Our client is a leading European telco with more than $100 billion in annual sales.
Prior to bringing in MIOsoft, they introduced a new and ambitious master data management plan. Responsibility for data quality was given to a central data quality (DQ) department, and new data governance, quality, and quality assurance protocols were introduced.
Our client had evolved a markedly decentralized approach to IT. Each department had full responsibility for its own data systems, was wholly focused on its own niche and own customers, and developed its own operational practices.
Corporate-level documentation of these operations didn't reflect the actual day-to-day IT activities of individual departments, and not all those activities were aligned with the client’s overall understanding of its data and processes.
Their revenue assurance system exhibited challenges typical of those the new DQ faced.
Revenue assurance systems are supposed to identify billing problems so that customers are billed exactly the right amount. Underbilling results in missing revenue, while overbilling reduces customer satisfaction and, for some enterprises, can have legal implications.
Major challenges facing data quality for the client’s revenue assurance system included:
Frequent changes: The client’s decentralized infrastructure let departments make architectural changes (including major ones) to their systems relatively frequently. And since departments were responsible for operations, not data quality, communication with the data quality team was sometimes lacking or slow.
Complex systems: Department systems were frequently made up of multiple subsystems. Even within a single department, data could flow asynchronously through multiple paths, making it even harder to track down the source of an error. The revenue assurance system, for example, originated everything from a single CRM, but the data then traveled through 1-5 other subsystems.
Missing and lost relationships: A single customer’s information was often spread across the infrastructure of several departments, making it difficult to directly compare the data. This dispersion also meant that the relationships between customers and contracts were frequently lost. The data quality program needed to reconstruct those, since assessing the correctness of a customer’s bill is impossible without knowing what contracts the customer has.
Massive data: The data quality project needed to handle 11 billion records. And, because production systems couldn’t support resource-intensive analytics alongside normal operations, the data also had to be migrated to an analysis-specific system.
We developed a solution for the client that used our expertise in complex, large-scale data systems and our tools, which we had created specifically to bootstrap problems like Deutsche Telekom’s.
A unifying central model represented all entities, and the relationships between them, that the revenue assurance system knew about. Since our connector tools don't require preprocessing or affect the original source systems, we could populate this model with each department's data without affecting operations.
Using entity resolution expertise, our solution automatically matched and linked data that was about the same customers, bills, contracts, and products. Unsupervised machine learning created meaningful matches even when the data was not identical and/or not complete.
Finally, we developed a way to automatically judge which data, within a group of matched records, most reliably described the entity. This best-quality data was committed to the main database, creating a single set of improved, reliable data.
Because of our forward-looking techniques and tools, the solution did not need to change whenever new subsystems were added to or removed from the data quality program, since independent connectors isolated the sources from the project's core capabilities. This also worked in the other direction: implementing our system did not require any significant changes to the client’s already difficult-to-manage technical ecosystem.
We found that 4% of billed contracts were underbilled, resulting in $50 million/year of lost revenue.
In addition to the cash savings:
Ultimately, our solution enabled our client to proceed with their master data management plan with full confidence in their revenue assurance data while also recovering missing revenue, improving their data automation processes, and increasing their customer satisfaction.
