According to a report by Dun & Bradstreet, in the next 60 minutes, 211 businesses will move, 743 new businesses will open their doors, 429 business telephone numbers will be changed or disconnected, 12 businesses will file for bankruptcy, and 13 companies will change their names.
Imagine what this could mean for the data in your Dynamics 365 ERP. When data is constantly changing and evolving, it is challenging to keep it accurate. If your organization is struggling to use data effectively, manage risks, and reduce costs, implementing data governance becomes critical.
At To-Increase, we’ve been assisting organizations with improving operational effectiveness and customer satisfaction by delivering consistent and reliable data. Our data governance solutions enable data stewards and data owners to efficiently manage the creation, validation, and distribution of master data within Dynamics 365 F&SCM companies and across external systems from Dynamics 365 F&SCM.
Data governance is not only about minimizing data risks but also about maximizing data usage. You probably have collected lots of data already, but if you want to leverage it properly you need to know how to govern the data.
But data governance does come with its set of challenges. While these challenges are not the same for every organization, there are some that are common. This article will introduce you to data governance and its common challenges.
What is data governance?
Data governance is defined as a collection of policies, processes, roles, standards, and metrics that ensure the efficient use of information to enable an organization to achieve its goals. Data governance decides who can take action upon what data, in which situation, and using what methods. (Bryant Bent, Data Governance for Modern Businesses and Practitioners)
A good data governance solution will cover the following four components:
- Data distribution and integration
- Data quality
- Field security management
- Data entry workflow
Did you know that 71% of business leaders attribute improved customer retention rates and 58% attribute an increase in revenue to good data governance? (Harvard Business Review, 2020)
Implementing data governance isn’t easy. As the volume and variety of data increases, challenges with efficient data governance will only get more difficult.
6 Common challenges in data governance
Challenge 1: Lack of data ownership
Data governance is IT’s responsibility! Have you heard this before? Many organizations believe that data governance is the responsibility of the IT team while others are just users of data. This is a myth that needs to be dispelled.
Expecting IT to devote resources to data governance puts a lot of pressure on one team as they do not have the time to manage all the data.
Solution: While IT is responsible for the technology required in producing and using data, business users are to be made responsible for working with IT to define data requirements and use it for decision-making purposes. As a best practice, get your senior management team’s buy-in before implementing data governance. Everybody that uses data must be held accountable for how they use the data, and this is not the sole responsibility of the IT team and data stewards. It is a good idea to have a team in place to drive your data governance initiative.
Challenge 2: Unintentional data silos
A data silo refers to stored data that is available only to a few teams, business units, or individuals and not to the entire organization. Data silos are often created unintentionally. For example, the product management team could have multiple spreadsheets with product information and a similar spreadsheet could also be used by sales and marketing. The result — different teams could end up having different versions of product information.
Solution: Your Dynamics 365 F&SCM ERP allows data exports to break down your data silos. But you need to ensure that no new data silos are created. With master data management and data governance solutions in place, you can ensure that a single source of truth is provided for the entire organization.
Challenge 3: Dealing with poor data quality
Organizations that have legacy on-premises systems most often must deal with bad data that is not standardized and categorized. Instead, it’s spread across various on-premises systems.
Consider the example of a retailer that stores online sales data in one system and brick-and-mortar sales data on another. In one system the income received by the retail company is called revenue and on the other it is termed sales. Now imagine the challenge if the company decides to run analytics on its total income. Although both revenue and sales are data fields that refer to the income of the company, there is a confusion as the metadata is different.
While this was just a simple example, poor data quality can cause bigger issues when it comes to sensitive data and its treatment.
Solution: Data quality is the cornerstone for data-driven organizations. While data quality tools can help correct past mistakes, a proper data governance framework can ensure that data is standardized, and the right people are assigned to work on the right processes for maintaining data quality. It also takes care of setting the right rules and processes to gather trustworthy data.
Challenge 4: Lack of data control
Lack of control over enterprise data is one of the most common data governance challenges. The sheer volume of data available in organizations today makes it difficult to find the right data and control access to it. In the absence of data control, duplicate and inaccurate data (caused because of human error) increases, resulting in mass communication of incorrect data, misinformed decisions, skewed data analysis, and business process inefficiencies. For instance, if an organization has branch offices in multiple locations and employees in each office are making changes to legal entities because there are no data control policies in place, this could snowball into bigger data management challenges.
Organizations want to ensure that the right people oversee the creation of master data, and they also want to be able to control the distribution of this master data that is created.
Solution: A data governance framework can specify how and where data is used. Global organizations that have offices in multiple locations need to control global and local data. Different permissions can be set depending on whether the business user needs to access global or local data. When you want to prevent specific master data fields from being changed locally, you can specify which group of people should not have access. But doing this with the standard security feature in Dynamics 365 F&SCM is a hassle. A data governance solution can let you control who is adding data to certain fields and enable central governance.
Challenge 5: Following a one-size fits all approach
With an increasing number of organizations moving to the cloud, data governance programs built for on-premises IT infrastructure cannot be deployed for cloud infrastructure. Within a cloud there could be different storage solutions that contain different data structures (e.g., files, tables, images) and this makes governance difficult.
Solution: Cloud data governance requires a new design and implementation. Cloud services can be classified into three delivery models: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS).
Depending on the model, business users will have different levels of control over their data and each model will require a different approach to data governance.
Challenge 6: Limitations of data integration and distribution in D365 ERP
When an organization has multiple internal systems (e.g., CRM, ERP, HRIS), or is going through mergers and acquisitions, there are bound to be problems with data integration and distribution. Standard Microsoft Dynamics 365 tools have limitations when it comes to data integration and distribution.
Solution: A data governance framework helps you create a unified view across the enterprise and enables a single source of truth. Data governance policies dictate how technology and solutions are used.
Who needs data governance?
If your organization does not anticipate any changes to master data or is not responding to dynamic changes in the marketplace, then you do not need data governance. If your organization runs a simple business, does not have multiple branches, and is not dealing with critical information then you do not need data governance.
Let’s assume you are a global manufacturing organization with branches in multiple locations. If you do not communicate your product specifications clearly across all branches and do not have data governance policies in place for data control, then it could result in lost opportunities or business losses. That’s why it becomes important to implement data governance properly.
Any organization that needs to have master data updated to meet ever-changing business needs, will need data governance.
How can you properly implement data governance?
Large organizations using Dynamics 365 ERP usually have a data governance strategy in place and follow these best practices for master data management implementation. In smaller or mid-size organizations this may not always be the case. You may need to set up your initiative for one company within D365 F&SCM before moving on to the next.
You can always talk to an expert to understand the options available to you to kickstart your data governance initiative. As you get started with your data governance initiative, you may be required to address some of these challenges and a few others, head-on. Rather than using manual effort to streamline your data, consider automation with MDM solutions that take care of mundane governance tasks and allow you to focus on the more important tasks that fuel organizational growth.
To find out how To-Increase’s data governance solutions integrated within Dynamics 365 F&SCM can help your business, download this factsheet.