in the company

To get the most value from data, stakeholders need to know how to select, collect, store, and use it effectively. Take stock of all the data that exists in the organization and identify its various sources, such as management systems, websites, social networks, and marketing and advertising campaigns. Then define the friction points where there is a loss of value due to poor data quality. Pay particular attention to the following points:

• Volume: The amount of data has exploded in recent years. Determine the amount of information stored in your databases to determine your data management method.
• Diversity: Data can be complex and diverse, as well as structured or unstructured, and can come from a wide range of information systems. Capture it in multiple places, centralize it, and reconcile it to comprehensively map all information.
• Speed: Rely on powerful, flexible software that incorporates machine learning. Review your infrastructure to select the most efficient tools that meet your needs and build a solid technical foundation.
• Truthfulness: Explanation errors in forms, diversity of collection points, bot actions, malicious actions, human errors, and more compromise the data foundation. There may also be biases in the analysis. Therefore, perform a diagnosis of the quality and accuracy of your data.
• Value: The data you use must be perfectly aligned with your company's business and marketing goals and add value to both the brand and your customers. Unify the data and react quickly to be on the winning side.

2. introduce a uniform data governance strategy

All departments in the organization need to be involved in data use - from senior management to team leaders to operations and field teams. The entire workforce should understand the challenges and benefits of shared, high-quality data. Consider the following to engage the entire operation in this transition:

• One-on-one or group meetings with various departments to better understand the current data situation, identify organizational requirements, and address any data governance expectations.
• Workshops with the aim of jointly developing a holistic methodological framework for data governance implementation.
• Real-world use cases in which, with the support of a number of employees, a business problem is analyzed in connection with a specific data area. In the e-commerce sector, for example, it could be errors in product packaging dimensions that lead to logistical difficulties and purchase abandonment because the customer finds delivery costs too high.

Next, set strategic goals that apply to the entire company or individual business units. Then define all the organization's performance indicators so that everyone understands their role in the governance model.

3. select a suitable data governance model

When you start a data governance project, don't fall into the trap of trying to answer all the technical, organizational, and regulatory questions at once. You need time to get the first tangible results. Create an accurate, stakeholder-validated roadmap with intermediate goals to evaluate efforts and progress to date. Also, keep in mind that there are different data governance models. Choose the one that best fits your environment, needs, human and financial resources, and data maturity.

4. identification and selection of all data stakeholders

First, appoint a chief data officer (CDO) who is responsible for data governance across the enterprise. He or she approves and prioritizes projects, manages budgets, recruits personnel for the program, and ensures complete documentation. Ideally, the CDO should report directly to the CEO. If your company is smaller, you can assign this role to another executive at a comparable level. Then expand the project team by assembling a multidisciplinary group with the following profiles:

• Data owner: You oversee the data in a given area and monitor the processes to ensure the collection, security, and quality of the data. They determine how data is used to solve a particular problem. For example, the marketing manager may be the data owner of customer data, or the HR manager may be the data owner of internal employee information.
• Data controller: They are the data coordinators and administrators of the central data store. They are responsible for organizing and managing all data or a specific data unit and monitor compliance with policies and regulations. They record and correct data elements, prevent duplicates and check the quality of the databases.
• Data manager: This ensures the proper lifecycle of the data by authorizing and controlling access to the data, defining technical processes to ensure data integrity, and implementing controls to secure and archive the data and the changes made to it.

5. eliminate data silos

Once you have assembled your data governance project team, you can bring it together into a body that makes strategic decisions about implementation across the business. This body approves data policies and addresses all issues around data governance, security and quality. Also, hold regular meetings with the opportunity to provide feedback. Ideally, you should opt for horizontal governance by putting data at the center of your operations and business affairs. Based on this principle, you can, for example, accelerate the breakdown of silos between direct marketing, advertising and customer service, and unite CRM and media expertise and technologies within companies, brands and their agencies. Educate your employees about the benefits of collaboration and daily data sharing. Then, ensure that all data useful to the execution of projects is consolidated on a data management platform that ensures data reliability and linkage. It is important to make all teams aware of the existence of a centralized data repository. This creates a shared vision.

6. document project and resources

To successfully implement a data governance project, you need to establish standard processes and find a common language within the organization. To do this, provide your teams with a "data folder": it allows you to identify the data assets, their flows, their storage and their processing methods. In this way, you make the data accessible and understandable for all employees. The data folder consists of a business glossary with precise definitions of all terminologies related to the data in circulation. In addition, there is a model that shows the structure of the company's data and provides information about how it is stored. A data flow diagram should not be missing either. The data folder also contains a section on the format of the various types of data and provides information on their access and usage conditions.

7. ensure quality of data

Data drives most of your decisions, such as the type and timing of advertising or communication campaigns, segmenting audiences, fixing or adding features to a website or mobile app. You must be able to rely on the quality of the data. Because low-quality data can have serious consequences for your company, such as lower revenue, traffic blocked by adblockers, or overestimated conversions due to poor source attribution. To mitigate these risks, be vigilant at all stages of the data lifecycle - starting at the critical moment of data collection. Any change or update to the site or tracking poses a risk to the quality of the capture. Implement effective methods and tools to manage and document this process. First, make sure the tags in your tagging plans are implemented correctly. Check them regularly and completely, ideally with automated acceptance tests, as manual implementation not only costs a lot of time but also increases the risk of errors.

8. ensure conformity of data

At the latest since the implementation of the General Data Protection Regulation (GDPR), companies know how important it is to observe the protection of users' personal data on their various digital platforms. Violations not only threaten sanctions, it can also damage the brand image and lead to a loss of trust among customers. Therefore, on your websites and mobile apps, you should ensure that your visitors' consent is obtained properly, freely and in an informed manner. To this end, you need to choose a provider that has strict data management and fully complies with the law.

9. democratize internal data use

The democratization of data within a company is one of the elementary components of a data governance approach. This involves making available to employees all the information and resources they need to perform their tasks and create value. Some measures can help, such as defining the use cases for this data, as well as details of where the data is located and how it can be accessed. Appointing data officers to help users on a day-to-day basis also proves to be a good idea in practice. Next, set up a specific support program. For example, you can organize training sessions and internal workshops to guide users in the operational use of the tools and in the use of the data on specific topics. To encourage staff to use the data, the data team can also design dashboards to manage each activity. Author: Adrien Guenther is Director of Analytics at Piano at the Munich location, where he has been strategically advising companies in the DACH region on the planning and implementation of digital analytics for a decade. Prior to joining AT Internet (acquired by Piano in 2021), Guenther was head of business intelligence at an advertising agency. He also has experience in search engine optimization, search engine development, as well as digital asset development, websites and online apps.

This article originally appeared on m-q.ch - https://www.m-q.ch/de/neun-tipps-wie-man-ein-effektives-data-governance-modell-aufbaut/