The key challenges in translating high quality data to value
Most organizations consider their data quality to be either 'good' or 'very good', but there’s a disconnect around understanding and trust in the data and how it informs business decisions, according to new research from software company Syncsort.
The company surveyed 175 data management professionals earlier this year, and found that 38% rated their data quality as good while 27% said it was very good.
A majority of the respondents (69%) said their leadership trusts data insights enough to inform business decisions. Yet they also said only 14% of stakeholders had a very good understanding of the data. Of the 27% who reported sub-optimal data quality, 72% said it negatively affected business decisions.
The top three challenges companies face when ensuring high quality data are multiple sources of data (70%), applying data governance processes (50%) and volume of data (48%).
Approximately three quarters (78%) have challenges profiling or applying data quality to large data sets, and 29% said they have a partial understanding of the data that exists across their organization. About half (48%) said they have a good understanding.
Fewer than 50% of the respondents said they take advantage of data profiling tools or data catalogs. Instead, they rely on other methods to gain an understanding of data. More than half use SQL queries and about 40% use business intelligence tools.
Author: Bob Violino