Why is it that we are perfectly comfortable returning an item to a retailer due to a small, cosmetic blemish, but then go into work the next day and correct mistakes for an hour on some spreadsheet we received and not think anything of it?
We have been trained that things we buy from stores should look great, function perfectly and make our lives easier. Products have improved over time, and therefore, our expectations of product quality on the shelf has increased. This is partly due to 100 years of manufacturing innovations, continuous improvement and a relentless focus on removing waste from processes. Taiichi Ohno, considered the father of the Toyota Production System, developed his ‘7 Wastes’ model to identify the potential sources of waste in manufacturing;
This list still is the standard today, and its acronym ‘TIMWOOD’, has been written about in countless books and articles. The ‘7 Wastes’ model and others have instilled a culture of perfect quality in the best manufacturing companies. They recognize that a focus on quality at all levels of the organization will set their products apart from their competition and create more opportunities. Every high quality product on a store shelf is its own brand ambassador, loyalty program and the company’s most efficient salesperson. Product quality is not an ‘extra feature’ or ‘something we’ll get to’, it is the expectation.
This expectation has not yet been fully translated to data quality.
In the book, Getting In Front on Data, Who Does What, author Thomas Redman initially poses the following question to a media executive client;
For that client and the majority of people, the answer is likely no. Why is this acceptable, or worse yet, the norm?
There are common contributing factors, including, but certainly not limited to;
Disparate systems that don’t communicate (“My CRM doesn’t connect with anything.”)
Inaccurate or incomplete data from the source (“Why does our web form allow N/A for an email address?”)
Undefined data requirements (“Why does this report from Finance include customer numbers, but not account numbers?”)
Obsolete data (“I only get this report once a week and by then, its old news”)
Fortunately, a number of the contributing factors can likely be addressed within your immediate team or by a simple meeting or two. Others factors can be extremely challenging or impossible depending on your requirements and that’s okay. This effort is certainly a journey, not a sprint.
No matter what the next salesperson tells you in a webinar, there is no magic platform that is going to solve for all of this. You can attempt to bolt on different software tools and platforms, but if the underlying framework for your data collection, storage, analysis and distribution is weak, you are simply automating the creation of bad data. Taking the time to manually collect 50 or 100 key data points, even with a pen and paper, can potentially provide much more insight that a software platform that delivers ten reports a day that no one believes in.
It will take a concerted team effort to establish a clear vision for the future, identify the requirements that will support that vision, consistently communicate these requirements all the way up and down the value stream and evaluate the effectiveness and efficiency of your current tools. Once that is complete, iterate, iterate, iterate.
The benefits of data quality often are not expressed in the appropriate terms or scale. It does not command attention like a big sale or captivate like a flashy ad campaign. It does something much more valuable, establishing a strong foundation that helps everyone in the business work more efficiently.
We cannot afford to let data quality become a ‘nice to have’, it must become the expectation.
We are always happy to chat. Please contact us at Bonsai to see how we can help your business thrive.