3 March 2022

5 Data Quality Mistakes in Microsoft Dynamics 365 ERP to Avoid

While several organizations have moved towards adopting data governance practices to improve their data quality, there are still several others to catch on to this practice. In fact, based on a 2020 annual global data management benchmark report released by Experian, 49% of the interviewed organizations (1100) did not consider their CRM/ERP data to be clean enough to leverage.

If you are also of the mindset that your ERP data is not clean enough to leverage, then this blog is a good starting point to the journey of improved data quality.

In this blog, we will cover:


The six integral pillars for good data quality

In a nutshell, what defines good data quality?

The essence of good data quality is defined by six integral pillars which are the completeness, accuracy, validity, integrity, timeliness and consistency of the data. These six core data quality pillars or dimensions are important to adopt as part of your data governance strategy.

For managers and decision-makers, good data quality is paramount for paving the way forward. If the data quality is based on the six pillars, the chance that they make a wrong assumption which impacts decision-making, based on the quality of the data is lower. So, for better decision making it is important that your data is clean because we believe that “garbage in, is garbage out".

5 most common data quality mistakes in Dynamics 365

If you can adopt the above six pillars of data quality in Microsoft Dynamics 365, and can avoid the most common data quality mistakes, you will be well on your way to achieving data transformation.

1. Lack of data governance checks


The most common data quality mistake is human error which is expected. But you can prevent as much human error as possible by having good data governance. Implementing a data quality tool for your ERP can prevent errors being made by manual data entering.

Let’s look at an example to understand this better. Let’s say you have a database with customer records, and you want to share an email campaign with all your customers to share some discounts and offers to increase sales. If you set up a check that only name@website.com can be created with the flexibility of domain types, add a validity check for the email address, and make email addresses a mandatory field, you can ensure the email addresses are accurate and records are updated.

If you have an accurate and complete list of customer email addresses, that process automatically improves the chances of success of your customer campaigns.

2. Obsolete data


When the data is not updated or checked regularly, what we are left with is obsolete data. Data becomes obsolete when it is redundant and cannot be used anymore. Therefore, that data becomes incomplete and needs to be updated with the latest information. For example, when there have been several upgrades to a software (say V4.0) but the docs of the software are still showing V1.0 information, the information becomes obsolete if that version has been discontinued after the latest upgrade.

One of our customers for example, had a lot of ERP migrations and dealt in spare parts. In their records, they had a lot of items named ‘do not use’. So, for example, if a new employee needs to decide which spare parts need to be ordered, they would have to check the records. They would have to sift through 10,000 records, out of which 2000 was obsolete data, so making a decision was difficult and was time-consuming since they did not have a data governance system in place.

3. System challenges

When an organization has several systems that they work with but aren’t aligned there could be issues with data synchronization. Then there is probably a lack of consistency and duplicate data records. For example, if within your organization, you have different verticals, and one vertical uses a different CRM system from the others. And, there is no alignment between the two CRM systems then that could be an issue for reporting or analytics.

And if you decide to change one of your CRM systems for example, there is a risk to data quality as the existing data will have to be migrated to a new software. It is possible to lose data, or data could get mixed up during migration. Due to the complexity of data migrations, they are largely dependent on the IT team to supervise data checks. And if this process depends on manual checks, then there is a high chance of human errors.

4. Siloed information

When you have several touchpoints of data entry without a team to manage the master data – that is a recipe for disaster! There could be duplicate information updated by several teams, syncing issues, invalid and missing data points without any data governance.

If you have a master data tool and data stewards managing your data, you could establish rules, validations and have one team pushing the data to the rest of the company. It is essential to have one entity monitoring the accuracy of the data and setting up data governance.

5. Lack of knowledge of processes


Sometimes people who manage the data governance don’t have any insight on order processes or how different teams who enter the data really work. And they sometimes make incorrect assumptions that could impact data rules or data output. They might set rules that are too stringent which makes it difficult for the end users to enter anything in the system. For example, if they make it mandatory to enter every detail in an order processed for it to be saved, it could delay the order if some customer details were missing.

