Data governance has become critical for organizations to ensure usable and accurate data. But it also requires collaboration across multiple teams and stakeholders, including data teams, IT, legal, compliance, risk management, finance, and different business units.
Cross-functional collaboration in data governance is important because it allows organizations to break down silos and create a shared understanding of data assets, policies, and processes. It ensures that everyone involved in data management is aligned, understands their roles and responsibilities, and has access to the correct data to make informed decisions.
Cross-functional teams can identify and mitigate risks by working together on improving data quality, ensuring compliance with regulations and policies, and driving better business outcomes.
However, despite its importance, many organizations need help to achieve efficient cross-functional collaboration. Here’s a list of the most common challenges data teams and business stakeholders face:
Lack of communication and alignment
Lack of clear ownership and accountability
Conflicting priorities and resistance to change
Lack of data literacy
Poor data management tools and processes
This article explores some of the most common challenges in data governance for cross-functional collaboration between data and business teams and provides actionable tips to help you overcome them.
While data teams manage and protect an organization’s data, business stakeholders rely on this data to evaluate their performance and make better-informed decisions. Yet, both parties can struggle to communicate with each other. This lack of shared understanding leads to misaligned expectations.
For instance, the data team may create policies and processes that are too complex or extensive for business users, such as having to fill out cumbersome data access requests. This makes it difficult for relevant business stakeholders to access the data they need when they need it. Frustrated business stakeholders may preemptively export data to spreadsheets and create their reports with their own calculations and a new source of truth — every analyst’s worst nightmare!
Another common challenge is when business stakeholders change their KPI definitions or data sources without informing the data team. This leads to broken data pipelines, inaccurate data models, and incomplete dashboards, ultimately breaking the critical data metrics that business stakeholders use to monitor their organization and make business decisions.
Here are three solutions you can implement to resolve communication issues between the data team and business stakeholders:
This can include monthly or bi-weekly status meetings, monthly data governance reviews, or quarterly business reviews.
Meetings between data and business teams are an excellent opportunity to communicate changes to core KPIs, data models, or tools. They allow the stakeholders involved to anticipate the changes and adjust their metrics, sources, or models accordingly.
Data teams and business stakeholders can improve collaboration by implementing shared documentation. They can access all data-related documentation in a centralized location, leading to more transparency and better information accessibility.
This usually involves the data team maintaining a data catalog and then publishing relevant metrics and documentation into a business glossary. Here, each team (e.g., sales) can easily see a short list of all metrics relevant to them, with definitions and links to dashboards and data for further exploration.
When business stakeholders work with business data, they can avoid confusion or having to reach out to data team members because they can easily reference the shared documentation.
Data teams can organize training sessions to ensure business stakeholders understand the data they capture and how to use it effectively. It’s even possible to include training sessions about using data analysis tools, implementing data visualization techniques, or using best practices for data management.
Effective data governance requires the implementation of tools and processes to manage and protect data. By working with the right data management tools and processes, data teams can enhance inter- and intra-team collaboration, reduce security risks, and derive more value from their data assets.
Data quality issues are possible indicators of poor data tooling and procedures. For instance, data inconsistencies, missing data, inaccurate data, or duplicate data highlight the potential need for better tools to manage your data. Note that this is often one of many problems.
Poor data tools are slow when processing data and prone to crashing. When dealing with big data, this can slow down your data teams and your company’s ability to make fast decisions.
A final red flag is high data maintenance costs incurred through manual data processing, for example. This is resource-intensive and takes time away from the data team that they could instead be using to build further data products.
So, how do you select the proper data tooling?
Here’s a quick list of tips to select the right data tooling:
Pick data tooling that centralizes data access and enables collaboration so that all teams can access and share their data.
Adopt data tools that automate the data cleaning, testing, and updating processes to reduce the risk of inconsistencies.
Find data tools that allow you to define clear roles and responsibilities for data lifecycle management as well as clear owners for each data asset to ensure accountability.
Select data tools that allow you to monitor data usage and identify potential risks and opportunities for improvement with data tests, alerts, and data lineage.
Data teams tend to face resistance from business stakeholders when implementing new technologies like data observability tooling and data catalogs or new processes such as data governance frameworks.
This happens due to a lack of data literacy on the business side, which occurs when business users need to gain the necessary knowledge and skills to understand, analyze, and use data effectively. As a result, they may be hesitant to adopt new data technologies or processes. Or, they may not see the value in investing time and resources into improving data management practices.
Resistance to new data technologies and processes, as well as a lack of data literacy among business stakeholders, can lead to several challenges for data teams and organizations as a whole.
Slow adoption and integration of new data technologies or best practices
A lack of trust in the decisions derived from their data by business stakeholders
Data silos or fragmentation as different departments will start using their own data tools, sometimes even in parallel to the existing tooling implemented by data teams
Increased risk of data-related security incidents
Unreasonable data requests and friction with the data team
So, how can you overcome resistance and improve data literacy?
Inform the company before implementing new technologies and educate them on how the upcoming changes will affect the output provided to them by the data team.
Have open discussions with business stakeholders before making changes to address their needs and concerns. These meetings provide valuable insight into challenges or roadblocks and allow you to plan and budget accordingly.
Provide workshops for business users on how to effectively use the company’s data tools and how to request assets from the data team.
Empower the keenest non-technical employees by hosting “Data 101” training sessions to equip them with basic data management and governance concepts. For example, these can be Excel power users or those with basic SQL knowledge. Education is needed to solve the lack of communication and alignment.
Foster a culture of data-driven decision-making. Focus on how data-driven decision-making can help employees to be confident in their business decisions and improve their skills. It’s a win-win.
Use data visualizations and dashboards to make abstract data tangible for business stakeholders. Let them experiment with these tools to make it easier to derive value from the data. (Note that it’s important to foster a data culture first.)
To ensure effective data governance, organizations must foster streamlined collaboration between data teams and business stakeholders. Effective cross-team collaboration can improve data quality, efficiency, and decision-making. These improvements should result in increased innovation as the new data technologies and processes lead to new insights and opportunities.
However, setting up data governance processes won’t be of much help if you lack buy-in from stakeholders and team members. Education, training, and open conversations are crucial to increase data literacy, streamline communication, and improve cross-team collaboration.
What’s more, choosing the right data tool can decide the effectiveness of your governance and collaboration strategies. By investing in the right data tooling and ensuring that it aligns with the organization’s goals and needs, a business can maximize the value of its data assets. A tool like Y42 can help you in your journey to increasing the value of your data.
Book a call with our data experts to learn more about making Y42 the core of your data governance framework.
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