Our series, Strategic Imperatives for the Chemical Industry discusses the issues that industry organizations are most concerned about going forward in 2015. Based on extensive research performed by the Eventful Group, these key issues come directly from players in the industry ranging from small blossoming organizations to the household name manufacturing giants.
Welcome back. This installment showcases top industry issues in the realm of data management and utilization. If you missed or would like to revisit the previous entries in our series, please follow the links below.
Master data management and governance continues to be one of the greatest challenges facing the chemical community from a systems perspective. Being predictive and strategic are key to being competitive in the global marketplace, and poor data quality eliminates this advantage. Data issues are less about numbers and decimal points and more about people and process―the culture of the organization must embrace the importance and value of data and accountability must be visible from the top-floor to the shop-floor.
What are the best practices in master data management?
What are the best practices to technically manage master data?
What are the best practices to qualify, clean and analyze the accuracy of master data?
What are the best practices for aggregating data across disparate systems?
How do we streamline the aggregation of data from SAP satellite systems?
How do organizations that have been live on SAP platforms for some time approach “re-cleaning” master data?
How do organizations develop effective business cases for investing in master data management? What are the best practices for developing a master data management and governance team?
What are the most effective data warehousing solutions?
What are best practices for integrating, aggregating and retaining legacy data?
Who owns the governance of master data and how are organizations involving key stake holders from across the organization to ensure the accuracy and strength of master data?
The culture of the organization must shift to impart ownership of data quality by all employees. Should responsibility for the data be held by IT, the business or both?
What are the most cost effective means of getting rid of historical or legacy data?
How do organizations determine what data is integrated and stays and what data is thrown away? What are the implications? Some organizations are turning away from keeping historical or legacy data as a means of reducing risk and legal exposure. How do we reduce risk and exposure and when it is practical to keep legacy data and appropriate to discard?
If data management is an area that your organization seeks to improve, please reach out or feel free to continue the conversation in the comments section below.