New eBook! TDWI Checklist Report: Strategies for Improving Big Data Quality for BI and Analytics
In a big data environment, the notion of data quality that is “fit for purpose” is important. For some types of data science and analytics, raw, messy data is exactly what users want. Yet, even in this case, users need to know the data’s flaws and inconsistencies so that the unexpected insights they seek are based on knowledge, not ignorance. Syncsort’s new eBook, Strategies for Improving Big Data Quality for BI and Analytics, takes a look at applying data quality methods and technologies to big data challenges that fit an organization’s objectives.
As organizations grow dependent on the data they have stored in their big data repositories, or in the cloud, for a wider range of businesses decisions, they need data quality management to improve the data so that it is fit for each desired purpose.
Our TDWI checklist report offers six strategies for improving on big data quality:
- Design big data quality strategies that are fit for each purpose
- Focus on the most important data quality objectives for your requirements
- Perform data quality processes natively on cloud and big data platforms
- Reduce analytics and BI delays by applying flexible data quality processes
- Provide data lineage tracking as part of data quality processes
- Use data quality to improve governance and regulatory compliance