Data Lineage: How To Ensure Data Quality And Integrity

To rely on data and truly become a Data-Driven organization, you must first of all trust it; one of the fundamental elements in this sense is to use Data Lineage techniques and solutions.

The Data Lineage is the technique that allows you to identify and represent the life cycle of the data from when it enters our organization until it leaves it, keeping track of the transformations it undergoes over time, which are the main processes that involve it and the interdependencies of the latter with other company systems.

It is therefore evident, since its definition, that the Data Lineage is a fundamental element, in general for Data Governance and even more so for Data Quality, because the more significant the knowledge of systems, processes, and transformations that characterize the data, so much the implementation of controls on them is more effective.

The Risk Of The Wireless Telephone

The risk of not adopting effective Data Lineage solutions is that which occurs when playing on the wireless telephone: the words entered into the system by the first player can be wholly distorted during a word of mouth that characterizes the game, with the risk that the last player pronounces completely different words from the original one by changing the meaning of the initial sentence, without being able to reconstruct where the words have been changed.

Replacing words with data, this is precisely what happens in a company in the absence of a Data Lineage system: data, during its life cycle, flow within a flow-through complex architecture, solutions, and processes and, if not adequately managed, they can suffer the application of wrong rules, or they may lack information, and reach the end of their cycle (for example the production of a report) without anyone being able to explain where and why the error or change has been generated.

Customers, legislators, and business users need secure and compliant data. This data must be available when and where it is needed and, at all times, it is necessary to be able to reconstruct its history; this need becomes even greater within complex organizations where we typically find large platforms used by many users, data in various formats and hybrid architectures (both cloud and traditional).

The Value Of The Data Lineage

The Data Lineage offers numerous advantages to all those who have to do with data and Data Governance systems in general:

Data Incident Analysis

Often, data errors are found only at the end of the process that involves them, typically when a report is read; often, however, the error originated much further upstream. The Data lineage allows you to easily and quickly reconstruct the chain that produced that data and, therefore, quickly identify the moment the error was introduced.

Impact Analysis

It is often necessary to change existing systems for regulatory or business reasons or to change some software systems. When the change involves data, the Data Lineage allows you to evaluate a priori which applications, which data, and which downstream processes will be affected by the change and helps plan the updates of the various applications.

Performance Assessment

The Data Lineage makes it possible to identify any “bottlenecks” in data management and, therefore, allows intervention with alternative solutions that can be assessed through impact analysis.

Data Lineage, Data Governance, And Data Quality

As previously anticipated, there is a strong interdependence bond between Data Lineage, Data Governance, and Data Quality. The Data Lineage indicates where the data comes from, where they are going, and which transformations are applied to them through multiple processes: one of the essential information processes for Metadata Management.

From the point of view of Data Quality and Data Governance, it is essential to use the Data Lineage to ensure that business rules exist or not, that they are applied where required, that the calculation rules and other transformations are correct and that the inputs and outputs of systems are compatible. The traceability of data, guaranteed by the Date Line, is also the best tool for data validation and represents a control tool relating to their use for audit and regulatory compliance purposes.

The Link Between Business And Operations

Mapping processes from a business point of view and doing it from a technical/operational point of view is quite different. Still, there is a connection: data is behind the business and all operations. By creating the business metadata and their technical counterpart, following the life cycle of the data, or using the Data Lineage, it is possible to build this connection. The result is surprising, as it is possible to locate specific business metadata and track it across multiple applications, data sources, interfaces, models, analytics, reports, and more. This guarantees the entire company organization maximum data reliability, transparency in its management, and possible collaboration at all levels.