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 suitable 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 maximize the benefits of data, stakeholders need to know how to select, collect, store and use it effectively. Take stock of all the data available in the company and identify its various sources, such as administrative systems, websites, social networks and marketing and advertising campaigns. Then define the points of friction 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, you should not fall into the trap of answering all technical, organizational and regulatory questions at the same time. You need time for the first tangible results. Create a detailed roadmap with milestones that has been approved by stakeholders to evaluate efforts and progress to date. Also keep in mind that there are different data governance models. Choose the one that best suits your environment, your needs, your human and financial resources and your level of data maturity.

4. identification and selection of all data stakeholders

First appoint a Chief Data Officer (CDO) who is responsible for data governance throughout the company. He or she approves and prioritizes projects, manages budgets, recruits staff 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 manager 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 put together your data governance project team, you can bring it together in a committee that makes strategic decisions about implementation in the various business areas. This committee approves data policies and deals with all issues relating to data management, 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 breaking down 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 on the benefits of collaboration and daily data sharing. Then ensure that all data useful for the execution of the 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 set up standard processes and find a common language within the organization. Provide your teams with a "data folder" for this purpose: it allows you to identify the data sets, their flows, their storage and their processing methods. This makes the data accessible and understandable for all employees. The data folder consists of a business glossary with precise definitions of all terminology relating to the data in circulation. There is also a model that shows the structure of the company data and provides information on how it is stored. A data flow diagram is also included. The data folder also contains a section on the format of the various data types and provides information on their access and usage conditions.

7. ensure quality of data

Data controls most of your decisions, for example the type and timing of advertising measures or communication campaigns, the segmentation of target groups, the correction or addition of functions on a website or mobile application. You must be able to rely on the quality of the data. Poor quality data can have serious consequences for your company, such as lower revenue, traffic blocked by adblockers or overestimated conversions due to inadequate source attribution. To reduce these risks, you should be vigilant at all stages of the data lifecycle - starting at the critical moment of data collection. Any change or update to the website or tracking poses a risk to the quality of collection. Implement effective methods and tools to control and document this process. First of all, make sure that 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

Ever since the implementation of the General Data Protection Regulation (GDPR), companies have been aware of how important it is to protect the personal data of users on their various digital platforms. Violations not only result in sanctions, but can also damage the brand image and lead to a loss of trust among customers. You should therefore ensure on your websites and mobile applications that the consent of your visitors is obtained properly, freely and in an informed manner. To this end, you must choose a provider that has a strict data management policy 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 all information and resources available to employees that are required to fulfill their tasks and create value. Some measures can help with this, such as defining the use cases for this data and specifying where the data is located and how it can be accessed. Appointing data officers to help users on a day-to-day basis has also proven to be a good idea in practice. Next, you should set up a specific support program. For example, you can organize training courses and internal workshops to guide users in the operational use of the tools and in the use of data on specific topics. To encourage employees to use the data, the data team can also design dashboards for managing individual activities. 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/