Data Quality: A “must” for the Business Success

Many organisations are seeking to become data driven and are investing heavily in new technologies to make it happen.  But this investment will not deliver its promised benefits unless the data itself on which it depends is fit for purpose and of the required quality.  Having accurate, complete and reliable data is a ‘must have’ for any digital business or organisation.

One of the oldest rules in Data Management is ‘Garbage in, Garbage Out’.  If data is of poor quality other Data Management disciplines can never fulfil their promise.  Business Intelligence initiatives will generate information and outcomes that cannot be relied on, leading to poor decision making.  Master Data Management will fail to deliver a single, integrated view of a customer or product.  Artificial Intelligence and Machine Learning will deliver faulty insights and generate misguided actions. All Data Management disciplines rely on a sound and trusted data foundation, but all too often this is not the case.

Worse still, poor data Quality has a significant direct and adverse impact on the business or organisation itself.  When data is not fit for business purposes, revenue is lost, unnecessary costs are incurred, productivity and efficiency suffer, and the risks of breaching regulatory and legal requirements (for instance with GDPR) are greatly increased.  Poor Quality data can damage the brand and reputation of any organisation. 

Despite this, the reality is that data Quality in most organisations remains poor, and often very poor.  A major study conducted by The Harvard Business Review and MIT in 2017 found that fully 97% of records surveyed in key organisational databases contained critical data Quality errors. Other surveys since have reinforced these findings and so identifying and tackling data Quality issues is a top priority for any organisation which seeks to extract the maximum value from its data assets. 

In this two day seminar and workshop Nigel Turner will answer a number of fundamental questions that need to be answered to deliver fit for purpose data Quality.  These include:

• What is good data Quality?
• Why should I care about it?
• How do I know what my main data Quality problems are?
• What is the impact of these on my organisation and how can this be measured?
• How do I make the case for action?
• What data Quality problems should I seek to address first?
• How can these data Quality problems be fixed or improved?
• Who should be responsible for taking action?
• How can I ensure that I prevent future data Quality problems as the volume, variety and velocity of data increases?