Data profile

Data Scientist

DATA PROFILE

Data Scientist

data-scientistIn the 21st century, data scientists look to strike gold by venturing into an enterprise’s data assets, looking for gems to transform.

This new profession has both operational and strategic challenges. Their 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 resolve the enterprise’s problems, 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! Zeenea offers the solution and features to fulfill their needs.

DATA CATALOG

The (meta)data search engine for 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 an enterprise’s metadata search engine; it allows Data Scientists, among other things, to find, identify, and understand data via an intuitive interface.

Our data catalog becomes the reference tool for data scientists in their data discovery phase. Our tool allows a Data Scientist to:

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Find relevant data sets

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.

  • An easy-to-use platform to discover and retrieve datasets in just a few minutes.
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Understand the context and trust in an enterprise’s data

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 thanks to our data lineage feature, a visual representation of the lifecycle of the data.

  • Valorize a data set’s context for data users.
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Collaborate with all data users

We offer a collaborative data catalog that allows Data Scientists to share their knowledge on data sets thanks to our community features, such as a system of comments. 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.

  • Furthering the knowledge and feedback of Data Scientists through collaborative features.

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