Major Use Cases and Capabilities
Major Use Cases
Discovery and search of data
Unstructured data that lies undocumented across an organization represents a security risk and a missed opportunity to create value from that data. Digital transformation creates data on endpoints, within cloud services, in email, messaging and collaboration platforms that can slip under the radar. Often this data can provide valuable insights into what is happening to organizations, especially in terms of customer relations or marketing. It is vital that Data Governance platforms can discover unstructured data, order it, and give the organization the option of what action to take. This can include deleting duplicates, reassigning data to different stewardship or protecting data that contains sensitive information.
Compliance
While personal data held in structured databases is relatively easy to find and protect, the real danger arises with personal information (PII) that lies hidden within unstructured data stacks. For example, a customer may have provided email, home address or even credit card details in an email. Such information may also reside in unprotected documents and spreadsheets created by employees. Health data may be contained in letters and other documents. All modern compliance laws put the responsibility for data protection on the Data Controller defined as the organization that stores such information on behalf of individuals or other organizations. Organizations have been fined large penalties for failing to protect personal data. Without Data Governance in place such data may go unnoticed and unprotected.
Auditing and classification
Data that has been discovered from unstructured sources must be audited and classified if it is to be protected and add value to the organization - data that is not identified and classified is valueless. Tools within Data Governance platforms perform this function with varying levels of automation. Some platforms offer further insight into file access patterns from Active Directory, SharePoint or Teams. Further, data can be divided into functional or business categories from a Business Glossary. For example: customers, suppliers, inventory, personally identifiable information (PII), confidential, protected and so on. A good Data Governance platform should enable the customer to define more categories in line with business policy and sector and define data for future classifications.
Value creation
Unstructured data is like crude oil; value is only extracted once it is refined. Data Governance platforms assist in creating value from data after discovery, auditing, and classification. While some organizations may initiate processes and applications to generate and analyze specific data - a simple example would be a market research exercise - there is much untapped value to be found from the unstructured data created through normal business processes every day. To find value, Data Governance applications will offer a range of capabilities such as dashboards that display inefficient data flows and data usage, data duplication and data usage that violates business policies. Interactions between customers and employees can also be mapped, some platforms also monitor data shared on social media platforms.
Cyber security
Data that is not governed remains at constant risk of being lost or stolen, which is a big problem if it contains information that is confidential, such as PII or is critical to the security of the organization. A Data Governance framework that includes a Data Governance platform is an important part of cyber security, so that high value and confidential data can be found, classified, and protected on an ongoing basis. The platform must be intelligent enough to recognize PII data or other critical data or use business policies to identify and treat it accordingly. The Data Governance platform cannot apply protection, and therefore, the organization must use other tools in its cyber security portfolio to protect it: encryption software, privileged access management (PAM) and identity governance & administration (IGA) tools. Data Governance is a welcome tool to have in the frontline against data breaches. Organizations cannot protect what they do not know exists.
Capabilities
Enterprise Applications
The majority of unstructured data is created in mainstream enterprise applications such as Office 365, SharePoint, Google Docs, Adobe Acrobat and many other applications that are popularly used. Data Governance platforms will offer different levels of support for these types of applications, the more the better.
DataBase applications
Huge amounts of data are stored in databases – often needlessly. Data governance that can read popular database technologies ensure the accuracy, completeness, and reliability of data across different databases. By reading databases, these tools can identify inconsistencies, duplicates, or errors in the data, which can then be corrected to maintain high data quality standards.
Metadata
Metadata helps in the organization and management of data by providing information about its source, structure, content, and context. This is crucial for data governance, compliance, and lifecycle management, ensuring that data is accurately cataloged, stored, and maintained over time.
Reporting
Without timely reports, making decisions on data is hard. Report generation allows all stakeholder to have a clear idea on the creation and usage throughout the organization.
Social Data
In modern organizations social media platforms such as Facebook, Twitter and Instagram are used by employees within the enterprise infrastructure as well as formally by LOBs such as marketing. Content created on these platforms should be included in any Data Governance architecture and covered by Data Governance platforms. Can also include messaging tools.
Compliance
Many industries are subject to strict data protection and privacy regulations, such as GDPR, HIPAA, or CCPA. Data governance tools need to access databases to classify and manage sensitive information properly, ensuring compliance with these laws by monitoring how data is stored, accessed, and used.
Data Discovery
Discovering data involves exploring available data sources within and outside the organization. This can include databases, spreadsheets, external datasets, and more, with the goal of identifying relevant data that can be used for analysis. iscovering data involves exploring available data sources within and outside the organization. This can include databases, spreadsheets, external datasets, and more, with the goal of identifying relevant data that can be used for analysis.
Search
By allowing users to easily locate and examine data, search tools contribute to improving the quality and accuracy of data. Users can identify discrepancies, redundancies, and errors, enabling timely corrections and updates. This leads to better data quality, which is a fundamental aspect of data governance.