Many companies don’t see anything wrong with their data quality, management and governance processes. But upon examination, this often isn’t the case. It’s easy to forget how big of a role humans play; your people are an important cog in the stream of data. They contribute to these streams and if they’re inconsistent in their approach, it can lead to issues further down the line.
I talk about this topic in more depth on an episode of Migration Minded, a podcast series created by the D365 Migration Community. Hosted by Tom Wisniewski, Microsoft’s Global Product Marketing Manager for Dynamics 365, he asks me about cloud migrations, the importance of change management, data management and more.
Listen to the episode below (or watch it here).
Address fields: The perfect example of data inconsistency
A common one I’ve seen is address fields. For example, your business may have multiple systems - from ERP and payroll to commerce and CRM. All of these systems will contain different elements of data - like addresses. This is a piece of data all of these systems will hold.
But depending on the format of the system, these addresses will be held in different structures. In Dynamics 365 Finance & Supply Chain Management (D365 FSCM), street name might be in one field while in Customer Engagement, it might be in three.
Both are Microsoft products, part of the same D365 family, and yet, addresses are held in different structures. Imagine if it’s a third-party system! There may be different fields or field lengths, for example, so when you move your address data out to this external system, it can cause conflicts.
I’ve seen some businesses hold credit control notes in street three. Obviously, it’s not street three, it’s a note. But not everyone will use (or need to use) the street three field. So, when you go to extract this data, you’ll find that someone lives at 1 Blogg Lane, London, not a good payer.
That’s why it’s important to have a fundamental approach to data holistically within your business. Data is integral to what you do - it’s used for reporting, business decisions, planning and forecasting.
It’s even more critical in this modern era where businesses can move to the cloud and access tools such as dashboards and PowerBI to view real-time data. Modern technology has made it possible for data to be consumed more immediately so it needs to be accurate and reliable.
How to overcome this
You need to conduct a fundamental review of the data. Look at everywhere the data is used within your businesses.
Let’s start with data governance and profiling. Is there a master data management structure in place? There are tools that can help you bring all of this data together so you have a centralised master data. All of your systems can then ‘pick’ from that central system which can lead to more consistency of data because it’s coming from one place, not 10.
From where you’re using this data and how it flows through the systems, you must know what structure it needs to be in. In other words, your data architecture. Data management controls will allow you to align these different systems so you cleanse the data (which generally happens through an implementation), perhaps through external tools.
Within a week or two of live, if you don’t have a data governance policy in place, people will start putting data in the wrong place, wrong structure etc. And that can obviously unpick all of the progress you’ve made since go-live and impact the value your new ERP system can offer.
Stay as close to standard as possible, unless data governance is compromised
One customer I’m working with at the time of writing is a global organisation. Their European division uses 15 external systems. Think along the lines of e-commerce, warehousing, different shipping carriers, product information and finance. All of this data is moving around these systems, generally through Azure Integration Services.
Making all of the data talk to each other is one of the biggest challenges - addresses certainly being one of them! In legacy systems, they’re held in three different locations. We’re trying to bring them in while doing a rollout at the same time.
Keeping all of that data aligned with good governance is difficult. As a whole, we would always recommend businesses stay as close to standard as possible when it comes to their solutions. However, we customised some of the products to encourage the user to pick the right record - all in the name of data.
For example, in D365 Sales, many of the address fields are free type - so you type in those records. In D365 Finance & Supply Chain Management, it’s multiple choice so you pick them rather than type. We customised this so it can be picked in Sales. Now D365 Sales could communicate across to FSCM in a standard format.
It was as important that the data was standardised so we could communicate between the systems as it was for reporting. But ultimately, those elements fell into reporting anyway. So consistency of data and a team that can ensure that data is kept up to a good quality is critical for businesses.
Data is just one component that contributes to the overall success of your cloud ERP project
As you know, undertaking a cloud ERP project is no easy feat. Yet, it’s something that should happen if your business is to stay competitive and at its most efficient. Your data considerations are only one element that can ensure your journey to smooth ERP implementation and adoption.
In our guide, we cover the complete cloud ERP lifecycle, stretching from strategy to adoption and beyond. Learn the recommended best practices to follow and common mistakes to avoid, including how to leverage data to your advantage.
Download your copy below.