Implementing new business applications is a massive investment that requires spending significant sums defining business requirements, defining terms, configuring the solution, building RICEW objects, cleansing data, transforming data. After working on dozens of implementations, we find that organizations frequently overlook using the implementation as an opportunity to upgrade data governance to fuel long term data performance.
Upgrading data governance as part of the implementation doesn’t need to be a big lift and can be accomplished in bite-sized chunks throughout the project timeline. If an organization doesn’t consider data governance beyond the new application controls, the data will only be as good as the application was configured. This myopic approach can lead to disaster. We’re currently working with a Fortune 500 client who implemented a new HR solution without Premier’s guidance. Their implementation lacked the governance to control the data within the new system. After go-live, it quickly became impossible to get a trustworthy report from the new system in a timely manner. After several months of untangling the data and working with the business to redefine what its requirements should be, they’re finally closing in on trustworthy reporting.
The lack of governance applied to this HR solution hindered the company in two primary ways. The first is that they were unable to generate trustworthy reports for months. This challenge made it impossible for them to meet the basic HR reporting requirements for a large enterprise. Additionally, being focused on tactical data cleanup and definitions also prevented this department from being able to use the data to drive the organization forward.
The firm now has clean definitions for many of its attributes and is developing training materials to help ensure that divisions across the enterprise are using the attributes and the application consistently. The business requirements can be used to update the application and execute ongoing quality reporting. Most importantly, the governance they’re using will enable the focus to shift towards improving HR rather than trying to generate standard reports.
To avoid the pitfall this HR department encountered, the following activities are a good start to upgrading data governance during the implementation without impacting the timeline or licensing another piece of software. While the following sections may seem overwhelming at first blush, these activities can be spread out across the entire implementation and even after go-live. The important part is that they get scheduled while data is on everyone’s mind.
Kick off Data Governance - If you don’t have an active data governance organization within your firm. Set some time aside towards the beginning of the implementation to discuss data governance, what it is, and how it can be used to improve everyone’s work. If you have active data governance, it’s a great opportunity to talk through the project with the governance team and project teams together. Outline the program's goals, how governance can help with implementation, and how business can continue after go-live. The important part of the discussion is to get people thinking about data governance.
Document the Gaps and Define the Best Practices - Identify current data governance strengths, weaknesses, gaps, and risks, and define future state data governance best practices. While working to identify the gaps and best practices, start to identify the key people who will be the driving force behind ongoing data governance. The exact roles vary from organization to organization, but some common roles are executive champion, data governance manager, data governance council, and data steward.
Define a Data Governance Charter - Similar to the program charter that is created as part of the Oracle implementation, create a data governance charter to keep everyone on the same page about how we’re going to make sure data is enabling the organization to meet its needs. The charter usually contains the following sections: problem statement, responsibilities, goals, benefits, scope, assumptions, dependencies, activities, deliverables, risks, and critical success factors.
Collect the Metadata - There will be many meetings discussing what is an active customer, what is a line of business, what is an item type, and how to classify an item for a given item type. Don’t lose that knowledge in the opacity of the system. Make sure the decisions are documented and create a library that contains the terms, requirements, and all the metadata that you can. This will speed up future development and the potential future implementation of governance software.
Build out the Organizational Framework - Formalize the resourcing decisions made while documenting the current gaps to create the data governance organizational framework. Identify each of the important roles that will make up the data governance council and office and their responsibilities.
Assemble a Data Governance Handbook - If your organization doesn’t already have a handbook that contains the organization’s policies, procedures, roles, and responsibilities for data governance, define what should be included in your organization’s data governance manual. This handbook is the go to authority for all things data governance. It can be quite difficult to create, but it doesn’t have to be done in one go. It is also a living document that is meant to evolve with the business over time. The handbook frequently contains the mission, guiding principles, organizational framework, roles, responsibilities, communication framework, metadata catalog, policies, standards, metrics, processes, tools, and resources. If there is something data governance related, it or a reference to it should be found within the handbook.
Create a Communication Matrix - Effective communication is critical for a successful data governance program. To clarify communication protocols, create the protocol for how to communicate data governance policies, procedures, issues, and updates. The one created by Robert Seiner, with his non-invasive data governance approach, is excellent. It is recommended to work on defining the communication matrix during the initial set-up of the data governance organization, as it provides a mechanism for defining a clear communication structure — heading off the game of telephone before it starts.
Define the Data Governance Scorecard - Define the metrics and mechanisms that will let you know if data governance is effective at the organization. These metrics align with General Accepted Information Principles (GAIP) and measure data infrastructure, security, quality, and financial goals. They can be about reducing the number of legacy databases, increasing employee skills, and making sure that the costs are in line with the committed return on investment.
Build out the roadmap - Chances are, at the time of go-live for the main implementation there will be many activities and sections that still need to be completed or updated within the governance handbook. Create a roadmap to keep improving and building data governance operations. This is a journey and not a project. Defining the next steps will continue to provide a vision for how the organization can better serve its constituents.
Establish data governance council meeting cadence - Getting the data governance council together is important to keep the data improvement momentum going. During the first meeting, show the group where the data has been, how far the organization has come, and where it is going. The format for the meeting depends on your organization's culture. The important pieces are that all data related initiatives are discussed, and issues are identified, discussed, and solved.
While these deliverables may seem like a lot, if they are spread across an implementation or at least planned to be executed at the tail end of the implementation, they are manageable. Additionally, you will be able to get more out of your new system and recognize benefits over the long term. We see that organizations that have even a minimal data governance operation at the time of go-live have the mechanisms in place to measure what’s important, communicate issues, and quickly come up with resolutions.
If you have questions about data, data governance, or data migration, let’s spend 30 minutes together to go through your questions and approach on your project.
