How should a company begin to put in place a data quality management strategy? First, as with all disciplines, IT executives must assess the current state of data in the enterprise. Once they’ve ...

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Here are seven simple steps that will help in the extension of data life and will also assist in helping data retain its worth: 1. Data Sources: All data sources must follow stringent protocols of ...

In today's ever-evolving landscape of supply chain management, executives and leaders are facing an intricate web of challenges. To navigate these complexities, advanced analytics and AI have emerged ...

Data quality management efforts — tied to disrupting innovations, rapid market shifts and regulation pressures — will continue to grow in 2023 and take on a more dominant role in the data management ...

In 2025, enterprises are leveraging AI capabilities to enhance data management. Just like 2023, 2024 was a dynamic year for enterprise data management, and 2025 is shaping up to bring even more change ...

It can be tough to manage data manually, and doing so can sometimes lead to errors or inefficiencies. Spreadsheets can get overly complex, and data quality can suffer. This has become a large enough ...

“Data is a precious thing and will last longer than the systems themselves.” So said Tim Berners-Lee, the inventor of the World Wide Web. ‘Precious’ provided the data is indeed trustworthy and of ...

Kore Integrate is the perfect platform to improve Data Quality Management, said Maxwell Dallinga in a recent blog post by Kore Tech. Data quality management is a process to ensure that data is ...

Value stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of ...

Universities must tighten the quality of the data entered into AI models to improve the output generated by tools such as chatbots. Universities have been cautious adopters of artificial intelligence.

Last time we looked at the essentials of Data Collection. Now, we’ll take a look at the five main points to address to achieve higher levels of data quality. Data quality begins at source which is the ...