Define data governance and explain how it helps organizations manage their data.

The scope of data management is broader than data governance. It can be defined as the practice of ingesting, processing, securing and storing an organization’s data, where it is then utilized for strategic decision-making to improve business outcomes. While this is inclusive of data governance, it also includes other areas of the data management lifecycle, such as data processing, data storage and data security. Since these other areas of data management can also impact data governance, these teams need to work together to execute against a data governance strategy. For example, a data governance team may identify commonalities across disparate datasets, but if they want to integrate them, they’ll need to partner with a data management team to define the data model and data architecture to facilitate those linkages. Another example can include data access, where a data governance team may set the policies around data access to specific types of data (e.g. personally identifiable information (PII)), but a data management team will either provide that access directly or set the mechanism in place to provide that access (e.g. leverage internally defined user roles to approve access).  

Benefits of data governance

Implementing a data governance framework can increase the value of data within your organization. Since data governance helps improve overall data accuracy, it also impacts outcomes based on that data, which can range from more simple day-to-day business decisions to more complex automation initiatives. Other key benefits include:

• Drive scale and data literacy – Limited data access across an organization can limit innovation and create dependencies on subject matter experts (SMEs) within business processes. Data governance practices create a pathway for cross-functional teams to come together to create a shared understanding of data across systems (e.g. reconciling differences domain-agnostic data). This shared understanding can then manifest itself through data standards, where data definitions and metadata are documented in a centralized place, such as a data catalog. This documentation, in turn, becomes the foundation for self-service solutions, such as APIs, which enable consistent data and federated access to it across an organization.   
• Ensure security, data privacy and compliance – Data governance policies provide a way to meet the demands of government regulations regarding sensitive data and privacy, such as the EU General Data Protection Regulation (GDPR), the US Health Insurance Portability and Accountability Act (HIPAA), and industry requirements such as Payment Card Industry Data Security Standards (PCI DSS). Violations of these regulatory requirements can result in costly government fines and public backlash. To avoid this, companies adopt data governance tools to set guardrails, which prevent against data breaches and the misuse of data.
• High-quality data – Data governance ensures data integrity, data accuracy, completeness and consistency. Good data allows companies to better understand their workflows and customers as well as how to optimize their overall business performance. However, errors in the performance metrics can steer an organization in the wrong direction, but data governance tools can address potential inaccuracies. For example, data lineage tools can help data owners trace data through its lifecycle; this includes any source information or data transformations that have been applied during any ETL or ELT processes. This enables close inspection of the root cause of any data errors.
• Promote data analytics – Quality data lays the foundation for more advanced data analytics and data science initiatives; this can include business intelligence reporting or more complex predictive machine learning projects. These can only be prioritized when its main stakeholders trust the underlying data; otherwise, they may not be adopted.

Challenges of data governance

Although the benefits of data governance are clear, data governance initiatives have a number of hurdles to overcome to achieve success. Some of these challenges include:

Data governance is the process of managing the data’s usability, security, availability, and quality within an organization using internally set and enforced rules and policies.

Why does data governance matter?

Data governance is a must for any organization that seeks to use their data for analysis. It creates an environment where data can thrive as a source of useful insight that enables the organization to prosper. Without it, data may fail to meet the quality standards necessary for usable insight extraction or be exposed to security threats that compromise its integrity thereby putting the organization at the risk of being sued.

Data governance provides consistency across all the organization’s businesses thereby making efficient data integration possible. For instance, a supplier’s name may be specified differently in the procurement’s office and the factory’s database. During the integration of data, this may pose a challenge for the analyst who has never interacted with the supplier. It ensures that there is uniformity and that the analyst doesn’t need to consult the departments generating the data in order to gain an understanding of the data.

Governance goes beyond insight extraction and security provision by governing who within the organization can access the data and how they can access it. It is therefore important to understand what data each individual member of staff needs before setting the rules of policies of accessing the data internally.

What are the Goals?

