Through a research period of four months, Eventful Conferences has conducted extensive interviews with 50+ chemical industry companies, and held two roundtable events in key chemical industry locations – Philadelphia, PA and Houston, TX. The intention of the research is to summarize the most common and critical challenges that the industry must address – crucial to their success. These pain points have been identified by the industry during the roundtables and throughout the interviews. The Best Practices for Chemicals Conference will strive to address each challenge, pain point and trend by providing solution-oriented presentations, backed by specific evidence and packaged to provide the audience with clear takeaways on how to achieve similar results.
In this issue we will discuss the importance of master data governance. If you missed or would like to revisit the previous entries in our series, please follow the links below.
If analytics and reporting is the common thread in all of our conversations, master data governance is the fiber that makes up that common thread. Analytics are only as good as their inputs, and with master data being a part of many jobs across an organization, the opportunity for mistakes or failures is wide spread. Good data, for many of our representative companies, is often an afterthought, with sales and production taking precedence over good input.
Further, ownership of master data governance is not always clear and well-delineated. While individual employees are typically the source of input, the data outputs, cleaning of data, and overall management of the process sits somewhere between IT and the business. Because of this ambiguity and the often daunting tasks associated with governance, many companies find it difficult to justify the time and costs needed to make a significant change. While many of our chemical industry representatives acknowledge that this hot topic requires their own internal discipline, many want to know how technology can help.
How do we improve inputs without frustrating users and losing any progress to date?
What are some best practices for master data governance and who is the ideal owner for the processes associated?
How do we justify the time and costs associated with master data governance projects?
How can we use our technology to better detect errors before they make their way into time-sensitive, important reports?
Sales and moving product always take precedence over inputting good data – can predictive data help me with my master governance problem?