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Data is the lifeblood of every business. This adage is more true today, in the corporate digital transformation era than ever before. Some equate data with oil, while others equate data with Uranium in the multi-cloud environment. Either way, this shows asset data's significance to any organization as it helps them make informed business decisions, enhance customer experiences, drive innovations, and stimulate growth.
However, just as a library, whose assets are its collection of books, requires a system in place for acquiring, collecting, maintaining, or lending books appropriately and reasonably, an organization requires a similar framework to collect, store, maintain, and manage data efficiently. This is where an efficient data governance strategy comes into play.
According to a survey, 71% of data professionals cite data governance as the key component that improves the time to get data for business analytics. With faster access to accurate and reliable data, organizations gain the competitive edge they need to thrive in an ever-evolving business environment.
Data governance refers to the overall process of managing, using, and protecting data in an organization. It includes a set of rules and policies for enforcing data quality, data privacy, data security, data access, and data management. These policies and standards are defined and governed by a steering committee that is typically composed of data management personnel, including data stewards, data owners, and a chief data officer.
Data governance strategy is the blueprint that outlines those policies and standards, instructing data management teams on how to best implement the government processes. The primary objective of a data governance strategy is to help data teams ensure that organizational data is of high quality, accurate, safe to use, and well-protected. The timely availability of accurate and trusted data enables teams to efficiently analyze and process the data to drive desired business objectives.
Data is growing exponentially, spanning numerous physical systems and devices from one cloud to another and to the multi-cloud. The multi-cloud brings speed, cost-efficiency, global footprint, and time to value, but it also creates many challenges for organizations due to its inherent complexities and limitations. With a solid data governance strategy, businesses can navigate those complexities and also effectively handle, leverage, and protect their data systems and data. Here are some important reasons organizations must have a good strategy:
Data scientists cannot drive actionable insights without the availability of data that they need. Similarly, Business Intelligence analysts cannot identify the right trends or patterns needed to make informed decisions if they don’t have access to the right data. Due to the disruptive nature of the multi-cloud, organizations are challenged to get their hands around the required data due to distributed data systems and applications available across multiple cloud services.
Data governance enables organizations to have a systemized process that allows them to identify and track data across their corporate environment to get a comprehensive view of their data systems and landscape.
When the sales and customer success teams rely on different sets of data and conclude different opinions regarding customer satisfaction, misunderstandings are bound to ensue. This scenario leads to the same result across all departments. Therefore, it is critical for data teams to ensure data consistency. The term data consistency refers to the accuracy, reliability, and trust of data across the data environment. Accurate and reliable data allow organizations to drive success and revenue through informed decisions.
Data governance ensures data consistency by enforcing policies and standards for data ingestion, collection, and management. It further helps establish a “single source of truth,” which means an inventory of data providing the same grammar, meaning, and description throughout the organization.
Data hoarding is a byproduct of the uncontrollable proliferation of data across the globe. However, this intentional or unintentional hoarding tends to produce more and more stale or irrelevant data. According to studies, 82% of companies use stale data for decision-making, while 85% cite that such data leads to incorrect decision-making and loss of revenue.
A well-planned data governance strategy helps teams create data retention policies based on legal, regulatory, geography, or business requirements. It further establishes procedures around when or how data should be purged or archived depending on its relevance, purpose, or further requirement.
It is imperative to understand that the need to have a solid governance strategy is not limited to business-specific purposes but also to compliance and security. For instance, the European Union General Data Protection Regulation (GDPR) lays down specific regulations for individual data privacy rights, obligations for businesses regarding how data is collected, processed, or shared, and how it must be protected. With a robust strategy in place, teams can ensure that their data collection, processing, management, and protection practices align with regulatory or security standards to meet compliance.
A well-defined data governance strategy is the key to achieving the aforementioned objectives. To guarantee the success of your DG strategy, you must ensure it includes the following essential controls:
Comprehensive sensitive data asset and data discovery is the first and most critical control of any data governance strategy, especially for multi-cloud environments. The discovery engine must be compatible with both cloud-native and non-native data systems, applications, and similar resources. It should also be able to identify and register shadow or dark data systems that often go undetected in native cloud environments, such as downloaded open-source databases (MongoDB, MySQL, and Cassandra) that a cloud-native service might not be able to register as a data system. It should provide detailed metadata specific to those resources, such as instance properties, vendor details, encryption status, port status, etc.
