Improving data quality offers many benefits – but the path to clean, standardized data may seem daunting. As with most business analysis, when the input data is bad, the output can be unreliable. And when that data output drives your business, the stakes are high for failure or success.

When talking about building an intelligent enterprise, you hear words like IoT, smart devices, digital transformation. In reality, it's usually taking “dumb” infrastructure and making things “smarter,” by adding technologies like sensors, transmitters, digital meters and so on, without actually changing the infrastructure itself.

But even the smartest technology is not useful if it cannot help predict assets’ maintenance needs, reliability, and how they should function in conjunction with other equipment. That means that master data loaded into any system of record as a digital representation of those physical assets needs to be reliable, real-time and integrated across every system that maintains a digital twin of that asset.  

Chances are, you have multiple systems of record where data stakeholders throughout your organization are accessing, changing and storing large amounts of complex data. When it comes to managing that data, and leveraging it's value for analytics, predictive maintenance and more efficient operations, organizations often face challenges in data ownership, long-term business rules identification and creation of workflows that align with everyone's priorities.

It’s easy to accept myths as reality. If you hear something enough – it HAS to be true right? I spent most of my lifetime believing (and saying) “Lightning never strikes the same place twice” only to realize my mistake on a tour of the Empire State building when they announced that lightning strikes that building on average 100 times a year. Boy, do I hate being wrong!

We live in a world where everything revolves around data and has become imperative for organizations to treat data as an enterprise asset. Master data poses many challenges such as how to effectively manage it, keep it up to date, sustain it and ultimately leverage it to make trusted organizational decisions. From customer and employee information, to product lists, supplier profiles, and article or asset records, organizations depend on master data to carry out crucial tasks across many lines of business. The accuracy of this data, therefore, is paramount to a company’s efficiency, compliance, and bottom line. However, gaining a 360-degree view of master data is easier said than done.

When you get a group of a data-minded people together – we talk about, well… data. We can’t help but live and breathe it, because it’s everywhere. As consumers of data, we have all come to expect organizations to use it to enhance our experiences with them or to improve their own efficiency and processes. Unfortunately, due to our heightened sense of awareness, it’s painfully obvious when that data is wrong, out-of-date or just plain bad.

It all started at ABC Company's Monday morning management meeting…

Is your organization’s data a lot like a bad relationship? You started off aligned, complete and everything seems to click with immense accuracy. But over time, maybe just a few years in, things that once seemed so easy, get more complex. The time you want to spend “enjoying” your relationship data is overshadowed by the large amounts of time you spend troubleshooting, problem-solving and trying to figure out why it seems like your data wants to leave you broke and unhappy.

One of the key differences, and I’m being very transparent here, is that SAP® NetWeaver™ Master Data Management is no longer going to be supported by SAP in the future. Everything is moving over to SAP Master Data Governance. From an apples to apples comparison of what is available to do in there the SAP NetWeaver Master Data Management platform is primarily used for you to start from scratch, build out what’s going to be governed, and how it is going to be governed. From a Master Data Governance (MDG) perspective your customers, suppliers, materials, products, financials, and assets are now provided to you with the best practices for your industry to start to put in place the foundation. In turn allowing you to now integrate them into that what your key business rules are, workflows, and approval process that you currently need to follow for your practices. It really moves you ahead much further in the governance practices right out of the gate.

What is Data Governance?  

Data governance is defined as management of data to validate for accuracy as per the requirements, standards, or rules that an individual organization needs for their individual business.