Data-related demands are constantly increasing within organizations. They range from demand to access data more easily and flexibly, through demand for increased data governance, and on to demand based. These demands, which arise in a data landscape that grows ever more complex and distributed, are shifting the focus of organizations from managing data to managing metadata. And there is no other way to manage metadata then having in addition of a Data Governance Tool a Reversal Data Lineage solution – like iGovernance Suite – running behind the IT systems and architectures that allows companies to achieve the maximum value from its metadata and grant the success of the most important data-related strategies:

  • Strong Data Strategy
  • Robust Data Governance:  
  • Enterprise Metadata Management 


Creating a multi-focal Data Strategy is crucial to manage inter-related data sources, processes, and goals across the organization.

A successful Data strategy serves as a way to align business strategy with governance, and clarifies how people, policies, and culture around data should be managed in line with the goals of the business. It’s a coherent strategy for organizing, governing, analyzing, and deploying an organization’s information assets that can be applied across industries and levels of data maturity. It includes a strong Data Management vision, a strong business case and specific goals for the data assets.



The “right” people at the “right” time in the data definition process and by getting the “right” people to authorize that a data definition is thorough and complete. Data Governance is the people, process, and technology required to create a consistent and proper handling of an organization’s data throughout the entire enterprise.  It provides all data management practices within the necessary foundation, strategy, and structure needed to ensure the data is managed as an asset and transformed into meaningful information.

Effective Data Governance ensures that data is consistent and trustworthy and doesn’t get misused. It’s increasingly critical as organizations face new data privacy regulations and rely more and more on data analytics – through Data Science, AI, Machine Learning, Big Data – to help optimize operations and drive business decision-making.



Metadata is the “data in context”. It’s both the business and technical context around data. It’s the “who, what, why, where, and how” of data. Understanding the meaning of each term, each metric, each field in a database and each transformation rule applied are crucial to achieve the real business value and what it means for the organization (DAMA definition).

This strategy needs buy in from both the technical and business constituents at all levels of the organization. That’s why we talk about the Enterprise Metadata Management (EMM). EMM embrace all of an organization’s metadata management initiatives. It encompasses the roles, responsibilities, processes, organization and technology necessary to ensure that appropriate usage of metadata across an enterprise adds value and protects an enterprise’s data.



From data-quality, data-governance and data management perspectives, it is important to understand physical data lineage to ensure that existing business rules exist where expected, calculation rules and other transformations are correct, and system inputs and outputs are compatible.

Generally companies’ decisions are based on information residing in heterogeneous and stratified systems (applications and databases). The problems range from the difficulty in managing code errors (mostly manuals), to the difficulty of including obsolete system in the physical IT system lineage. From organizational point of view, aggregation of rules and metadata management determine the accuracy of the information needed at each level of the organization: from CEO dashboard to the warehouse picking list. Any Company business process must match with the physical level to ensure that each business rule is properly configured and executed by the IT systems and architecture.  Unfortunately this is often the most complex task in a Data Governance initiative because of the heterogeneity and complexity of the IT systems. Sometimes it happens that the need to recover that knowledge is so urgent that the Reversal Data Lineage initiative has nothing to do with a proper Data Governance initiative.

The main reason why Reversal Data Lineage has been considered a “Self sustaining” initiative is because some of the most critical metadata lie with one individual in IT, who ‘just knows’ how things are done.  Often this knowledge is also not shared inside the Company and it is lost when the one person who ‘knows’ is no longer available in his role or in the Company.

With a Reversal Data Lineage solution – our iGovernance Suite – any organization can recover and share the knowledge of its physical IT processes to support the Company’s data-driven strategy and digital transformation, ensuring trustworthy and reliable data.

Reversal Data Lineage is not only an IT solution. It should be at the heart of every organization looking to the future and trying to grasp every single opportunity. It is crucial to any organization that would like to: 

1) Ensure correct data to the whole organization: Digital transformation, AI, ML, Data Governance, Compliances, Migrations, Improve efficiency, Data Quality, Big Data, Data Integration Projects, Data Protection, DevOps & Agile, Transformational change and many others;

2) Recover the Company’s knowledge of IT physical processes to better manage the metadata and obtain, through trustworthy data, the maximum business value.

People want to know the data’s origin or provenance—the earliest instance of the data (the word provenance in art has implications similar to lineage; it refers to a record of ownership that can be used as a guide for the authenticity or quality of a work). They also want to know how (and sometimes why) metadata changes have been applied during a certain data lifecycle.

Understanding changes in metadata requires understanding the complete metadata chain, the rules that have been applied to data as they move along the data chain, and what effects the rules have had on the data. 

When the knowledge of these IT physical processes is lost, it causes frustration in carrying out digital transformation initiatives. Many companies do not know that today they have the possibility to recover this knowledge in a totally automatic and easy way and make it shareable within the whole Company.

The Reversal Data Lineage resulted to be really valuable for a Company needs to be:

  • 100% complete
  • Validated
  • Automatic
  • Historicized (in any runned process, different concept from versioned)
  • Usable (intelligible)