When data stewards do not align with end users that really use the system and work from the back-end just from their data quality perspective, making everything as watertight as possible, there is a lack of flexibility for the end users. Because of this, the end-users tend to enter data in an excel sheet manually to keep track of details and that process goes unmonitored.

So, for good data governance, it is important to align with the end users who use the system. They should discuss rules and validations with the teams on which items need to be mandatory for example.

5 most common data quality mistakes in D365 and their consequences

 

What are the results of poor data quality?

If you are making the above data quality mistakes, your organization will suffer from the following consequences that will impact your bottom-line and ROI.

Here are some of the most common results:

Unimpactful decision making

With lack of data governance and teams working in silos, your data records will lack majority of the six integral pillars of good data quality. And that would negatively impact decision making and planning.

Data management is time consuming

If you have several inconsistent data points to analyze without proper syncing of systems that could make data management extremely time-consuming. It would also be difficult to pinpoint the right data point in such cases.

Data is not streamlined

When you have data stewards that lack knowledge of processes or teams that work in silos, there is misalignment of teams and lack of streamlined data processing.

Data cannot be analyzed

When you are dealing with obsolete customer records, system challenges, and siloed information, analyzing data would be a futile task to take on as the data records would lack consistency, accuracy, validity, and timeliness.

How to improve data quality in D365 in 3 simple steps?

Step 1: The first step to improve Data Quality is initiating data governance processes. Also remember that good data governance needs to be aligned to all the teams and existing processes.

Step 2: Next, is to outline a strategy you will use in order to have good data quality in the system. You need to have a plan and structure in place and data governance before you implement a data quality tool.

Step 3: Look for a tool that fits in the data governance strategy you want to apply. So, let’s say you want to have really strict data entry you need to then identify the correct tool for that. Or if you want more flexibility then there are tools available for that too.

Good Data Quality =

Data Governance processes +  Data Governance Strategy + Data Governance Tool

Since the first two steps will vary based on the organization’s priorities, we have covered some general pointers below to help you with Step 3.

Things to look for in a good data quality solution

A functional tool

When choosing a data quality solution, it would be helpful to choose a tool that has functional capabilities. What we mean by that is, in any system, the developer can make rules for a field, let’s take the example of the email address field. So according to the set rules, it should always be name@email.com and you cannot add a comma – this can be done by a developer.

But in the future if you decide to change that field or it has been blocking, a change will be needed to be made so that data can be entered differently. But since you’re stuck with the code, you need to approach the developer again, specifically in Dynamics 365 for the next release to change that code. But with a good data quality tool, let’s say Data Quality Studio or another functional tool, you can change that same rule without the need of a developer.

Flexibility

If you have a flexible strategy, then it is key to have a good degree of flexibility in your data quality solution. Your data quality tool should allow you to restrict certain data fields or add rules wherever needed. And incase, something goes wrong you should be able to revert those changes so that you are not restricting the end users. If data entry is being held up in the system due to a validation, your data administrators should always be able to turn it on and off.

Avoid erroneous entry

If you can prevent incorrect data from being entered in the system by adding validations to check for accuracy of the data, you can make better decisions and increase business revenue. A good data quality tool that helps you prevent errors, eventually will justify the cost of the software, if it can increase your ROI in the long run.

Start improving your data quality today!

If you do invest in a good data quality solution for your ERP, you will witness benefits such as better decision making, improved ROI, improved customer satisfaction, and streamlined processes. But along with a good data quality system, it is important to have data governance and a strategy in place, prior to adopting a data quality tool.

Once you start your data improvement journey and have data governance and a data management plan in place, you are halfway to your data transformation goal.

When you do decide to invest in a Data Quality tool for Microsoft Dynamics 365 and begin evaluating options, then read our factsheet that highlights the benefits of To-increase’s Data Quality Studio solution.

Improve data quality and achieve unmatched business success

Download Factsheet
Data-Quality-Studio-factsheet-thumbnail
Kevin Rahan,
Kevin Rahan,
Delivery

Also interesting