Data lakes offer an unlimited storage for data and present lots of potential benefits for data scientists in the exploration and creation of new analytical models. However, this structured, unstructured and semi-structured data are mashed together and the business insights they contain are often overlooked or misunderstood by data users.

The reason for this is that many technologies used to implement data lakes lack the necessary information capabilities that organizations usually take for granted. It is therefore necessary for these enterprises to manage their data lakes by putting in place effective metadata management which considers metadata discovery, data cataloguing, and overall enterprise metadata management applied to the company’s data lake.

2020 is the year that most data and analytics use cases will require connecting to distributed data sources, leading enterprises to double their investments in metadata management. – Gartner 2019.

How to leverage your data lake with metadata management

To get value from your data lake, it is essential for companies to have both skilled users (such as data scientists or citizen data scientists) and effective metadata management for their data science initiatives. To begin with, an organization could focus on a specific dataset and its related metadata. Then, leverage this metadata as more data is added into the data lake. Setting up metadata management can make it easier for data lake users to initiate this task.

Here are the areas of focus for successful metadata management in your data lake:

 

Creating a metadata repository

Semantic tagging is essential for discovering enterprise metadata. Metadata discovery is defined as the process of using solutions to discover the semantics of data elements in datasets. This process usually results in a set of mappings between different data elements in a centralized metadata repository. This allows data science users to understand their data and have visibility on whether or not they are clean, up-to-date, trustworthy, etc.

 

Automating metadata discovery

As numerous and diverse data gets added to a data lake on a daily basis, maintaining ingestion can be quite a challenge! By using automated solutions not only does it make it easier for data scientists or CDS to find their information but it also supports metadata discovery.

 

Data cataloguing

A data catalog consists of metadata in which various data objects, categories, properties and fields are stored. Data cataloguing is both used for internal and external data (from partners or suppliers for example). In a data lake, it is used for capturing a robust set of attributes for every piece of content within the lake and enriches the metadata catalog by leveraging these information assets. This enables data science users to have a view into the flow of the data, perform impact analysis, have a common business vocabulary and accountability and an audit trail for compliance.

 

Data & Analytics Governance

Data & analytics governance is an important use case when it comes to metadata management. Applied to data lakes, the question “could it be exposed?” must become an essential part of the organization’s governance model. Enterprises must therefore extend their existing information governance models to specifically address business analytics and data science use cases that are built on the data lakes. Enterprise metadata management helps in providing the means to better understand the current governance rules that relate to strategic types of information assets.

Contrary to traditional approaches, the key objective of metadata management is to drive a consistent approach to the management of information assets. The more metadata semantics are consistent across all assets, the greater the consistency and understanding, allowing the leveraging of information knowledge across the company. When investing in data lakes, organizations need to consider an effective metadata strategy for those information assets to be leveraged from the data lake.

 

Start metadata management with Zeenea

As mentioned above, implementing metadata management into your organization’s data strategy is not only beneficial, but essential for enterprises looking to create business value with their data. Data science teams working with various amounts of data in a data lake need the right solutions to be able to trust and understand their information assets. To support this emerging discipline, Zeenea gives you everything you need to collect, update and leverage your metadata through its next generation platform!