The delivery of well-understood, precise, and comprehensive data, particularly sensitive data, is an arduous effort for most organizations, and the difficulty of this work rises as organizations grow. Often, so much data gets accumulated in such a short timeframe that it is difficult for businesses to keep track of it. If you cannot keep track of the information, you can’t secure it or use it to its fullest extent.
To fully avail your data opportunity, you need to focus on systematically organizing the information. Organizing this information keeps it safe, secure, and readily available to individuals throughout the business.
Companies pour significant resources into developing a data governance platform to overcome this tradeoff between access and efficiency.
In this article, we will cover:
Data Governance in a Nutshell
Corporate data standards and regulations that govern data usage are the foundation of data governance, ensuring that data assets in enterprise systems are always available, usable, intact, and secure.
Data governance platforms that are effective ensure that the data is trustworthy, consistent, and secure irregardless of the role of an individual within the organization. It is becoming more important as businesses must comply with new privacy requirements and are increasingly reliant on data analytics to optimize operations and drive corporate decision-making.
The primary responsibility of data governance is to guarantee that you will maintain the quality of the data at a high level throughout the entirety of the data’s lifespan and that the controls that get put into place will follow the organization’s business goals. You must use the information effectively and efficiently to pursue the firm’s purposes.
Therefore, data governance solutions determine who is authorized to take action, to what data, under what circumstances, and utilizing what approaches.
Why Businesses Need Data Governance
Even though most companies do store large amounts of data either digitally or physically, the majority of that data gets stored in a format that is not standardized. In addition, companies cannot always be confident of the dependability of the data due to factors such as its age and the data sources, among other things.
Because of concerns about the validity of the data, employees or corporate leaders frequently hesitate to depend on this information for business intelligence when making decisions.
In this context, data governance refers to making an organization’s data more trustworthy. It also ensures that high-quality data is accessible throughout the organization. It allows every department to make data-driven decisions. Finally, data governance software and data governance applications are crucial in a company’s digital transformation.
Types of Data Governance Products
Data is at the heart of every business in today’s world. In many cases, compliance with data security and protection rules necessitates using a data governance system. With that, the best data governance tools assist in creating and maintaining a systematic set of policies, processes, and protocols that regulate how data gets kept, used, and managed within an organization.
Data governance solutions are available in standalone data governance software and integrated data governance platforms. Data governance often gets integrated into data quality, catalogs, and even analytics tools by data governance vendors.
The capacity to establish a business glossary, data lineage, and rules-based workflows are common aspects of cloud data governance tools. Many data governance automation tools include Artificial Intelligence (AI) and Machine Learning (ML). Some top data governance companies also add data discovery, Metadata Management, data cleansing, cataloging, modeling, integration, data stewardship, and other automated data capabilities.
Products For Cloud Data Governance
The growing size of datasets restricts the ability to manage data locally, particularly for resource-intensive tasks such as data collection. In this age of ever-expanding cloud computing resources, the need for dependability and assurance from cloud data governance solutions is even more critical.
Cloud data governance tools oversee the accessibility, integrity, and use of cloud computing systems to accomplish crucial business objectives. In multi-cloud or hybrid cloud computing environments, when data gets kept in several locations and data governance standards vary across databases, cloud data governance acquires a new degree of complexity.
Thus, the best cloud data governance tools require the following:
- Avoidance of cyber security risks and data leaks
- Controlled and regulated access to sensitive data
- Data privacy and security protocols are gathered and maintained regularly
- Increased data privacy and data security
- Modernized data analytics to enhance operations and improve decision-making
Current data access governance tools that allow data engineers and compliance teams to automate data governance, data access rules, and privacy protection through a web application interface can assist data teams in managing the intricacies of cloud data governance.
Among the most important aspects to look for in a data governance platform are:
Discovering, Capturing, and Cataloging Data
The best data governance software should be able to find and collect data from throughout the company. Data cataloging facilitates data discovery, which in turn enables business analytics. The catalog provides a bird’s eye view of each data entity, including its profile, relationships, history, and a business glossary with agreed-upon terms.
Data and Metadata Management
The data governance platform must be able to track and manage data management activities if your firm does not already have an MDM. Data quality measurements, master data rules and tasks, and data configuration are all part of this. It contains the data integration application and manages the data lifecycle and pipeline tracking. The underlying metadata is extracted and documented by appropriate data access governance software, making categorization easier.
Data Ownership and Stewardship Capabilities
Without the ideas of data ownership and data stewardship, a data governance system will fail. Data stewards are responsible for ensuring data accuracy, completeness, and consistency. Both owners and stewards can do their responsibilities with the help of a data governance system.
Self-Service Tools
Self-service technologies are critical for companies whose data governance aims get focused primarily on the business team. These data governance tools must provide all data intuitively and clutter-free, with reporting and alerting capabilities built-in. A self-service station makes it possible to make consistent and clear decisions. The tool must also make reviewing and monitoring simple to manage data better.
Clean Visualization
The graphical representation of data is the driving force behind any effective data governance solution. This option refers to more than just the depiction of data lineage, linkages, and anomalies. Policies, issues, and data pipelines must be represented visually in data governance systems to use data at its highest potential.
Data Lineage Automation
Data lineage records the beginnings of each data entity, its changes, and its mobility within the system. It aids in the tracing and detection of any system faults. Maintaining data lineage manually is time-consuming and error-prone. As a result, data governance technologies must provide automatic lineage tracking via code parsing, ETL, SQL log parsing, and machine learning.
Business Glossary
The development of standardized data definitions and file formats should always be the first step in any plan for the governance of data. Establishing a shared glossary of business words is an effective way to ensure consistency. Intelligent governance technologies also have the potential to import business terms that are already established. Consequently, you must bring all of the data assets, data quality, and data lineage of the company into sync with this data dictionary.
Compatibility with Existing Systems
Data governance platforms rarely get sold separately and are frequently packaged with other data governance software. Before choosing a data governance solution, it is good to assess existing capabilities and only pay for features that complete the data governance framework. This capability means that your company’s technology must be adaptable and configurable. It must also address current infrastructures, such as giving on-premise or cloud deployment options.
Compliance Audit-Ready
If compliance is one of the major purposes of governance, the data governance platform must allow for external and internal audits. HIPAA and GDPR are two examples of data-related regulatory legislation that mandate secure data storage and upkeep. Proof of compliance can be as simple as documenting the data dictionary, data rules, and access restrictions. A business can create understandable reports or grant specialized roles to external auditors with limited data exploration capabilities.
Policy Management
Policy controls must be configured and managed using data governance technologies. The controls are supposed to enforce policy management after getting set up automatically. This option goes a long way toward assisting data stewards in their duties.
Conclusion
Your organization’s data is far too precious to get managed ineffectively. The data governance framework you create today will help your company improve the quality and accessibility of its consumers’ data.
If data governance gets done poorly, data users do not trust the data and would not use it to make decisions. The way you handle data is helpful to your company, but it reflects your overall accountability, organization, and reputation.
Satori Provides Agile Data Governance
Satori helps you with DataSecOps for your modern data stack. This includes continuous sensitive data discovery, integration with existing data governance tools to make data governance more efficient and immediate, and means to streamline access to sensitive data and create security policies that are independent of the specific data infrastructure you’re using.
