Top trends and solutions for data quality in 2023 include: building a strong data culture alongside DQM strategy; taking cloud data technology to new heights; relying on AI/ML models for data quality management; and investing in trust architectures and other governance opportunities.
Data quality simply includes how effectively data works for data-driven business projects and operations. Poor data quality can cause problems such as inconsistent production models and loss of trust and reputation.
For the first trend, building a strong data culture is the key to the success of DQM and other data-driven business strategies. Without a solid DQM strategy based on trained and organized processes, even the most advanced technologies can be inadequate.
The second trend involves taking cloud data technology to new heights. With increased competition among the top cloud providers, new cloud solutions are emerging. Corporate data teams therefore benefit from competition among cloud providers, which has led to the release of disruptive new data solutions and accelerated the modernization of data warehouses.
The third trend involves the use of AI/ML models for data quality management. DQM processes and technologies are already more reliant on ML and AI to solve common data quality problems. With the right intelligence models, companies can automate and augment tasks such as data classification, predictive analytics and data quality control.
The sources for this piece include an article in TechRepublic.
