Trusted Data for BI: Integrating Data for Success
Giving business and technical users data that they trust is key to good business intelligence (BI). If users don’t have confidence in the data of a data warehouse and other BI data stores, they may argue over the data’s accuracy, refuse to use the reports and analyses fed from the data, or build their own data stores. All these paths are non-productive and lead to faulty decision making. Outside of BI, similar problems arise when users lack confidence in application data. Data that’s incomplete, out-of-date, low quality, or poorly documented can erode users’ trust. Yet all these problems and more can be cured by following modern best practices in data integration, plus related disciplines like data quality, data profiling, master data management, metadata management, and so on. In a lot of ways, the road to trusted data leads through the careful preparation of data that data integration and related disciplines provide. So user organizations need to apply more of these to establish and maintain users’ trust in data.
What you will learn:
- Why you should care about the level of trust users have in their data
- Which attributes of data users perceive as trustworthy; how this varies from BI to operational applications to database migrations and so on
- What kinds of trust different data management disciplines engender
- How a combination of data management disciplines – applied from a unified data integration platform that supports them all – can establish and maintain users’ trust in data
Philip Russom, Ph.D.