# Data Lake Governance: Towards a Systemic and Natural Ecosystem Analogy
## 1 Introduction
There is a strong need to conceive, define, and implement data governance mechanisms, to handle proper data retention and minimize the risk of data swamps. According to [PERSON] and [PERSON], \"data governance is concerned with the data life cycle, quality and security of data\" [2] in any storage system. Hence it is a fundamental issue in relation to the data lake authenticity. Data governance disciplines and strategies aim to prevent data lakes from becoming data swamps or maintaining poor-quality data [10]. These disciplines control or fix data quality dimensions, like: \"accuracy\", \"completeness\", \"consistency\", \"currency\" and \"uniqueness\" to guarantee validity of data [11] and complement data management [12; 13]. In the big data ecosystem, many governance mechanisms have been proposed to guarantee the veracity and accuracy of the data value. In particular, [PERSON], [PERSON], and [PERSON] [14] distinguish three categories for data governance mechanisms which are frequently implemented for data management:  
* Structural mechanism in references to the governance structures
* Procedural mechanism related to the policies for data management
* Relational mechanism concerned with stockholder communications  
With reference to this specification, some researchers define some standards or guidelines to manage data in data repositories [15]. Others like [PERSON] and [PERSON] [16] propose frameworks for big data management. Some researchers put the emphasis on communication agreements to deploy feasible data governance [17]. Since data access can be a strong competitive advantage for any organization and is shared to exchange information, [PERSON] and [PERSON] [18] presented some regulations to govern and balance data contributions. Data governance plays an important role in improving self-service business intelligence in the big data era [19]. Consequently, beneficial data governance guidelines could minimize the risk of poor data quality in data mining processes and improve its accuracy [10]. Data governance assessment improves the strategy frameworks for deploying successful data governance with respect to relevant focus areas [20]. A practical data governance framework, with a focus on data quality, increases confidence in exploration and exploitation of the data [13] and monitors _data quality efforts_ in a sustainable manner [21]  
All proposed mechanisms for data governance, concentrate on setting principles, roles, and structures to improve data quality and data lake security. The data life cycle management is one of the most important reasons for applying data governance in each data repository. However, the influence of data lifespan on proper data quality strategies for deleting or preserving data in the data lake has received little attention, even though mortality and life expectancy of data in the data lake is a serious issue when it comes to increasing the productivity of the data lake. In this paper, we start from the assumption and claim that data governance implementation concerning the data life cycle, could influence the general purification of data repositories from useless data. The concept of data lake is defined as a **system** with multiple components that are derived from **natural lake definition**. Hence, some data governance policies or regulation methodologies have been extracted from systematic approaches or natural mechanisms to preserve and destroy data throughout their life cycle. This viewpoint provides enormous capabilities to govern a data lake effectively. In this article, we propose two solutions that are respectively derived from drawing analogies with (1) nature ecosystems and (2) the concept of the supply chain to address data lakes and their governance issues. _Our approach is based on a comparison of the dynamics, life cycles, and operations within those two systems with those needed for data lakes.