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Data Quality
Data quality is an issue that affects every application. Factors such as the quantity of data, the number of entry points for data and fewer application restrictions all contribute to the seriousness of this issue.
There are six major aspects of data quality: Completeness, Conformity, Consistency, Accuracy, Duplication and Integrity.
If any of the six aspects above are lacking, then that data can become hard to operate on and may even lead to poor business decisions due to inaccurate data, which negatively affect planning. |
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Correcting these data quality issues is not enough as they will continue to creep up again. You must correct the cause of it.
Implementing all these data quality aspects will lift your Enterprise applications to a new level of data quality, enabling more accurate forecasting, better business decisions and ultimately, a more successful business.
Data Quality Fits Everywhere
Data Entry Points |
Be Proactive. Correct the data before it enters your system. |
Application |
Correct data in a reactive mode once it’s in the application. This is a sure sign of the need for a Master Data Management (MDM) solution.
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Data Migration |
Data being migrated from legacy systems should be treated as a one-time data entry source. A Data Migration is the perfect time to avoid bringing forward previous years worth of poor quality, inaccurate or stale data. |
Application Upgrade |
Upgrading your system is the perfect time to revisit business decisions regarding how much data to bring forward. Purge stale or inaccurate data wherever possible and/or data that is no longer of use to the current business. |
Data Integration |
Integration points should also be treated as an entry point into your application. Whether the integration is through batch data file loading or real-time EAI, the integration process should transform data to meet all requirements of the application. |
Master Data Management |
A proper MDM solution will contain processes for the standardization and subsequently, validation of data. |
Skura’s Approach
- We perform a Data Quality Assessments.
- We will identify not only the major symptoms currently affecting your application, but also the hidden ones waiting to cause untold headaches in the near future.
Why Skura?
- We know CRM. We know Life Sciences. We know how Life Sciences use CRM. Our Life Sciences experience and broad client base ensure a best practice implementation.
- We know the data in Life Sciences, and how it is created, manipulated and reported on.
- We’ve seen it all, and fixed it all. If you have a new data integration issue, we want to see it. Then we will solve it, fix it and show you how to eliminate it in the future.
Helpful Tips
- Restrict data entry as much as possible on data which is deemed ‘master data’ (commonly customers, products and employees).
- Employ batch data fixes if the need is urgent, but only temporarily. Engage in a longer term plan to eliminate the need for any batch processes.
- Catch data errors at the source! Don’t let bad data into your system to begin with.
- Implement MDM solutions for those entities that are crucial to your business: customers, products and employees.
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