Bridging the Data Gap: Unifying Datasets and Streamlining . . . Each dataset presents unique challenges, from aligning varying structures to harmonizing data collected at different times, frequencies, or levels of granularity These datasets need to be stitched together
Challenges and best practices for digital unstructured data . . . In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges
Top 6 Data Challenges and Solutions in 2025 | Spaulding Ridge In this article, I’ll share the most common data challenges our data solutions team sees in our work with clients First, you’ll get to see real-world examples of organizations experiencing these data challenges
Data Harmonisation: Steps, Techniques, and Best Practices Challenges: Inconsistencies in data formats, structures, and semantics across sources pose significant hurdles in data harmonisation Data Format Variability: Data coming from different sources often have varying formats, requiring complex transformations to align them
What Are Some Data Collection Challenges and How Do You . . . There are many things that could go wrong when collecting data, which may ultimately compromise the trustworthiness of your findings To help you think through potential challenges and how to address them, here are some challenges and advice to consider as a nonprofit organization
Heterogeneous data integration: Challenges and opportunities Collecting and integrating such heterogeneous data has been regarded as a significant challenge, mainly due to the unstructured data and variety of data sources [[3], [4], [5]] The heterogeneity of the data creates integration problems due to the dimensions of its volume, variety, and velocity