Data quality, keys to efficient processes

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Improve your data quality to optimize processes, boost AI, and gain competitive advantage. Discover strategies and benefits.
Over the past decade, various technological developments such as artificial intelligence, cloud computing and the Internet of Things have led to exponential data growth. As a result, data management has become more complex and requires more processes and controls to ensure good data quality.

This challenge requires ensuring relevant data collection, managing it efficiently and analysing it to achieve objectives. Why is it important to have quality data? What strategies can be implemented to achieve this?





41 % of companies cite inconsistent data across self employed database as their biggest challenge.

Source: Webincare



What is data quality?
Data quality determines whether information meets a number of key criteria: accuracy, completeness, validity, consistency, uniqueness, timeliness, and suitability for its purpose and for management initiatives within the enterprise.



Data the power to be extraordinary



Quality standards enable users to trust the information and leverage it for data-driven decision making that improves business strategies. They are also useful for diagnosing underlying data issues and resolving them quickly and effectively. With the adoption of artificial intelligence (AI) and automation technologies, high-quality data will be crucial for the optimal use of these tools.





Companies discard up to 38% of the data they collect due to quality issues.

Source: Kantar


Why is data quality important?
Businesses rely on data quality management to support their data analytics initiatives and maximize the use of information, generating great benefits such as:

Better business decisions: High-quality data enables the identification of key performance indicators (KPIs) to measure the performance of different programs and make subsequent improvements.
Improved business processes: Good quality data makes it possible to identify where there are flaws in operational workflows.
Increased customer satisfaction: Quality data provides a complete view of target buyers. By integrating different data into the sales and marketing funnel, products can be sold more effectively.