Using Data Issue Management to Solve Problems and Drive Value

In the early days of data governance, the main driver was regulatory: being able to certify that an organization knows where its critical data assets are, has defined ownership for those assets that can attest to their validity, and ultimately avoid risks and fines.  As data governance has evolved, however, it has become much more about driving productivity, re-use, visibility and understanding of relevant data sets to help the business perform and enable business transformation. This is well documented in our data empowerment webinar series provided by Informatica customers.

Data governance is something that every organization needs, but since it is largely a process and people undertaking, supported by enabling technology to collaborate, automate and scale, it can be hard work.  This is because at its heart, data governance is about changing the way that the company manages its data—a paradigm shift in strategy, thinking, and behavior.   And this is where many programs struggle—where to start when seeking to “change the world”?  And unfortunately, those programs that do not take an approach that’s oriented towards driving both tangible short- and long-term business value can run the risk of ending up on the scrap heap of discarded initiatives in the ever-changing world of boardroom priorities.

Before the higher purpose of data governance can be achieved, these programs need to establish credibility and deliver value.  Plenty of excellent guidance has been written about the importance of engaging the business, using a value-driven framework to define opportunities, and the importance of defining and measuring success.   Here we’ll talk about an additional, essential process for driving the needed credibility and value in your program while also operationalizing governance in a structured and repeatable way that engages your business:  Data Issue Management.

Data Issue Management

When starting a data governance program, there are several core processes that should be focused on to establish the foundation for your program.  Near the top of this list is Data Issue Management.  This is related to, but different from, the typical incident management processes that most IT organizations have through ServiceNow, JIRA, or other tracking and resolution processes.  DAMA, the global organization of data management professionals, defines Data Issue Management as:

“Identifying, defining, escalating, and resolving issues related to data security, data access, data quality, regulatory compliance, data ownership, policy, standards, terminology, or data governance procedures.” 

In short, the Data Issue management process addresses “the hard stuff” – pervasive problems that have no easy answers, and often involve solutions ranging across process, people, and technology areas.

Whereas “incident” management seeks to raise and resolve one-off problems and mitigate their immediate impacts, Data Issue Management is the structured process by which your data governance program will consolidate those incidents and use root cause analysis (RCA) to analyze and resolve what are often long-standing data frustrations.  This also provides an opportunity to exercise the problem solving and prioritization mechanisms you’ve put in place in your stewardship and governance teams.  Being able to easily demonstrate how wide the impact of those issues permeates across the business can be a powerful way of securing the right people and resources to effectively address these issues.

The chances are, that if you have a governance program of any form, you are already trying to manage a list of issues. They could include:

• Operational issues arising from inconsistent use of data across processes
• Customer experience issues arising from bad or missing data
• Poor data quality leading to delays in order fulfillment or revenue recognition
• Inability to make reliable business decisions due to incomplete data for analysis

The Data Issue Management Process

The basic process of managing issues includes the following basic steps:

• Raise issue
• Assess & Confirm
• Establish Impacts
• Analyze Root Cause
• Propose Solution

Figure 1.0 below depicts this basic flow with a bit more detail.

Remember, the goal of the Issue Management process is to solve data problems, and in doing so, drive benefits for the organization and establish credibility for the data governance organization.  It is therefore critical to focus establishing the value/cost of these data issues.  These can take several forms:

• Incremental costs
• Lost revenues
• Reputational risk
• Operational inefficiencies

Do not make the mistake of failing to quantify these impacts for each Issue the program takes on.  By establishing these impacts for every issue, the program builds a portfolio of opportunities to drive value creation for the organization, subject to the prioritization of the Data Governance Council. 

In other words, Data Issue Management also provides a perfect opportunity for disparate or siloed business leaders to coalesce support around a common set of business priorities for remediation, based on factual evidence as established by the data governance program.  And as people hear about the success your program is having in resolving these issues, you will find a steady pipeline of opportunities appearing.

Roles in Managing Data Issues

Data Issue Management is a process typically managed by the core Data Governance program leadership.  There are two key roles within the program that are directly involved:

• Data Issue Coordinator – this is the person within the program who performs the initial onboarding of potential issues to be managed, assigns an Issue Owner, and tracks progress.  This person may be a lead Data Steward representing a given LOB or function or may be a dedicated program role in larger programs.
• Data Issue Owner – the person who is assigned to lead the investigation and Root Cause Analysis of a specific issue.  This person may also be a named Data Steward, or someone else in a line or business or function who has knowledge of a specific issue to which they have been assigned the owner.

Growing Data Governance Maturity through Data Issue Management

Once the foundational Data Issue Management process is established, the program can turn its attention to visibility, automation, and scale through tooling.  The Informatica Axon platform provides a great medium for establishing the relationships between business and data concepts, allowing for robust impact analysis that can be seen by the whole organization. 

This process in Axon leverages Change Request and Project tracking features to automate workflows and create a library of known data issues that are associated to the various systems, data sets, reports, business processes, organizations, and initiatives that they impact.  

Figure 1.2 below depicts the additional detail of how the Data Issue Management process is implemented in Axon:

As your Data Issue Management process gains momentum, you will find that the root causes of these issues lead down many separate, but related, pathways.  It is very common to discover that, say, an issue with bad product prices on your ecommerce site is actually a mixture of process, training, and technical issues…often stacked on top of one another due to years of short-term fixes or lack of funding.  With a robust portfolio of data-related issues and Council-level sponsorship, your governance program should be able to bring the right resources to bear on solving these issues once and for all in the annual budgeting process.

From an operational perspective, the Data Issue Management process also provides a natural linkage to other teams and resources in the company who can work together to resolve issues both short and long term.  These could include:

• Technical issues:  the IT demand management/AMS process
• Process & policy issues: the business process improvement team
• Training & skills issues:  the HR-based employee training/education program

At higher levels of maturity, good data governance programs align efforts with these groups to coordinate a holistic and programmatic approach to resolving the underlying causes of data issues.

Conclusion

By taking an approach that seeks to establish value through problem solving, a data governance program can quickly build credibility.  The key is to do so in a disciplined and structured way, utilize root cause analysis and value quantification, and to leverage the Council you have engaged to drive the prioritization. 

For more information on Data Issue Management, check out this article on the Informatica Network. 

Need help driving value, building a roadmap, or designing your data strategy, program, and processes?  Contact Informatica’s Advisory Services group for an initial consultation.