Leading analysts will reflect that on average DATA ages or goes out of date by approximately 1-2% each month which means that if you do nothing then your companies’ data could be potentially out of date or inaccurate by 10-24% per annum! Improved DATA quality leads to better decision-making across an organization. The more high-quality data you have, the more confidence you can have in your decisions. Good data decreases risk and provides consistent improvements in results, let us help you improve.
Data and analytics leaders i.e. CDO’s, CIOs are facing intensive pressure to provide “trusted” data that can help business operations to run more efficiently and making business decisions faster and with greater confidence.
Improved data quality leads to better decision-making across an organisation. The more high-quality data you have, the more confidence you can have in your decisions. Good data decreases risk and can result in consistent improvements in results.
High quality data leads to better data-driven decision-making, customer satisfaction, greater operational efficiencies, meeting legal and regulatory compliance requirements, and better analytics and enabling machine learning and artificial intelligence. High quality data is a “must-have” requirement for all enterprise.
High Quality Data and Governance – As data governance aids in discoverability, businesses with effective data governance programs also benefit from improved data quality. Although technically two separate initiatives, some of their goals overlap. These include, but are not limited to, the standardisation of data and its consistency. One way to clearly differentiate the two programs is to consider the questions posed by each field. Data quality wants to know how useful and complete data is, whereas data governance wants to know where the data is and who is responsible for it. Data governance improves data quality, because answering the latter makes it easier to tackle the former.
Success stories
Reinsurer Creates a Trusted Data Source with Data Cataloging and Data Lineage
A Smallnet client who is a leading Insurer and... read more
A Leading Property & Casualty Insurer & Re-insurer Netezza Data Warehouse Migration
For the past 7 years or more IBM have... read more
Data governance and Catalog Use case
SmallNet currently support a leading insurer operating in the Lloyd's,... read more
There are many common causes of data quality problems and these are just some of the factors;
- Manual data entry errors. Humans are prone to making errors, and even a small data set that includes data entered manually by humans is likely to contain mistakes. …
- Lack of complete information, missing records, fields
- Ambiguous data
- Duplicate data
- Data transformation errors.