Tips to Evaluate B2B Data Quality & Usability by Data Selling Companies

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Last Updated on April 24, 2024 by Umer Malik

Data quality is a vital aspect that can help in driving B2B demand generation and go-to-market engine. It, in turn, can impact the business outcome aspects, for instance, accurate decision-making, customer satisfaction, and regulatory compliance. Here are the five main criteria used to measure data quality:

  • Relevance – the data have to meet all the requirements for its intended use
  • Timeliness – and up o date data
  • Accuracy – regardless of the data type, it has to be accurate
  • Consistency – it should have the recommended data format and thus can be cross-referenced with the same results
  • Completeness – there shouldn’t be any missing values or data records

Tips For Evaluating Data Quality And Usability

Data selling companies should ensure they manage each data set’s quality from its creation or when it’s received. This way, data quality is guaranteed, but they can also use these tips to help:

Design the Best Data Pipeline to Avoid any Duplication

Duplicate data is when you create a part or whole data using the same data source and logic. Duplicate data leads to different results as it’s out of sync. In the end, when there is a data issue, it becomes time-consuming and difficult to trace the cause or even to fix it. Therefore, the company or organisation must create a unique data pipeline in data modelling, assets and other areas. 

Accurately Gather The Data Requirements

One important trick to having good quality data is getting all the requirements and delivering the data to clients for its intended use. However, this process seems more challenging than it sounds; there are several rules to follow. For instance, there should be clear documentation of the requirements to understand what the client is looking for thoroughly, and the requirements should capture all the data scenarios and conditions.

Enforcing Data Integrity

Data-selling companies enforce data integrity using techniques like triggers, foreign keys or check constraints. As the data volume grows and more data sources and deliverables increase, all the datasets can live in a single database system. Therefore, there is a need to enforce the referential integrity of data using applications and processes. These processes are defined by the practices of data governance included in the implementation design.

Capable And Reliable Data Quality Control Teams

There are two vital teams in ensuring the quality of data in organizations:

  • The production quality control team – this team has to have a better understanding of the business requirements and rules. Moreover, they must have the right tools and dashboards to detect unusual abnormalities.
  • Quality Assurance – this team is responsible for checking the quality of programs and software whenever there are changes. The changes this team manages are crucial for ensuring data quality in an organisation.

Data quality is essential and requires good management of the incoming data, the best data governance and careful design of data pipelines. It’s easier and more cost-effective to prevent data problems than using defence systems to curb data quality problems.

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