As the digital world continues to advance, so does the value and significance of data within organizations. This isn’t likely to ease up anytime soon either, as the sheer amount of data across organizations continues to grow drastically in terms of both volume and complexity. Therefore, data governance should be at the forefront of every organization’s digital strategy, as it is imperative to the quality, security and usability of company data.
However, the distinction between data governance and data management remains elusive; what skills are actually required for effective data governance within an organization? This article covers exactly what should be expected when ensuring effective data governance initiatives within your organization.
What is Data Governance?
Data governance refers to the overall management of data, which ensures the accessibility, usability, integrity, and security of the data used within an organization is adequate. To achieve this, the correct policies, standards, and procedures are established, to guarantee high quality data, management, and protection.
Moreover, data governance is essential for organizational strategy. For example, it can assist businesses in making informed decisions, maintaining customer trust, regulation compliance, and achieving overall organizational goals.
However, it also requires synergy between key professionals throughout your team. Collaboration is required between your IT department, data managers, and business stakeholders to establish a framework that ensures data is not only accurate and secure but is also used effectively to achieve organizational goals.
What’s the Difference between Data Governance and Data Management?
To put it simply, data governance refers to the overall strategy and framework for organizations to effectively manage their data assets. This involves defining policies, processes and roles to ensure data quality, security and compliance with regulations.
Data management is then what follows data governance. It refers to the active use of the practices, processes, and tools defined within data governance.
Furthermore, data governance will involve collaboration between business leaders and executives alongside data stewards and IT professionals, as it includes top-level management to align data governance initiatives with business objectives.
Once data governance initiatives have been established, data management will involve IT professionals and data specialists without the involvement of business leaders, as these teams will be responsible for the implementation and maintenance of databases and data systems.
Skills your Workforce Needs for Effective Data Governance
Data Fluency
While it’s true that data literacy plays a significant role in efficient data management, good data governance ultimately depends on data fluency. Data literacy provides the ability to read, understand and analyze data, however it’s the ability to convey data in a way that others can understand and then become fluent themselves, that makes the difference. It’s essential for all team members involved in data governance initiatives to have a solid grasp of all data concepts, to ensure they’re able to effectively identify data needs and gaps, evaluate tools, and communicate data insights and recommendations to other stakeholders.
Data Strategy
Once data fluency has been established within your data governance initiatives, your data strategy can start to be constructed. Your data strategy will effectively plan your data management process and provide the vision for how this can support and drive your organizational goals.
The success of your data governance efforts hinges on your ability to create a clear and coherent data strategy that aligns with the organizational strategy and culture. This is pivotal because your strategy shapes critical aspects of your data governance project, including setting data objectives and metrics, determining priority areas, and allocating resources and budgets.
Data Ethics
Data ethics requires the application of ethical principles and values to data collection, processing, sharing, and usage. It is crucial for data governance professionals to have a strong sense of data ethics, as to ensure that data is used and stored responsibly, fairly, and transparently. Additionally, data ethics also involves respecting the privacy and security of data and ensuring that all data handling within your organization complies with the regulations and standards within your jurisdiction.
Data Collaboration
In addition to having a deep understanding of data, organizations need to maintain ongoing collaboration surrounding data governance and management. This collaboration is crucial for ensuring that processes and regulations are consistently followed and adhered to.
To establish effective data governance, organizations need strong data collaboration skills and a culture that emphasizes efficient data management. This involves frequent engagement with internal and external stakeholders, including data owners, producers and users, as well as external partners like vendors and customers. Creating strong relationships and encouraging open communication regarding data are fundamental aspects of this collaborative initiative.
Data Leadership
Data leadership is important for impactful data governance, as it helps to motivate and guide organizations to the better utilization of data for decision-making and innovation. Exemplary data leadership skills are invaluable for data governance professionals, as they enable the showcasing of the value and impact of data governance to key stakeholders. Moreover, a proven leader is able to encourage the on-boarding of key skills, such as data literacy, the ethical usage of data and more.
Power Skills to Consider
Although technical expertise is crucial for effective data governance, this alone is not enough to navigate the challenges posed by vast and complex datasets. Equally crucial are ‘power skills‘ – a set of essential abilities that empower professionals to not just manage data but to transform and convey it effectively to stakeholders within the organization. This section provides several pivotal power skills that are reshaping the landscape of data governance.
Analytic Skills
Analytic skills within data governance are paramount for interpreting and creating value from intricate datasets. Professionals proficient in data should be able to delve into complex data and extract meaningful insights to support business decisions.
Language Skills
Language skills are indispensable in fostering a shared understanding of data concepts. Professionals fluent in data are able to create informative guides, such as glossaries and terminology guidelines. This helps to facilitate precise communication across departments and stakeholders, resulting in a reduction of gaps in communication and an increase in the efficacy of data governance practices.
Strategic Thinking
Strategic thinkers within data governance excel in assessing organizational needs. They meticulously evaluate the specific data requirements of the organization and strive to align governance practices with business objectives. This also includes the anticipation of potential risks associated with data usage, allowing for proactive measures to uphold data integrity, security, and compliance.
Furthermore, strategic minds will also integrate innovation into data governance, exploring cutting-edge technologies and methodologies, fostering continuous growth and providing a competitive edge.
