The orkestra Data Platform facilitates and accelerates the porduction and distribution of data for business teams.
Data for businesses is generated in the form of Data Products integrating all knowledge information.
Data Products circulate within and between business domains, under the control of use-cases governance.
Key feature
In the increasing flow of dataset production, identification becomes essential.
The Orkestra Data Platform automates this process by creating a codification for each dataset.
The label is inserted within the dataset, making unique, immutable and identifiable
The label is the key to promoting the control, the quality and shearing of datasets
Each file streams automatically generate a « descriptive label »
This label of metadata, a kind of a barre code, enables:
Every identifiable dataset is also traceable
The Data platform Orkestra automatically provides data lineage capabilities for operational dataset.
Our promise :
Pragmatic, operational governance that expands with the use cases
Which measures the data quality from a business perspective
Rationalize management’s information system, improve business process and deploy data governance
▶︎ Production of a set of data applications meeting business needs.
▶︎ Business capabilities operated by Orkestra, in a generic way
▶︎ Qualitative and quantitative gains (productivity, automation of low-value business tasks, ease of data interpretation, reduced risk, reduced non-quality) brought by Orkestra to these business capabilities.
▶︎ Data interpretability. The data platform offers four useful supports for data interpretation :
The Data Manager module integrates all business rules and data production processes into Orkestra.
Parameterization of global attribute catalogs: process, treatment, scope, typology (data type and format).
Construction of label structure.
Definition of management rules: allocation of Cycles, choice of additional attributes (part of a label grouping, must have a validity period, must have a distribution period, etc.).
The Data Fabrik module enables you to launch and validate the production of data sets.
The user (“business”) can configure and schedule production according to the rhythm of the activity and determine the teams in charge of validating and distributing results.
Validation is simplified thanks to the accessibility of data quality reports, facilitating approval and/or certification.
Production incorporates output data labeling, facilitating communication between the various parties involved throughout the production process.
Each file flow generates a “descriptive label”. This metadata label, a sort of “barcode”, enables:
Datasets are never deleted, enabling 360° data lineage.
Labels are generated by the platform.
Restitutions can be made with the latest data label(s), or with specific labels. (Budget applications, historical reconstitutions, ….)
The Datashop portal is the best place to access and share business datasets.
Its intuitive interface makes it easy to find and download datasets.
The MSO module monitors processing to assess completeness and compliance.
After two decades of effort on the part of companies to better collect, analyze and understand data, we can only conclude that many professions still have only limited access to data, and that the democratization of its use is still wishful thinking. Yet we know that data is an essential element in improving performance. Companies…
A “data swamp” occurs when data is stored in a disorganized and unstructured way, making it difficult to use and understand. The data becomes unusable, without any value, so there’s poor data management. To avoid a data swamp, it’s important to implement effective data governance processes. Here are a few tips on how to achieve…
Data traceability has become a major issue. By implementing effective data traceability systems, organizations can protect the privacy of individuals, guarantee the security of their systems, comply with data protection regulations, assume their social responsibilities and optimize their processes. Let’s take a closer look at these famous processes: Data traceability is a key element of…
Orkestra is a business data orchestration platform that enables businesses to increase the quality and traceability of the data they produce and share. At the heart of the system, Orkestra has developed a data labeling technology. Data tagging involves assigning labels (or tags) to data to facilitate its search, organization and classification. Tags can be…
DICS Pyramid, Data Governance, Traceability, Tags and Actions, or how to access Wisdom? “True strength begins with wisdom.” The DICS pyramid (for Data, Information, Knowledge and Wisdom) represents the entire data value chain. More than just a data classification method, it could be presented as an objective for all corporate data governance. Apart from the…
Business data governance is a complementary and more operational approach than enterprise data governance and is the starting point towards a data organization oriented towards Domains and Data Mesh. Although complex to implement, business data governance is nonetheless essential to support the proper use of data at business level. This approach’s success can only be…
Data monitoring is an essential step in data governance. It involves continuously monitoring the quality of data and ensuring that it is used in accordance with the organization’s policies and procedures. Data monitoring can be carried out in several ways, such as: Data quality monitoring This step involves monitoring data quality to ensure that it…
Imagine that, despite all the efforts made by organizations to centralize, organize, catalog and exploit data, a significant proportion escapes any form of “governance”. We call this Shadow Data. So where does it come from? Well, mainly from the business units, who, far from wanting to make a mess of this organized data management, find…
