Business leaders with strong technology proficiency drive successful digital and data transformations.
In my experience, the secret sauce to becoming a successful data-driven leader is to understand and facilitate the implementation of key elements for managing data:
The common pitfall is to think of these only as operational elements. I see them as critical items on the C-suite agenda if data is to be used as a strategic asset and revenue generator.
Why is it important for executives to focus on improving their data skills?
First and foremost, designing and implementing these fundamental data management elements is not a simple task. It involves complex, cross-silo connections that often lack proper ownership and have numerous dependencies. People responsible for working with these concepts often lack the authority to drive the necessary changes. Therefore, the support of upper management is essential to initiate and accelerate the transformation and adaptation process.
This may sound like a lot of effort, but when you understand the potential benefits, it is certainly motivation to go the extra mile. Investing in understanding and implementing a solid data management foundation is an investment that will deliver clear business benefits, such as:
- Cost savings: Organizations can reduce data storage costs by effectively managing duplicates and irrelevant data, and making informed decisions about data archiving and destruction. This cost-saving measure becomes particularly essential in the cloud environment, where data volumes can easily multiply. In addition, proper data management practises minimize data management overhead costs.
- Regulatory compliance: Implementing these elements ensures compliance with regulatory requirements, such as the General Data Protection Regulation (GDPR) and data sharing regulations. Data management provides essential support for banks in adhering to BCBS 239 principles.
- Improved data security: Tracking sensitive data improves overall data security.
- Enhanced reporting and data analysis: Implementing these elements enables better data discoverability and facilitates improved reporting, data analytics and data quality analysis.
The organization also benefits indirectly from improved data management:
- Improved employee satisfaction: Clearly communicating business requirements and establishing data ownership leads to improved employee satisfaction within e.g. IT/data department. Mutual expectation management can also be further improved.
- Assessment of organizational capability: Having these elements in place serves as an acid test of an organization’s ability to effectively define and communicate its business capability requirements throughout the organization.
Key elements to learn about data management
Let's dive into the essential data management elements. A better understanding of these three elements is key to focusing on more effective data management practices to leverage organizations’ data assets for decision-making and strategic initiatives.
1. Data lifecycle management (data lineage)
What: A data lifecycle management framework facilitates the efficient management of data assets within an organization, from the creation or acquisition of data, through its use, maintenance, archiving based on business requirements, and eventual disposal.
Direct benefit for business: One key benefit is cost savings through efficient use of storage resources and minimized data management overhead. Secondly, by implementing appropriate data management practices, organizations can demonstrate compliance and minimize legal and financial risks. Overall, data lifecycle management provides organizations with a robust and systematic approach to managing data, leading to improved data quality, security, compliance and decision-making capabilities.
This component ensures effective management of data quality at all stages of the data lifecycle across multiple environments (especially cloud). To effectively use technologies such as Artificial Intelligence (AI), organizations need data that meets the required quality standards. High quality data is a fundamental requirement for successful AI implementations and the generation of valuable insights.
2. Data catalogue - categorizing data at a high level
What: The way an organization views its data assets from a holistic perspective, i.e. a high-level categorization of data, a centralized inventory or repository that organizes and provides comprehensive information about an organization’s data assets.
Direct benefit for business: Organizations that create, maintain, and share comprehensive data catalogs can effectively maximize the controlled reuse of data assets. Second, a data catalogue plays a critical role in data governance by providing visibility into data assets and supporting data compliance efforts. Third, catalogues enable users to better understand the meaning and context of data, facilitating effective data analysis and interpretation. In addition, a data catalogue helps to effectively manage data assets and optimize data storage and usage. Finally, a data catalogue promotes collaboration and knowledge sharing among data users and stakeholders.
3. Metadata - descriptive information about data (in catalogues)
What: Metadata is a key component of a data catalogue. It provides detailed information about the records, enabling users to discover, understand and effectively use the data assets available.
When it comes to data lifecycle management, specific regulations may impose data retention requirements that extend to metadata. To comply with legal or regulatory obligations, particularly in areas such as finance or law, organizations may be required to retain metadata records for a specified period of time.
Some regulations require organizations to maintain an audit trail of metadata changes and provide transparency into metadata management practices to support compliance and accountability.
Direct benefit for business: Metadata provides several benefits, including meeting regulatory requirements for (meta)data sharing and sensitive data. In addition, metadata supports data governance and compliance requirements, as well as facilitating cost management by identifying and removing duplicates, thereby optimizing data storage costs.
A focus on execution capability
An organization needs sufficient execution capability to implement earlier presented data management elements. In the best scenario, all elements are in place simultaneously. Often, undefined ownership, limited authority and competing priorities slow down the execution process.
Technological solutions and data management frameworks exist to address the elements; the potential missing piece is a lack of business ownership and commitment - or a comprehensive understanding of these complex issues or management of those.
Especially when managing data in the cloud, success depends on maintaining complete control over all data assets. For this reason, it is highly recommended that these fundamental elements are in place before moving to the cloud. Otherwise, existing data management deficiencies will be compounded. Without proper data management, it is uncertain how much benefit cloudification will bring to an organization; it could even create more problems, especially in terms of security.
Data management can be likened to an octopus, with tentacles extending throughout the organization. It requires someone to act as the head, establish clear ownership and create orchestration, especially in agile setups. In other words, data-driven organizations need a full symphony orchestra, complete with a conductor.
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.
Did you know that we provide the EDM Council frameworks such as DCAM and CDMC certified trainers and consultants to our customers? The EDM Council, the world's leading data and analytics professional association, is dedicated to developing, innovating and promoting best practices in data and analytics management.
The EDM Council's DataVision Finland event is available on demand! Discover international best practices in data management, cloud data and the transformative impact of AI on data management.
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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.