Potential Challenges your Organization may Face in Data Governance
Understanding the Business Value of Data Governance
Organizations are continuously generating information, which creates an overwhelming quantity of data. However, for this data to become valuable to an organization, it needs to be presented or formatted in a way that decision makers can understand and use to support future business strategy. By actively harnessing data, its purpose and providing it in a digestible format, it can help to support improvement throughout all aspects of your organization.
Limited Resources
Navigating a data governance program with limited resources can pose significant challenges. Constraints in terms of funding, workforce, or technological infrastructure may hinder the implementation of comprehensive data governance strategies. Therefore, it’s important to allocate resources judiciously to critical areas, such as ethical and legal regulations and aim to find cost-effective solutions for workforce training and processes that will still enable you to achieve organizational objectives, but within the resources available.
Siloed Data
Siloed data in data governance refers to the practice of storing data in isolated, separate systems or databases within an organization. For example, this can often happen when new data sources are introduced. This segregation of data prevents seamless sharing and collaboration between different departments or teams.
In the context of data governance, dealing with siloed data becomes a challenge. It can lead to data inconsistencies, duplication, and hinder the organization’s ability to make informed decisions based on a comprehensive view of its data. To avoid siloed data with your data governance strategy, these silos can be broken down by integrating systems, standardizing data formats, and ensuring a unified approach to data management.
Lack of Leadership
Due to the intricacies of data governance, the absence of strong and centralized leadership often stands as an obstacle. Effective data governance requires clear direction, decisive decision-making, and consistent implementation of policies and protocols. Without robust leadership, organizations often find themselves adrift in a sea of data, lacking a unified vision or strategy. This lack of a guiding force can lead to confusion among employees regarding data management practices, resulting in inconsistent data quality, usage standards and possibly even failure to comply with regulatory standards. Therefore, it’s crucial to solidify your data governance strategy with a strong leadership team that collectively follows a clear vision for data governance within your organization.
Poor Data Quality and Context
With organizations having to sift through such vast amounts of data, it’s hardly surprising that, unfortunately, some of the data picked to be used can be of little to no quality. Without a clear definition of what constitutes ‘good’ data and an understanding of how specific sets of data may relate to one another, organizations risk making flawed decisions based on flawed information. This can then lead to misinterpretation, making it difficult for stakeholders to make accurate business decisions based on the data provided. This lack of clarity can snowball into poor strategic choices, which have the potential to hinder organizational strategy, innovation, and progression.
Addressing this requires a robust, multi-faceted approach, which can include processes such as data profiling and cleansing. When also supported by robust leadership within data governance, organizations can begin to ensure that data is not only accurate but also meaningful, allowing it to be used to support strategic decisions.
Lack of Data Control / Compliance Complexities
Whilst Organizations grapple with the ever-expanding volume and variety of data, it may feel as if the laws and regulations surrounding data are growing at a similar rate. As data flows through diverse systems, platforms, and geographical locations, ensuring compliance with regional, national, and international regulations becomes a tricky puzzle. Unfortunately, this is the organization’s responsibility to solve and failure to do so not only jeopardizes data security but also exposes the organization to severe legal consequences.
Striking a balance between utilizing data for innovation and ensuring compliance with a plethora of regulations is challenging, however, it can be achieved through strategic leadership, robust technological solutions, and the usage of the correct processes to anticipate and address emerging compliance challenges for your ever-growing landscape of data.
Data Governance Frameworks to Consider
Bottom-Up Philosophy
The bottom-up approach to data governance, like organizational structure, begins at the grassroots level, which in this case, is the raw data. Once data has been reviewed, structures or schemas can then be built on top of the data. This is then followed by the addition of data governance rules set by the organization, additional quality controls that ensure the data is compliant with set regulations and any additional features the organization deems necessary.
Top-Down Philosophy
A top-down approach will often suggest that the decision to set up data governance comes from a senior level. The approach will consist of a mixture of both the executive vision for the data governance initiative and clearly defined methodologies surrounding guidelines, policies, and appropriate processes.
This approach can often be advantageous, as executive involvement often provides clarity on the vision for data governance, increasing quality control and collaboration, therefore reducing the likelihood of implementation challenges.
Best Practices within Enterprise Data Governance
Make Sure to Consider Processes for the Entire Data Lifecycle
Data management processes created should cover the entire data lifecycle, from data creation and capture to storage, analysis, archiving, and disposal to prevent oversights and minimize the risk of future complications. Ensure that data governance practices are applied consistently at each stage, to guarantee data quality, security, and legal compliance from beginning to end.
Invest in the Right Technology
Invest in advanced data governance tools and technologies that align with your organization’s needs. Choose platforms that offer features that align with your data governance and overall digital transformation goals, such as data lineage tracking, access control, and policy enforcement. By choosing the right technology, you can simplify data governance tasks and enhance your ability to manage and protect your data effectively.
Automate Wherever Possible
Make sure to utilize automation opportunities whenever they arise, as these can help to streamline data governance processes. Automation can facilitate tasks such as data profiling, data cleansing, compliance checks, and policy enforcement, helping automated workflows ensure consistency and reduce the likelihood of human errors, enhancing the overall efficiency of your data governance initiatives.
Master Data Governance with Elev8
At Elev8, we understand the significance of data governance and its role in the protection of organizational data. We empower our clients with comprehensive data governance initiatives that are designed specifically for your needs. Connect with a member of our expert team today to discuss how you can maximize the value and efficacy of your organizational data.