Together with sensitive data discovery, the control should give you a comprehensive view of the entire data stored in those data assets. It should be able to detect structured, semi-structured, and unstructured data. It must further identify different types of data elements specific to security or data privacy laws, such as GDPR, CPRA, PCI DSS, HIPAA, etc. The detection engine must have a custom policy engine so that users may define custom data elements specific to their business’s needs.
A data catalog is one of the core components of a data governance framework. It provides organizations with a consolidated metadata repository of their entire data landscape. An extensive metadata repository enables teams to understand what data exists, including sensitive data, in their corporate data environment. It further provides insights into its location, meaning, structure, context, and intended usage.
The control must further deliver a comprehensive business glossary for corporate data. With an effective glossary in place, organizations can establish a common vocabulary of data that remains consistent throughout the business. It further improves data consistency and promotes data quality. After all, if the customer and sales data have a common definition, teams can ensure that the data is analyzed and reported consistently. The catalog should provide a mechanism for users and domain experts to collaborate on the business glossary. Similarly, the catalog must provide a searchable repository where users can discover related metadata by tag, owner, or label.
Data classification falls under the same basket as sensitive data discovery and data cataloging. It runs in parallel with the data discovery and cataloging process. Data classification is the process of categorizing corporate data based on its sensitivity, risk, and value. Classification allows businesses to enable appropriate security, governance, and compliance controls around the data. After all, if data is classified as confidential, it is imperative for a security team to restrict its access, encrypt it, or put other security controls in place.
Data classification shouldn’t be a one-off practice. Instead, it should be an ongoing process so that you have an automated classification process in place that categorizes the data at the point of ingestion or creation. There must also be a set of schemes and grammar to classify the data consistently across the multi-cloud. For instance, a criterion must be set for how data needs to be classified based on its value and sensitivity and how it must be labeled based on the selected classification. This way, teams can ensure that the classification control is consistent and accurate throughout the process.
The data access governance control leverages the insights derived from data classification and data cataloging to ensure that only authorized users have access to sensitive data assets and data. The access and entitlement control further establishes rules and policies to determine who can access the data, depending on the level of sensitivity and what access privilege they require for their job function. For the access and entitlement control to be effective, it must identify the users and roles who have access to data across the multi-cloud infrastructure and the access path they take to that data. After all, users can access sensitive data via multiple paths, such as inherent access, administrative access by default, etc.
Organizations must strive for a least privilege access model by eliminating the multiple paths that users have to sensitive data and limiting access rights to only what is strictly required for them to do their job. Another important component worth discussing is the dynamic data masking of sensitive data. By changing the value of the sensitive data and masking it, data teams can share the data internally and externally securely and in a compliant fashion.
Data lineage gives data management teams detailed insights into the history of the data or its entire lifecycle. For instance, where the data originated from, how it is changed throughout its lifecycle, the level of transformations it went through, or where it is stored. These insights enable the governance team to establish policies and procedures around data management and control how data is accessed and used. Take, for instance, the financial industry. A financial institution could use data lineage to track the flow of transactions. With this level of information, data teams can determine if the transaction is conducted via outdated data or if it violates any regulatory requirement. Data lineage can be a very productive tool in helping data teams determine data accuracy, reliability, and data quality.
Data quality and data lineage are closely linked as both controls help data teams determine the accuracy, reliability, and consistency of data. Organizations require access to only high-quality data to make informed decisions or to conduct business analytics. There are a number of challenges that could hinder an organization with its data processing or analysis, such as data duplication, data inconsistency, data incompleteness, or lack of clear ownership of data. It is important that organizations look to provide insight into the quality of data by putting data quality information into the data catalog and making it easily accessible.
Data protection is critical not only to prevent data breaches but also to comply with data protection regulations. Organizations are reminded about establishing technical and administrative security measures to protect users’ data repeatedly, such as Article 32 of the EU GDPR, section 164.312 of HIPAA, Section 4.7 of PIPEDA Canada, or CPRA under section 1798.100 (e) of the Cal. Civ. code. This control is also linked to data classification and cataloging, as teams can leverage the data sensitivity details and intended use to determine appropriate security measures.
Data protection starts with data systems. Hence, security teams must first discover and identify misconfigured cloud data assets. Depending on the complexity of the misconfiguration, the control must enable security teams to either manually fix the issue or put it on auto-remediation. Similarly, to protect sensitive data, you tighten up the access controls around it or employ data masking for safe data sharing.
Securiti Data Controls Cloud is based on a Unified Data Controls framework that maps all those necessary controls that enable organizations to optimize Data Governance. Use granular insights into sensitive data across your on-prem, SaaS, IaaS, and multi-cloud environments to drive your governance strategy and leverage data with trust and confidence.
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