A brief history of business
Thousands of years ago, a human put a stick to clay for the first time and made some marks. These marks may have indicated “I sold an ox and the buyer owes me 30 bushels of grain.” Over the years, we got much better at recording such transactions. We invented the concept of ledger accounting, then we invented calculating machines to help with the math. The advent of computing machines led us into an age where we could store all of this electronically and automate much of those tasks. Then, someone realized that other surrounding tasks, namely business operations, could also be recorded and automated in a similar fashion. ERP and other business systems were born from this (not to mention the entire IT industry). We created data records to talk about our customers, suppliers, materials, and how they interact in transactions – all for automating the paperwork involved in the business.
Fast forward to today. We have reached a new threshold in automation. In order to “go digital,” we not only need the basic operating parameters (units of measure, lead time, source lists, etc.), but also the information detail and accuracy required to automate at a digital level. This has been very challenging, considering that many organizations know the complexities related to data that runs their existing businesses.
Breaching the digital threshold
Computer programmers have always known the rule about GIGO – “garbage in, garbage out,” and with going digital, it’s no exception. In fact, this becomes even more relevant as analysts work to provide more accurate forecasts, automate tasks based on insights found from the vast streams of big data surrounding us. While some will argue that big data allows us to deal with statistically approximate accuracy, the reality is that those companies who succeed in achieving higher levels of accuracy will generally have the edge over their competition.
I often get asked by my clients, “I understand how to do reporting and data science, but how do I ensure my data is clean and reliable enough to feed these calculations and give me results I can trust?” The answer is simple – bring relevance to the data. Data is always treated as an artifact approximating what has happened. We inspect inbound materials to specifications to ensure product quality. I wouldn’t want to buy a product from a firm who didn’t do this, because I know that without it, I’m taking my chances. Similarly, inspecting inbound data to ensure data quality to its specifications is the same process and gives you the same quality result. Despite that, I spend hours in conversation with companies that still don’t see the connection between raw material quality inspection of data against governed data specifications and the resulting finished goods quality of insights. This only works once you elevate your data to be on par with the care taken for physical products and assets that you use in the traditional business operation.
The secret to success
In summary, companies who choose to take the time to establish CDO roles in the business and identify, classify, and manage their data assets, will be much better equipped to achieve digital transformation. Those who don’t will continue to struggle along the path, and risk losing out on significant opportunities.