Why Bother With Data Quality?
According to an IBM estimate – $3.1 trillion is the yearly cost of poor quality data, in the US in 2016, and in the UK, it’s 6% of annual revenues according to Royal Mail Data Services.
Data engineers, data wranglers, data scientists, data analysts get that, as they spend 70 – 80% of their time fixing these issues.
Data touches all parts of the organisation i.e. marketing, sales, customer service, operations, finance etc., enabling executives to make high-level strategic decisions. Data needs to be accurate, on-time, well organised, valuable and actionable.
Having data of bad quality in organisations results in missed opportunities – which can mean the difference in getting to market first with a product over the competition. Or spotting new trends that allows early differentiation in the customer value chain.
9 steps to make sure your data is working for your business:
- Launch a Data Quality Program
- Develop a Project plan
- Build a Team
- Review Business Processes
- Review Data Architecture
- Assess Data Quality
- Clean Data
- Monitor Data
- Improve Business Processes
Don’t make data quality the lowest priority. Attaching it to the business / regulatory drivers such as GDPR, will give it life, relevance, and ROI!
What are your thoughts?