Improve data scientists’ journey
A data scientist’s missions are, among others, to develop predictive models, to make data understandable and exploitable for the enterprise’s top management, and build machine learning algorithms.
To achieve their missions, collaborators must be able to determine what data is available, which ones they really need, understand the data (context and quality), and finally know how to retrieve them!
However, surveys still suggest that roughly 80% of data scientists’ time is spent on cleaning, finding, and understanding data instead of analyzing it…
Simplify data discovery for your data scientists
In order to bring meaning and context around data assets, it is essential for an enterprise to be equipped with a data catalog.
Zeenea is the solution that allows your Data Scientists, among others, to find, identify and understand data from an intuitive interface.
SMART SEARCH ENGINE
Easily find and retrieve relevant datasets
Our data catalog indexes, and automatically updates, a data set’s knowledge in Zeenea from the storage systems with which it is connected.
In the same way as Google, Data Scientists have access to a search engine to accelerate and simplify the discovery of relevant data sets for their use cases.
Simply type in a keyword, add a few filters, click search to find the needed data sets.
Work with the right information
Zeenea’s features allow data users, such as Data Scientists, to understand a data set’s context.
Metadata imported automatically or manually entered by the Data Steward in our data catalog allows anyone to verify the relevancy or even the quality of a data set for their use case.
A Data Scientist can also study the relations associated with a data asset with our data lineage feature, a visual representation of the lifecycle of the data.
EXPLORE USE cASES
Collaborate on data science projects
We offer a collaborative data catalog that allows Data Scientists to share their knowledge on datasets and their uses.
The different data profiles (CDO, Data Steward or even a Data Analyst) thus participate in the construction and improvement of knowledge of the enterprise’s data assets.
The centralized information in our data catalog allows a tribal knowledge around an enterprise’s data. In fact, sharing information and feedback in our data catalog allows Data Scientists to make better decisions when choosing which datasets to use.
Learn more about Data Scientists
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.
In the data world, a business glossary is a sacred text that represents long hours of hard work and collaboration between the IT & business departments. In metadata management, it is a crucial part of delivering business value from data. According to Gartner, It is one of the most important solutions to put in place in an enterprise to support business objectives.
To help your data scientists with their machine learning algorithms and their data initiatives, a business glossary provides clear meanings and context to any data or business term in the company.
Through automated capabilities, data discovery allows data & analytics teams to discover patterns and trends to harness and exploit data for quicker and better decision making. Knowing this, enterprises all over the world, both big and small, have started adopting data discovery solutions within their organizations.
Whether they are developed internally, or bought from Data Discovery vendors, it is no secret that this challenge isn’t just a new data buzzword, but an essential way for companies to become data driven.