How to apply Data Governance to Digital Marketing
Tags: Governance, Data Science, Digital Marketing, Technology

With the increase in data production and consumption in recent years, a product of what is now known as the Third Platform, the issue of Information Security has been growing continuously, especially with recently established government regulations, such as the GDPR (General Data Protection Regulation) in Europe and the LGPD in Brazil.

When data becomes a company’s most important asset, it is essential to take certain security measures. Adherence to data protection laws is indeed necessary, but it is not the only thing that we need to consider. With Data Governance there are many other pillars that are equally relevant.

When we analyze the evolution of Big Data and the 3 Vs (Volume, Variety and Speed), we see a paradigm shift in many pre-established concepts in Data Management. Industry, for example, has already evolved into Industry 4.0 and DataOps has been gaining prominence, especially in Marketing. These paradigm changes, together with the digitalization movement of large companies and the emergence of startups, are becoming ever more present. Investments in media and advertisements, the analysis of digital behavior and sentiment in interactions on social networks, and the personalization of content have all brought new needs. And so, new approaches to these topics are necessary.

One term that has been gaining traction recently is Data Enablement. Essentially this is just consuming data in an agile and dynamic way, quickly generating added value (Data Management Platforms, or DMPs, are an example of the need to create and store audiences and instantly apply them to a business). Another term, one that hasn’t been mentioned much yet, is Data Fabric. This is a structure of services and architecture that allows an overview of all data (either on-premise or in the cloud). It optimizes processes and allows faster evolution of the organization’s maturity (still embryonic and perhaps utopian due to the reality of continuous technological advances, such as IoT).

And so, the idea of this post is to emphasize the importance of Data Governance for Digital Marketing and help the market to adapt to new needs, educate professionals about risk mapping, increasing revenue and reducing expenses. Data Quality, a theme that has been gaining momentum for the solutions of MarTech, is one of the pillars of Governance and will be addressed in other publications soon.

What is Data Governance?

“Data governance is the specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of data and information. It includes the processes, roles, standards and metrics that ensure the effective and efficient use of data and information in enabling an organization to achieve its goals.” — Oracle

In other words, we can understand Data Governance as the management and structuring of all people, processes and technologies that involve the creation, reuse, consumption and deletion of data within an organization.

Why should we be concerned with Data Governance in Marketing?

Unfortunately, the leaking of confidential information is common these days. We generally think that this type of error is inherently an IT problem, which sometimes leads us to make some common mistakes, such as not performing proper access management to analysis tools or sharing data sheets with third parties without proper encryption.

Another common scenario is the lack of reliability of data because there isn’t enough knowledge or documentation about the collection. Often, the source of the data, and whether it is correct or not, is barely known. Such situations can lead to incorrect data analysis, which can result in unnecessary costs, misdirection of resources and poor understanding of the customer journey.

Data Governance guarantees the security of information, helping to improve its reliability, quality and protection against possible exclusions or accidental changes. In addition, it can directly impact revenue growth, risk reduction and improved results.

Dimensions or Disciplines

According to the framework proposed by DAMA International (a global data management community), we can divide Governance into areas or functions that serve as guidelines for its application in a company. These are:

Data Architecture Management — In summary, this is the whole process that involves data collection (ingestion of data in analytics tools, datalakes, datawarehouses etc.), the path that the data takes, the processing and visualization of the data and the technologies that are used throughout the process. It is important to document the architecture of the collection, from its origin to the final destination of the data (considering the life cycle of the information, from collection, maintenance and storage to disposal). You should also have a design of the Martech tools used in the flow, to know how the databases from the collection are structured and mapped.

Data Development — This involves creating and analyzing a project and implementing data solutions in order to maximize the value of resources. In marketing we can define it as everything that involves the architecture of navigation data (modeling what will be collected, such as the creation of collection maps), media pixels, integration with CRM and audience to enable and facilitate consumption, serving different business areas. It can also encompass the definition of a Cloud platform with a hub of connections and APIs for executing ETL processes.

Database Operation Management — This is everything that involves the database, so we must define how the data ingestion and storage will work, thinking in terms of the structure of the bank that will receive it, and also have plans for backup and recovery. We can also mention the optimization of database performance (Tuning) and policies for purge and data retention. This dimension is a little more technical and may seem a little distant from Marketing, especially in organizations that use Cloud, but when we talk about Data Lake and Data Warehouse it is important to raise these concerns.

Data Security Management — This is the most well known dimension and the one that people are most concerned about today. It is about planning and executing a structure that guarantees data privacy and security. This is where GDPR and LGPD policies come in, and where we should be concerned with access to tools and databases. In addition, the encryption of sensitive data, logging and tracking of logs, control of consumption requirements and data sharing should be considered.

Reference & Master Data Management — This is the definition of a data source structure that works as a reference of the truth. In other words, it is a version of the most relevant data, that is reliable and consistent and that generally forms a single origin of the data. For example, we can use the definition of the source of media data (Should you consider the numbers from Facebook Ads or Google Analytics? Which tool should be considered the source of the traffic?), the source of downloads of an application (Should you consider the numbers from AppsFlyer or Firebase?) or even revenue and transaction data (Should you consider data from analysis tools or internal data from transactional databases?). These definitions are important in order to have consistency in the reported numbers.

Data Warehouse and Business Intelligence Management — As the name implies, this dimension consists of the “final” environment where we analyze and obtain the results for visualizing data. Therefore, it is important that the data is ready to be used for making decisions. The main consideration is the historical data and how it is being modeled for use with the current data, to allow the manipulation and generation of reports for business analysis.

Document and Content Management — This is the process of planning, implementing and controlling activities to store, protect and access a company’s unstructured and semi-structured data. It can also be in a database, such as CMS data, documents, videos, images etc. In the Digital Marketing scenario it can be applied to HTML standardization, structuring of the Data Layer, URL parameterization, configuration of tools and documents of measurement models, in order to facilitate the structuring and collection of data that is intrinsic to user content and navigation.

Meta-data Management — Metadata is the information that complements and describes other data. Therefore, we can understand this dimension as the structuring of what the data is. This includes data dictionaries, especially in relation to data integration and the creation of bases. It is important to have some documentation to explain it (expected volume and timing, origins and destinations, relational schema etc.). This is essential in order to allow authorized people to access and manipulate the data.

Data Quality Management — This involves promoting, measuring, evaluating, improving and guaranteeing the quality of the company’s data, in order to meet the needs of different consumers. In Digital Marketing, it is linked to the validation and monitoring of the quality of navigation, media and audience data throughout the life cycle of the data. It seeks to evaluate the processes and optimize the steps to reduce costs and increase productivity, with the quality of data reflected in the quality of products and services. Here we can talk about collection monitoring, with defined metrics that measure the quality of the data, and also good practices for the collection and validation of the data, as well as an action or contingency plan to regularize situations highlighted by the monitoring.

Conclusion

There is still a lot to be discussed in relation to Data Governance for the Digital Marketing area. Here we have just given an overview and some examples of how the dimensions of governance could be applied to Marketing. It is important that at least some guidelines are incorporated into the day-to-day operations of the organization and remain in constant evolution and adaptation, always aiming for greater reliability and security of data.

It is also worth mentioning that there is no “finish line” in the context of governance: each company must interpret it and adapt it to their needs. The most important thing is to keep the topic on the agenda, seeking new solutions for an organization’s current scenario.

As this is a very comprehensive subject, future posts will bring a more in-depth view of the dimensions listed above, suggesting good practices and giving more examples of their use as applied to Marketing. Stay tuned to our blog for more news!

Until next time!