Archive for the ‘Skills’ Category


When it comes to Data Quality Delivery, the Soft Stuff is the Hard Stuff (Part 6 of 6)

April 5, 2013

In my previous blog I explored the importance of a firm understanding of commercial packaged applications on data quality success. In this final post, I will examine the benefits of having operational experience as a key enabler of effective data quality delivery.

Similar to understanding packaged applications functionality, having hands on experience in an operational capacity (procurement, production planning, order fulfillment, etc.) provides an invaluable perspective and appreciation for the intended purpose of data on the processes they enable. Since data permeates every facet of the enterprise, it should be no surprise that operational roles work very closely with data – typically as both a consumer and producer. Regardless of which end of the data supply chain you reside on, folks in these roles have first-hand experience when it comes to the impacts of bad data on day-to-day operations. For example consumers of bad data often times end up having to design elaborate workarounds to make up for deficiencies in the data that they receive. These exception processes drive increased overhead and drag, resulting in operational inefficiencies and work output delays – which in turn may result in further downstream impacts. Not only do these consumers understand the importance of good data on operational effectiveness, they have an innate understanding of which particular data attributes are more critical than others when it comes to their domain of expertise (e.g. non-stock procurement). This expertise perspective, coupled with a profound appreciation for the importance of good data, makes folks with operational experience great data quality candidates. All that remains is making sure these resources have the necessary technical skills to perform the job. In my experience, layering on the technical proficiency to complement these functional skills is much easier than the other way around.

Lately, we have been witnessing the emergence of the data scientist role. The recognition of this role is helping to elevate data’s importance to the enterprise, and the need for the “right” skills to harness the business benefit potential that effective data management and use can deliver. However, to date I have seen a disproportionate emphasis on the more technical aspects of the role. It will take more than advanced analytics, predictive modeling and slick algorithms to advance the cause of data management as a means of optimizing business performance. To be truly effective, these new roles will have to balance the “hard stuff” with the “soft stuff”.