Why Bother With Data Quality?

Why Bother With Data Quality?

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:

  1. Launch a Data Quality Program
  2. Develop a Project plan
  3. Build a Team
  4. Review Business Processes
  5. Review Data Architecture
  6. Assess Data Quality
  7. Clean Data
  8. Monitor Data
  9. 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?