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Managing Data Traceability: Impact and Benefits
Data is at the heart of digital transformation. A company can’t support data lacking integrity if they aim to advance in their digital transformations initiatives. The integrity of the data lies primarily in the confidence that users can have in the latter. Most of this trust rests on the traceability of data. In the absence of traceability, it is not possible to know if these data are trustworthy.
Data traceability is a concept that companies have been trying to understand for some time. You might be asking for which reasons do today’s companies need more traceability? Well with a large amount of data from unmanaged external sources (sensors, data streams, Internet of objects), it is essential, for companies, to monitor these data when they are collected, processed and moved to be able to use them effectively. Digital transformation requires higher levels of data integrity. Indeed, companies need to have better data, which can be a basis they can trust.
Previously, data traceability was based on two dimensions: “where” and “how”. The need for better analysis and exploitation of the data leads to new demands and extends the definition of data, adding the following dimensions: “what”, “when”, “why” and “who”. Faced with these new requirements, it is necessary to master the bases of the primary components of the type: “where” and “how, especially as regards the impact and the value to be realized.
The “where” component of traceability focus on the origin of the data. The “how” component focus on how the data source was manipulated to produce its result. It is also possible to refine these two types of dimension via their level of granularity: “low” granularity and “high” granularity. The “where” component at the “low” granularity level focus on defining an upstream output dataset at the point of consumption to understand which dataset have been used to produce a result. The “how” component of the “low” granularity level focus on the transformations applied to the set of data source to produce the output dataset. On the other hand, “high” granularity level of traceability is concerned with the values of data in the “low” level granularity: instead of where they were created and how they were modified to produce the result.
An example will better illustrate the types and granularities of traceability. Let’s take an accounting report, showing the total amount paid to suppliers over a given period. The “where” component of the “low” granularity level would trace all output data from the source invoice to the supplier tables from the accounting application. The traceability component “how” of “low” granularity level would look at how the supplier and billing tables were linked together with the calculation functions that were performed on the billing table to produce the total amount paid to each supplier. Traceability “where” at the “high” granularity level could (to search for the amount paid to a vendor) trace back to the invoices provided by the supplier. In order to take interest in the entire process, traceability at the “high” granularity level could also link to the original request: the purchase order, the receipt operations, in addition to the payment approvals.
Benefits of using data traceability
Declined in many forms, traceability can provide many benefits in terms of impact and added value to the companies that implement it. Such as:
- Governance: Ensure the traceability of upstream data to provide owners and data sources with quality and access control results. This will allow data owners to manage their procedure in addition to downstream traceability (integrated with a corporate glossary, data traceability can allow data managers to control current definitions and understanding of terms and fields of data).
- Conformity: Provide regulatory authorities with information to govern data sources, users and their behavior.
- Change Management: Enabling users and developers to understand the impact of modifying certain data on downstream systems and reports.
- Development of Solutions: Improve design, testing and deliverables of better quality. This is achieved through the sharing of traceability metadata, glossaries, and relationship among distributed development teams.
- Storage Optimization: Provide as an input to archive decisions and provisions, an overview of the data being accessed and indicate where, how often and by whom access is permitted.
- Data Quality: Improvement of the quality scores defined by the application of business rules and standardization on data, added to the metadata population as input of algorithms and decision making.
- Problems Resolution: Helps in the analysis of root causes in repair-type processes.
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Traceability also brings a deeper advantage such as focus on the changed values of the basic data entities that are shared between processes, services, and applications. For example, the impact that a change in, position, service, address or even the employer of a contact might have on the marketing, business or maintenance service of a business. According to the “U.S. Bureau of Labor Statistics,” an employee has on average 11 different employers throughout his career. Taking into account the speed at which US residents move and change their professions each year, the potential change in baseline data may be important in light of the adequacy of these statistics to the population of basic data within a company. The ability to collect, validate, distribute and track these changes in a timely manner could lead to better protection of existing revenue streams and the ability to capitalize on new revenue in B2B or B2C business relationships.
So, the companies which take advantage of traceability, are able to find data faster and are better able to support security and privacy requirements.