These days, companies are increasingly using Digital Transformation (“DX”) and Business Intelligence (“BI”) and Data Analysis to inform their business strategies and growth plans. The world has seen exponential data growth in the past few years, which has driven the need for all organisations to implement best-practice methods for data protection and data management.

Due to the enormous escalation of data growth, it has become imperative for companies to address Data Quality, i.e. the refinement of data sources and removal of data which is incorrect or irrelevant for their purposes.

Clean, accurate and targeted data better serves business purposes rather than huge amounts of data consisting of unclean, irrelevant or inaccurate data.

The best time to improve data quality is at the outset of a data analytics or business intelligence project; research shows that the later data quality issues are addressed in the business intelligence lifecycle, the more expensive rectifying issues becomes. User confidence in analytics and intelligence also suffers as a result.

Data Quality therefore also plays a large part in the development of an organizations’ marketing strategy; i.e. how they decide their marketing tactics? Data analytics based on high-quality data is paramount to success.

To understand how you can improve Data Quality, we have curated some of the methods which may help you.

1. Create Specific Data-related Roles

Creating high data-quality requires proper data management and data processing which cannot be achieved without assigning roles and responsibilities to appropriately experienced staff. In many cases, new staff may need to be hired for specific data-related roles. This allows them to completely focus on data management and governance activities. Ideally, the process should begin with a Digital or Data Strategy that outlines the various roles required.

2. Adopt Better Data Processes

Adopting better data-related processes helps with data quality assurance. One such process which ensures high-quality data is data quality improvement cycle.

The data quality improvement cycle consists of the systematic process of analysing, cleansing, and monitoring data quality. The cycle emphasizes the fact that data-quality management is not a one-time process but an ongoing one.

3. Process Standardisation

Instituting standardisation of data management eradicates duplicate data, avoids recording of incorrect or missing data and ensures consistency and quality of data analysis.

Standardisation may also help improve data protection and reduce data breaches.

Using appropriate data management tools may help with standardisation, also increasing data resilience efficiency and making processes easier, faster and more accurate.

4. Hire Help

Hiring an expert in Data Quality management is one of the best methods for data management and ensuring high-quality data.

This can help the owner or top-level executives to completely focus on marketing strategies instead of worrying about the data itself. Hiring an external expert can help fast-track delivery of high-quality data, as they usually have expertise in various data-related disciplines, such as data legislation requirements.

Conclusion

As competition increases and Digital Transformation projects mature, many industries are becoming increasingly competitive so any improvement to data quality helps maintain a competitive advantage and expand the horizon of your business.

For more information, visit www.dataresilience.com.au or drop us a note at [email protected]