What if a management approach existed that improved customer retention, made your organisation more efficient and gave you a competitive edge?
In the 1970s and 80s a new manufacturing philosophy began to emerge in industry: total quality management. TQM sought to minimise quality issues by taking an all encompassing approach to the product lifecycle, spanning the entire design, supply and manufacture process. Organisations that adopted TQM saw their competitiveness skyrocket.
Japan was one of the first nations that saw widespread adoption of these principles. In the 1950s and 60s the quality of products produced by Japanese organisations increased dramatically. This led to a great increase in exports, massively boosting the Japanese economy. By the late 1970s and 80s, American companies were adopting the same techniques in order to restore their own competitiveness. Today, the principles of TQM continue to evolve and are implemented by organisations worldwide.
TQM revolutionised the manufacturing process. Quality assurance enabled organisations to provide reliable, trusted products to consumers, vastly increasing their competitiveness as a result. Before it, there was no standard, systemic approach to quality management. With it, quality could be effectively monitored and continuously improved, with clear business results.
Today’s organisations have become highly effective at producing quality products. But when it comes to data, quality issues are the norm.
In a 2016 report by 451 Research, 94% of respondents thought business value was being lost to poor quality data. 65% thought 10-49% of business value was lost, and 29% thought more than 50% of business value was lost.
The above graph shows where 451 Research respondents thought that business value was being lost.
A recent Experian Survey produced similar results. Respondents said on average that 33% of customer and prospect data was inaccurate. 89% of respondents believed data quality was undermining the ability of their organisations to provide excellent customer experience.
Anybody who has worked with data understands that quality is an issue. Most organisations will openly admit to it. So why is this issue not being addressed?
Part of the problem is that many of the principles underlying TQM - total responsibility for all aspects of the product lifecycle, an integrated approach to production, systemic, process based thinking - are nonexistent when it comes to data. Data is all too often seen as “somebody else’s problem” (most often, IT) and is frequently trapped in departmental silos that prevent it being treated as an enterprise asset. Furthermore, many organisations also fail to design and implement effective processes for managing data across the entire systems architecture. This leads to a rapid buildup of problems that include duplications, incorrect values and missing data. These, in turn, have real impacts on business value. For example:
An IBM study found that poor quality data costs the US economy $3.1 trillion every year.
Gartner research suggests organisations believe poor quality data causes $15 million in losses per year.
Research conducted by Royal Mail in 2016 suggested around 6% of organisations’ annual revenue is lost due to poor quality data.
The TQM approach to manufacturing was revolutionary. Today, it is only a matter of time before a similar approach to data quality management is adopted. As extracting valuable insights from data becomes more and more essential for success, necessity will force thinkers and organisations to devise more effective and reliable methods for managing data quality. This goes beyond the need for new management software or better data governance policies. What is needed is a systematic, all-encompassing approach that spans every instant of the data lifecycle and every individual who works with data.
Future organisations will likely look back on us today the same way as we look at 20th century organisations before the widespread adoption of TQM. In a truly data-driven world, it will be hard to believe that so many organisations put up with poor quality data for so long.