9 Reasons Why Data Quality and Data Governance Initiatives Fail
A Gartner survey revealed that more than 90% of all data governance initiatives fail. Let’s take a look at some of the most common reasons.
Join the DZone community and get the full member experience.Join For Free
Data governance is an opportunity for organizations to make their processes more efficient and drive business growth. What sounds simple can be quite a challenge. A Gartner survey revealed that more than 90% of all data governance initiatives fail. Let’s take a look at some of the most common reasons.
1. Reactive Data Governance
Many organizations continue to value data quantitatively instead of qualitatively. Data quality only becomes an issue when it causes poor decisions or when the company fails to comply with regulatory directives. Data governance policies designed at this stage are reactive. Instead, for data governance to be effective, it should be created proactively before a problem is noticed so that high data quality standards are always maintained.
2. Lack of Expert Leaders
Data quality and data governance may be connected but they are not synonymous. Data governance is also a whole new field as compared to data warehousing, etc. Hence, it cannot be managed in the same way.
Contrary to what you may think, data quality isn’t only about identifying rules and correcting errors. It is also about monitoring data continuously and designing processes that minimize the risk of error. It cannot be considered a one-time exercise that can be handed over to a third-party.
For a data quality and data governance program to be effective, you need people with relevant experience in the field leading it and working on it consistently.
3. Lack of Skilled Executors
Even if the program is designed excellently, it may not be successful if the right people are not involved in executing it. Executing Data Quality and Data Governance initiatives involves specific skill sets. The team working on data quality must have a business/analytic mindset, sufficient knowledge about business processes, and the authority to make decisions.
For example, if the person working on improving data quality does not understand your business processes, they may not be able to understand the impact of wrong data. Thus, if these prerequisites are not met, your data governance initiatives will not be successful.
4. Isolated Data Governance Initiatives
Data quality issues cannot be assumed to be the responsibility of only the IT department. It affects everyone and hence collaboration is key to the success of data governance initiatives.
All important decisions about these initiatives must involve the IT team as well as the Business teams. While the former focuses on technology delivery, quality improvement is an aspect the business teams must look into. Both teams must work together and maintain open communication channels so that data quality can be monitored and improved.
5. Data Governance Initiatives That Are Not Driven by Business
The business side of a company needs to be involved not only in initiating efforts to improve data quality but also in everyday processes. They need to be able to accept changes in procedures and implement the same correctly. Else, it could create even more problems. For the initiative to be truly successful, it should be viewed as a tool to help people reach their goals instead of being an additional responsibility.
6. Lack of Clarity
One of the reasons many data governance initiatives fail is because the people involved have no clarity on their roles and responsibilities. This makes changes slower to implement and leads to many quality concerns being left unaddressed.
Since there are a number of people involved in these initiatives, a clear demarcation of duties, ownership, and accountability is needed. This helps prevent chaos and duplication of efforts. For example, the IT team can focus on data profiling, data lineage, and usage while the business team looks into data quality trends, identifying root causes of poor data and making changes in systems and processes.
7. Unrealistic Timelines
Data quality and data governance processes cannot be viewed as singular actions. These processes can span years. Thus, assuming that your data quality will improve overnight is setting yourself up for failure.
Every step of the process from identifying tasks to defining rules, documentation, and implementation of the rules is important and takes time. One of the reasons, the process takes so long is because all the different departments that deal with data need to agree on the data quality measures. Hurrying the process along or not taking someone’s opinions into account can derail the entire initiative.
8. Opaque Systems
In terms of data quality and data governance initiatives, transparency refers to both the regulators and stakeholders being able to assess data, its uses, and quality indices. All parties must have confidence in the data else, efforts will be duplicated and no clear path can arise.
Ideally, the master data sources and reference data must be accessible across the enterprise. The use of automated tools to manage lineage, metadata, etc. also helps with transparency. If the system is transparent, issues become visible early on and can be addressed before they snowball into something bigger. Else, the issue may remain undetected and you will be unable to reach your data quality goals.
9. Unclear Goals
One initiative cannot solve all your data quality issues. At the same time, concentrating on a myopic goal can lead to wastage of resources and, in turn, lead to the plan being pushed aside before it can make an impact. For the initiative to be successful, it should have a well-defined goal and a plan for sustenance.
Every company has a different way to define its data quality goals. One way to do it is to concentrate on one type of data at a time while another could be to focus on the different stages of quality management. Either way, the goal must be defined and understood by all. It should also be attainable and measurable.
High-quality data is critical to every business and if you haven’t already started a data governance initiative to improve data quality, you need to do it now. To ensure its success, set realistic goals, hire the right people for your team, use the right tools and involve everyone in a collaborative effort.
Opinions expressed by DZone contributors are their own.