Get to know data management definition, elements, and benefits.
Successful companies such as Nike, Zalando, and Hello Fresh don't just offer great products and services, they also leverage their data to gain a deeper understanding of their customers. By using data effectively, they can offer personalized and relevant experiences that keep customers coming back for more.
But what exactly is data management? And how can data management help you reach your business objectives?
What is data management?
Data management is a methodical approach to managing and making use of data within an organization. This involves planning and governing data strategically, organizing processes and resources, and utilizing technologies for effective data collection, storage, analysis, and visualization.
Successful data management therefore requires the development of dedicated capabilities at a strategic, organizational, and technological level. These capabilities are necessary to ensure high quality and availability of data and to realize data-driven requirements from the business units in the form of new products and services.
Data management also supports companies in complying with legal and regulatory requirements (e.g., GDPR) regarding the handling of data and making them traceable.
With data management to data excellence
Leading companies use their data to realize digital use cases that create value for themselves and for their customers. The ability to realize the full potential of data in the enterprise - known as data excellence - is no coincidence. Data excellence requires building up data management capabilities in four areas:
The neoverv data management framework
Data strategy
Data strategy, as part of a data management, creates a data vision, mission, and goals, and then pursues them by implementing a defined roadmap for data transformation.
The strategy dimension of data management determines how it is practiced in a company. There are two essential elements to this strategy. The first is the data management vision, mission, and goals, which are defined based on the overarching corporate strategy and set the long-term ambition of data management. The second is the transformation roadmap, which outlines the measures for realizing the vision, mission, and goals, and brings them to life.
Make data strategy measurable
Data management goals should be defined in a measurable way (e.g., by using the OKR method) and roadmap priorities and progress should be regularly reviewed and adjusted. Furthermore, the use of specific data KPIs is suitable here to check the status of the operational realization of the data strategy. There are already several best-practice data KPIs that can accompany the implementation and make it measurable, such as the number of implemented data use cases or established data roles. Read here how you can define and implement measurable goals for your data strategy.
Business
Effective data management must contribute directly to the business needs of the company. It should not be a purely technology-driven initiative, but rather aligned with the requirements of the various business units.
Business data domains
To effectively align data management in a company, two concepts are crucial. First, a clear understanding of the business application areas (known as business domains) and the corresponding business data. Second, an understanding of the current and future data use cases per business unit, such as meeting customer needs, driving innovation, or optimizing processes.
Managing company data shouldn't be the sole responsibility of business units that generate and use it. To make informed decisions, cross-business domain views and access to data are essential for units like Sales & Marketing. Data management facilitates this cross-domain application and avoids the creation of isolated data silos.
An example of a comprehensive view of the data is shown below. Customer data is significant in both the strategy area and Marketing & Sales area. Determining customers' demand for new products and services is strategic, while processing orders and providing customer service is operational.
Example of a business data domain model
Data Use Cases
Data use cases showcase the current and future application areas of data. They are crucial in prioritizing which data is necessary, and which data capabilities and technologies need to be established within the company to implement the use cases successfully. To facilitate easy comparison and prioritization based on the value for the company, a high-level use case example is shown below.
An example of a high-level use case is shown below, allowing for easy comparison and prioritization based on the value for the company.
Example of a data use case (high-level)
Governance
A data management system must provide effective data governance and data organization. This is the only way to optimize data quality and availability and provide the right skills for effective data use. Furthermore, data management helps companies to comply with their data protection and compliance requirements.
Data Roles & Responsibility
To implement data management effectively, new responsibilities and processes are needed in the company. Setting up specific data-driven roles such as Data Owners and Data Stewards ensures that new necessary skills are developed and responsibilities for data maintenance and usage are clearly defined and enforced.
Two factors are important for establishing data roles. First, a data role does not necessarily correspond to a new position to be filled. Existing positions can combine several roles. For example, a department head can take on the role of the strategically responsible data owner. Second, the establishment of the roles takes place in the context of concrete data use cases, so that the new capabilities are directly oriented to the business added value. New agile collaboration models help ensure that data experts from business and tech areas work effectively together.
