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How service providers can improve data trust and accelerate cloud migration with AI-powered Data Quality Management (DQM)

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Data Quality Management

Around the globe, businesses have increased their cloud usage. According to Gartner, 75% of databases will be deployed or migrated to the cloud by 2022. The cloud ecosystem offers businesses greater efficiency, scalability, and performance than on-premises data centers.

But achieving these benefits is unlikely if the migrated data is not reliable. What if the data quality is lost during migration? What happens if the data quality is poor prior to migration?

In the Connectedness industry, data quality challenges have a far more significant impact on business performance and customer experience. There is a lack of complete, consistent, and accurate data in many legacy applications. Unreliable data leads to flawed decision-making, impacting various functions such as service delivery, fault management, billing, revenue assurance, and many others. Resolving data quality problems delays overall project timelines.

Therefore, the service providers need to focus on improving data quality and accelerate cloud data migration to provide better services for retaining and broadening the customer base. This article further elaborates on a two-step approach to improve the data quality across a hybrid environment and accelerate cloud migration.

The two-step approach to improve trust in data and fast-track cloud migration

1. Create a holistic data quality strategy to set a strong foundation

Many service providers start migrating to Cloud without devoting sufficient time and attention to their data quality policy. Successful cloud adoption and implementation require a holistic approach to ensure the data is trustworthy, secure, and governed. To set up a data quality strategy, consider the following factors during the implementation journey.

  • Establishing an efficient data quality analysis, data mapping, and quality monitoring at every step in the migration data flow
  • Adopting a quality-first data culture as part of the standard business practices
  • Identifying the right DQM solution aligning with the business requirements
  • Addressing the security and regulatory requirements

2. Implement a Data Quality Management (DQM) framework

Migrating huge volumes of data from varied external data sources to the cloud can be complex and risky if not implemented correctly. If done manually, data mapping and classification exercises can be time-consuming and prone to errors. Service providers must adopt a modern DQM framework to migrate cloud data successfully.

Here is an efficient Data Quality Management (DQM) framework powered by the key accelerators that can fast-track cloud data migration:

  1. AI-powered data mapping to accelerate the data ingestion process
  2. DQM classification matrix to accelerate the migration discovery, implementation, and support phase for data quality improvement
  3. DQM monitoring dashboard to measure the data quality trends, increase issue fix rate, and accelerate the data migration lifecycle
Data Quality Management

Fig 1. Leveraging the DQM framework for a successful cloud data migration

  • AI-powered Data Mapping

Data ingestion is crucial for building a data pipeline. However, as the need for more pipelines grows, the complexities increase exponentially, resulting in a much slower implementation. A cloud-native, AI-powered data mapping approach can enable service providers to overcome the challenges of going through repetitive and manual steps in data ingestion, thereby fast-tracking the entire process and providing a high confidence score. Service providers can use a trained AI enabler with a natural language interface model pre-trained on telco datasets to load the source and target datasets and auto-map them. This can accelerate the data ingestion process by 30-40% and reduce manual effort by more than 80%.

Fig 2. AI-powered Data Mapping

  • Data Quality Management (DQM) Classification Matrix

The DQM classification matrix is an integral part of the DQM framework that strengthens the data quality and enables faster mapping, classification, and prioritization of issues in the overall data migration process. The matrix provides standardization for data quality issue mapping and enables prioritization of data quality issues, accelerating data migration.

Data Quality Management

Fig 3. DCM Classification Matrix

  • Data Quality Management (DQM) Monitoring Dashboard

Utilizing the DQM monitoring dashboard, users can visualize and track the data quality trends, fix issues faster, and accelerate the data migration lifestyle. The dashboard improves the productivity of migration, development, testing, and support teams with faster issue identification. It also enables the cross-verification of fixed issues by clearly visualizing the data quality trends to prioritize and fix the issues based on severity.


By embracing these transformation levers, service providers in the connectedness industry can achieve a successful cloud migration with improved data quality, reduced data mapping efforts, and shorter data migration timelines.

I appreciate the efforts of my colleagues Sumit Thakur, Pradeep Kumar Sharma, and Ravikumar S for their contribution and continuous support in shaping this article.

Author: Srinivasan Murugesan

Srinivasan Murugesan is a hands-on technology expert with 17+ years of consulting and architecture experience in the Telecom industry. He has a commanding knowledge of Microservices, Data Science, AI and ML, and Network virtualization. He is passionate about providing strategies and solutions to help Telcos/DSPs (Digital Service Providers) with their business and IT transformation programs. Srinivasan Murugesan is Solutions Director at Prodapt, a two-decade-old consulting & managed services provider with a singular focus on the connectedness industry. Prodapt’s customers range from telecom operators, digital/multi-service providers (D/MSPs), and technology and digital platform companies in the business of connectedness. Prodapt works with global leaders including AT&T, Verizon, CenturyLink, Adtran, Vodafone, Liberty Global, Windstream, Virgin Media, Rogers, and Deutsche Telekom among many others.

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