• The breakdown of data silos within the organization: Lack of a centralized system that defines the policies and rules to be followed can lead to departments within an organization deploying their own transactional systems. Governance seeks to harmonize the data structure being generated within the organization.
• Ensuring proper use of data: It ensures that all members of staff can easily access the data they require without unnecessary delays. By limiting the data available to only what is necessary, the employees are able to easily access and extract the information that they require. It also ensures that customers’ data is protected and is not misused by unauthorized employees at the expense of the customer’s right to privacy.
• Improve data quality: It ensures that there is a controlled flow of data within the organization and thus protects the quality of the data. The process of data cleansing is also simplified when the data is well organized, and all the sources harmonized in the structure of data stored.
• Ensuring compliance: Exhaustive privacy rules and policies ensure that the data being collected by the organization is well protected.

Benefits of Data Governance

1. Provides consistency in compliance: Data protection regulations such as EU General Data Protection Regulation (GDPR), PCI DSS (Payment Card Industry Data Security Standards) and US HIPAA (Health Insurance Portability and Accountability Act) are very strict on how data should be managed. Failure to comply with these laws can lead to the organization incurring hefty fines and damaging their reputation. Data governance takes into consideration all the requirements of the applying laws early on thereby protecting the organizations’ data.
2. Improved quality of data: Strong governance ensures that all points of data creation function with data quality as a priority. This leads to an overall improvement of data quality within the organization.
3. Readily available and accurate data map: By defining where the data is and how it can be accessed, data governance works like an address book for all the data in the organization. This ensures there is no data that is isolated by errors of commission or omission from the overall organization’s rules and policies.
4. Improves data management: The code of conduct and rules established by governance ensure that data management is made easier. It makes it possible for the management of the data’s security and legal compliance.

Data that has integrity issues can not be relied on by the management for profitable insight. Therefore, any organization that is seeking to extract value from their data must first come up with a data governance system that touches on all the data they wish to use and protect. Every organization has different needs for its data and thus the need to come up with rules and policies that are customized to an organization’s needs.

Data Governance Strategy Components

Before embarking on the specific details of a data governance policy, the team involved has to first come up with a mission statement, the objectives, the evaluation metrics, and identify the people responsible for various governance aspects. This should be shared with all the people involved so as to have a unified understanding of how the organization will treat its data.

Good governance should include the following:

1. Policies
2. Rules
3. Procedures
4. Structures

DQLabs, an enterprise data governance platform provides a comprehensive framework that is modern and will help an organization add value to all their data as well as remain compliant. This makes it easy for an organization to set up strong data governance fast with the comfort that they are compliant with the jurisdiction’s laws. DQLabs system will help in supporting the creation of a catalog, development of policies, workflow management, and process management.

Data Governance Implementation

The implementation within an organization requires a team from different areas of the organization that understands the importance of setting the right rules and policies. The diversity of the team ensures that the policy includes the needs of as many departments as possible and thus the possibility of some employees’ data needs being ignored is reduced significantly. The inclusion also makes it easier for all members of staff to understand the policy and adopt it with ease. After coming up with the policy, it is important to train all the users of data who will be impacted to make it easier for the organization to fully implement it.

Conclusion

Data governance sets the rules to be followed throughout the organization while interacting with the data. Strong governance ensures that the data is secure in storage when it’s being accessed and high quality when being created. With strict data regulations being introduced in different jurisdictions, data governance ensures that the organization remains compliant with the laws in all the jurisdictions it operates it. The governance matters to an organization that intends to use their data for analysis and remain compliant because it ensures consistency, integrity, and security of the data. By improving data quality and proper data use, it increases efficiency within the organization and saves a lot of time for the data users. To successfully implement it in an organization, the data users should be trained on the policies of governance to ensure they understand what will be required of them. DQLabs’ agile data governance tool makes it easier for an organization to set up strong governance by providing a framework that is comprehensive and is guaranteed to increase the organization’s data value as well as ensure compliance.

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