Data management is crucial in the era of cloud computing and artificial intelligence (AI), where accessing large volumes of data has become easier.
In this article, we highlight selected elements of cloud data management, providing business leaders with a compass to guide them in harnessing value and protecting valuable data in the cloud. I have chosen a sailing analogy for this article. Hoist the sails and pick up some tips to speed up your journey.
Waves of data can easily crash ashore, so a lighthouse is needed to guide data ships through the foggy depths of the digital ocean."
Automation and data controls in the cloud are key factors in making data handling more cost-effective and ensuring robust security, especially for sensitive data. Here is what do organizations need to do to succeed in cloud data management:
- Identify business owners of your data
- Organize your data assets
- Classify sensitive data
- Enhance data lifecycle management
Identify business owners of your data
Data ownership promotes effective data management, improves data quality, enables compliance, and improves decision making, leading to better business results.
The captain of your data ship is your business stakeholders."
It is fundamental to identify the responsible data owners from business side within the organization. In practice, absent ownership often leads to the failure of data transformation projects.
As a measure of control, you can require your cloud service provider to furnish automated documentation regarding data ownership assignments for both cloud data and metadata. Second, from a technical standpoint, it is crucial to include and populate an ownership field in data catalogs for sensitive data.
Organize your data assets
The effectiveness of cloud data management relies on maintaining complete control over all data assets. Contextual information is necessary to support comprehensive data management capabilities such as business definitions and classifications. Together, these characteristics form the data catalogue.
Treat your data as if it were your treasure chest."
Data catalogues and metadata help structure the data, and ontologies (domain knowledge) support interoperability and automation of controls across cloud service and technology providers.
Automate the cataloguing of all data at the point of creation or ingestion. Cataloguing should be consistent across all environments. To achieve this, organizations need to identify their data assets using domain-specific ontological principles.
Automate metadata harvesting and definition to scale metadata management efforts across cloud data architectures.
Classify sensitive data
From the moment data is created, it can be both an asset and a liability. Poorly managed data can pose risks if it is used inappropriately or accessed by unauthorized users. These risks are heightened in a cloud environment, as organizations often move large amounts of critical data to the cloud, increasing their exposure. Effective risk management of data in the cloud environment is therefore paramount.
Sensitive data should embark only classified sailing routes."
A data sensitivity classification is a labelling scheme used to categorize data elements based on their level of business risk or value. This labelling process is critical to ensuring security and regulatory compliance across all applications, including the growing number of cloud environments.
Sensitive data includes classifications such as personal information, company identifiable information, critical data elements used for business processes, and licensed data. Additional data classification types may be required to meet specific regulatory compliance requirements and facilitate risk reporting.
Automate classification of sensitive data at the point of ingestion. This requires organizations to define the classification and associated policies for their data.
Enhance data lifecycle management
Proper data lifecycle management ensures cost control and compliance with data storage regulations. A well-designed framework ensures easy access to relevant data, cost-effective storage, and automated migration of obsolete data.
A data lifecycle management framework is like your sailing itinerary."
Ensuring transparency and traceability of data at every stage, a data lifecycle management framework enables effective management of data assets. It establishes metrics and lineage views to identify sources of data quality issues and deterioration points.
Cloud-based solutions provide instant alerts of data quality rule violations, enabling rapid problem identification. This capability accelerates the resolution of data-related issues.
As a control method, define retention schedules and measure data quality for sensitive data throughout the data lifecycle in cloud environments. In addition, automate rules for archiving and disposition of data. To achieve this, organizations need to strategically plan and budget for data and data storage in a comprehensive manner, considering regulatory requirements.
Understand big picture
Establishing a comprehensive cloud data management system necessitates implementing extra automation and controls, in addition to the ones mentioned earlier. E.g. data lineage is a key enabler for efficient data lifecycle management and cost allocation based on usage and storage. Furthermore, it tends to require assessment of data prior cloudification to get all automation and controls in place.
Finally, it is important to recognize that in a data-driven organization, strategic decisions are heavily dependent on data. Therefore, when designing organizational and data/IT landscapes, it is essential to prioritize business needs over sole reliance on data professionals. This approach promotes long-term maintainability and reduces dependency on individuals. Consequently, it is essential that data-related strategic initiatives are led and owned by the business to ensure alignment with organizational goals. The captain sets the course, not the sailors.
Read also:
How to become a successful data-driven leader? Data management essentials for business leaders.
Tietoevry Tech Services specializes in developing effective data management strategies and designing data management solutions, including those related to cloudification. With our experience in both data management and cloud technologies, we can help organizations realise the benefits of cloudification while ensuring robust and efficient data management practices.
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Sirpa is dedicated to creating business value for customers and enabling growth through data from a strategic perspective. She has more than twenty years of experience in banking and finance in various roles within investment banking (M&A), corporate and industry analysis, rating and large data sourcing and platform integration projects.
In her current role as Head of Data Management Finland at Tietoevry Tech Services, she advises customers on data and data management related brainstorming, especially in connection with cloudification. Her focus is on helping customers do better business through a strategic approach to digitization and the use of data.