# Data Lake Governance: Towards a Systemic and Natural Ecosystem Analogy
## 1 Introduction  
With the realm of big data as a source of new knowledge extraction through data analysis and mining techniques, machine learning, correlation, and cluster analysis techniques, data heterogeneity and interoperability are common challenges. Ontologies and Finable, Accessible, Interoperable, and Reusable (FAIR) systems are presently able to handle these challenges effectively [1]. However, another challenge is rising, concerning the data volume and storage lifespan. In the past few decades, due to the vast amount of data being generated each second, data storage systems and analytical tools play a vital role in the big data ecosystem. They facilitate the processes of storing, manipulating, analyzing, and accessing structured and unstructured data ([PERSON] Pentaho, Hadoop, and data lakes. [[https://jamesdixon.wordpress.com/2010/10/14/pentaho-hadoop-and-data-lakes/](https://jamesdixon.wordpress.com/2010/10/14/pentaho-hadoop-and-data-lakes/)]([https://jamesdixon.wordpress.com/2010/10/14/pentaho-hadoop-and-data-lakes/](https://jamesdixon.wordpress.com/2010/10/14/pentaho-hadoop-and-data-lakes/)), 2010). Among modern data storage systems and repositories, we are primarily interested here in data lakes, designed to store a large volume of data in any format and structure. The data lake is arecent generation of storage systems conceived as data repositories to propose a flexible platform for data storage, access exploration, and analysis [2; 3]. Because their existing features can handle data heterogeneity, they provide means to generate new knowledge and identify data patterns from large amounts of data, independently of their format and structure. According to [PERSON], a data lake is a cost-efficient data storage system enabled by the new generation of data management technologies to master big data problems and improve data analyzing process from ingestion, storage, exploration and exploitation of data in their native format to mining information and extract new knowledge from massive unstructured data [4]. A data lake uses a flat architecture to collect and store data, on a platform initially based on Apache Hadoop (Highly Available Object-Oriented Data Platform) which is a beneficial big data tool [5; 6]. As mentioned above, a data lake operates as a central repository which loads data in no-schema approach. This means that the data is ingested into the data lake without predefined structure and the schema is defined only at the time of data usage and data querying. This approach is known as schema-on-read or \"_late bindings_\" and is the opposite of schema-on-write which is common in data warehouse [4; 7]. In data lakes, the \"extreme volume\" of raw data is stored and processed at the lowest possible cost, unlike data warehouses that load large scale of \"cleansed\" data in a more costly manner [4]. According to [PERSON] and [PERSON], a data lake could be viewed as a form of data warehouse that collects multiple structured data with minimum operational cost before extract-transform-Load (ETL) process or as a global storage system that contains a data warehouse for enhancing data life cycle monitoring with \"_cross-reference analyses_\" [7]. A successful data lake must satisfy properties concerning data handling and management such as: cost-effective and flexible ingestion, storage, processing, data access, and \"applicable data governance policies\" [8]. In general, data lakes contain heterogeneous and multi-modal data that renders its analysis complex, and sets requirements for rigorous processes to maintain and ensure data integrity from its storage to its exploitation. This will allow us to improve the data quality for data scientists and to decrease the cost of data storage and risk. Hence the concept of data governance has resurfaced to support the mastering of data management, to control data quality, and improve business intelligence in insurable manners [9]. Nevertheless, the life cycle of data that enters a data lake is seldom accounted for.