Data Intelligence

Understanding the Different Data Cultures

Actian Corporation

July 16, 2019

data cultures

Just like corporate or organizational culture, each enterprise that deals with data has its own data culture. We believe that what distinguishes Web Giants isn’t the structure of their governance, but the culture that irrigates and animates this organization.

At Zeenea, we believe in putting in place a Data Democracy. It refers to corporate culture, an open model where freedom rhymes with responsibility.

To better understand Data Democracy, it is necessary to compare it to other data cultures. Here are the main data cultures:

Data Anarchy

In this system, operational professions feel poorly served by their IT departments, and each one develops its clandestine base (shadow IT) which serves their immediate interests while freeing them from all control regulations and conformity to standards. In 2019, this culture brings sizeable risks: data leaks, contravention of ethical regulations, service quality degradation, reinforcement of silos, etc.

Data Monarchy

This system translates to a very strong asymmetry in data access depending on the hierarchical position. Data, here, is very strictly controlled; its consolidation level is carefully aligned with the organizational structure, and its distribution is very selective.

This monarchical culture prevailed for a long time in Business Intelligence (BI) projects: data collected in data warehouses were carefully controlled, then consolidated in reports where access was reserved to a few select people who were close to decision-making bodies. This method promotes a “top-down” approach and willingly encourages a defensive strategy, where rules, restrictions, and regulations insulate data. Its main theoretical benefit is the almost infallible control over corporate data, but that translates into very limited access to data, only reserved to certain privileged groups.

Data Aristocracy

A Data Aristocracy is characterized by a more significant degree of freedom than in Data Monarchy, but which is solely reserved to a very select subset of the population, mainly expert profiles such as Data Engineers, Data Analysts, Data Scientists, etc. This aristocratic approach is often the one that brings the most successful data governance projects to the surface.

Such a culture can be favorable to more offensive strategies, as well as to heterogeneous one, combining top-down and bottom-up. However, it deprives the majority of employees access to data and thus, a certain number of possible innovations and valorizations.

Data Democracy

Data Democracy’s main objective is to make a company’s data widely accessible to the greatest number of people, if not to all. In practice, every employee is able to pull data values at any level. This freedom of access offers maximum opportunities to create value for the company; it provides each employee with the ability, at their level, to use all accessible and compatible resources within their needs in order to produce locally, and through a trickle effect, it will benefit the entire company.

This freedom only works if the regulations and the basic tools are implemented, and each employee is responsible for how they use their data. Therefore, the distribution of necessary and sufficient information is required to allow employees to make proper use of it while adhering to regulations.

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About Actian Corporation

Actian makes data easy. Our data platform simplifies how people connect, manage, and analyze data across cloud, hybrid, and on-premises environments. With decades of experience in data management and analytics, Actian delivers high-performance solutions that empower businesses to make data-driven decisions. Actian is recognized by leading analysts and has received industry awards for performance and innovation. Our teams share proven use cases at conferences (e.g., Strata Data) and contribute to open-source projects. On the Actian blog, we cover topics ranging from real-time data ingestion, data analytics, data governance, data management, data quality, data intelligence to AI-driven analytics.