There are multiple data governance approaches, such as centralized, federated, de-centralized, lean, etc.; and each has its own organizational structures and recommendations. The key element is to identify the proper organizational structure that maps to your company’s culture, business climate, sponsorship and business buy in.
As the flow of big data - both structured and unstructured - continues to grow exponentially, the question every organization faces is how to effectively use big data mining for all this new information to improve their business processes and the return on their investment. According to a recent study by the IBM Institute for Business Value, the primary objective for most global companies is to use their big data streams to achieve customer-centric outcomes.
One of the ways to build a business case for master data management inside your company is to map into the decision-making or the process optimization within the organization. If your Chief Financial Officer is looking to reduce day sales outstanding, because every one day of reduction translates to $10 to $12 to $15 million in operating capital, consider reverse engineering from the activity into the data, into the foundation. You’ll want to have an appreciation of, “What do my invoices look like? What are my customers seeing when I invoice them? Are they going to pay their invoice in a timely manner?”
A maintenance health assessment is an excellent option to address the ability to use data for decision-making and improving maintenance, operations, inspections, and purchasing business processes. An all-inclusive assessment enables companies to know where to begin and how to justify the time and expense necessary to correct data and process issues. It also shows where to focus time and budget to obtain the most benefit in the least amount of time.
A best practices real time data governance scenario will typically leverage enabling technologies from SAP and SAP® BusinessObjects™ Data Services software to automate your data strategy to a real-time process in support of a “single view” of the data. Through a series of workshops and interviews with the business teams, global and regional business rules are captured, documented, and validated.
When discussing data migration best practices, it’s vitally important to establish a framework and process at the outset to identify your approach to data governance. You definitely want to start a data governance strategy early, establish what the rules are and who’s responsible and who owns the decision making process.
The challenge of effectively managing information handover during a major capital project can be daunting. The breadth and depth of associated information variables from the business processes involved can often dictate the success of a project.
Data drives business systems, applications and profitability. Conversion from legacy to a new ERP can be a large, daunting and possibly risky endeavor. A large component of the risk lies in the conversion and loading of data. A prudent thing to do is to conduct a pre-data migration assessment before starting the actual migration process so you are fully aware of any pitfalls.
According to a recent survey conducted by The Data Warehousing Institute, within six months of going live, approximately 20% of your data has aged or has data quality issues. There are a number of reasons for this, but that survey also shows that 75% of all mistakes entered into a system were entered in by employees.
When considering a master data management initiative, the appropriate flow would be for data to be first profiled and analyzed against the established data quality metrics via a Data Health Assessment. Understanding the integrity of your data helps you benchmark and manage its quality evolution. Profiling also exposes areas that need the most attention and your strengths.