Data protection & Compliance
Data protection is an important aspect of data management that ensures personal data is confidential, protected, and used only for its intended purpose. A data management system can promote data protection by enabling companies to implement necessary technical and organizational measures to safeguard data from loss, theft, or misuse.
Examples of such measures include encryption, access restrictions compliance, and security audits. Additionally, data management can help companies achieve transparency in data processing and inform individuals about their rights.
Compliance requirements refer to regulations and rules that companies must adhere to in order to meet country- and industry-specific standards. These regulations include the EU General Data Protection Regulation (GDPR) and the Payment Card Industry Data Security Standard (PCI DSS), among others. Compliance requirements specify how companies should handle personal data records to ensure their safety and security. Through data management, companies can track how they store and manage personal data, document their compliance requirements for third parties, and ensure compliance with regulations.
Data protection is an important aspect of data management. It ensures that personal data is protected and kept confidential, and that it is used only for the purposes for which it was collected. A data management system can help to promote data protection by ensuring that companies provide the necessary technical and organizational measures to protect data from loss, theft, or misuse.
Technology
In addition to strategic planning and governance, data management provides technical system architecture and tool support to effectively implement data use cases and meet governance requirements.
Developing and utilizing data technologies is a critical component of achieving data excellence. This includes creating a data architecture that enables flexible and cost-effective access to all data required for business use cases. Additionally, providing data governance tools that support daily data-driven tasks for experts is crucial.
A well-designed data architecture ensures that data is easily accessible and used effectively, enabling the organization to achieve its goals. By providing the necessary tools to manage data, data management helps ensure that the organization can make informed decisions based on accurate and reliable information.
Data Governance Tools
Data Governance Tools are software applications or platforms that companies use to manage, monitor, and enforce their data governance policies and processes. These tools can help companies automate and streamline various aspects of data governance, such as data quality control, access control, and compliance reporting.
Some common examples of data governance tools are data catalogs, technical data dictionaries, data lineage tools, and data governance frameworks. These tools help organizations to ensure that their data is accurate, consistent, secure, and used in a way that is consistent with business objectives and data protection requirements.
Data technologies
A comprehensive data architecture enables companies to effectively collect, store, analyze, and visualize their data. Customer data, which is often critical to implementing use cases, is typically stored in various technical systems such as CRMs and ERPs. Effective data management ensures an overall view of the data, independent of these disparate systems. Depending on the use case, data can be modified and consistently transferred back to all systems.
Examples of data technologies and application areas
Technologies such as data lakes or data warehouses, extraction and integration tools, and visualization tools are examples of how data management can support these efforts. These technologies enable the integration and analysis of data from multiple sources, providing a single source of truth, especially when combined with entity resolution technology, such as Tilores. Visualization tools can then be used to provide a clear and concise view of the data, enabling stakeholders to make informed decisions based on reliable information. By establishing a comprehensive data architecture and utilizing the appropriate tools, companies can effectively manage their data and derive valuable insights to support their business objectives.
What are the advantages of data management?
Companies that have successfully implemented data management initiatives have demonstrated that the benefits of data management are particularly evident in three key areas: customer experience, product fit and innovation, and operational excellence.
Customer Experience
A significant advantage of implementing data management is its positive impact on the customer experience. About 52% of companies that have implemented data management have reported an improvement in their customers' experience. By gaining better insights into their data, companies can understand their customers' preferences and purchasing habits better. Access to essential customer and product data also enables companies to provide personalized and seamless customer journeys.
For instance, leading eCommerce companies like Zalando have integrated data management processes across their entire value chain, from sales and purchasing to delivery. Efficient data management plays a vital role in meeting real-time information needs such as product availability and delivery times, strengthening customer loyalty (41%) and retention (39%). Moreover, data analysis helps companies target customers more effectively, leading to new customer acquisition (36%).
Product-Fit & Innovation
Effective data management provides valuable insights that contribute to the development of innovative products and services. By leveraging data, product managers can identify gaps in the market and better understand customer needs and preferences. With the help of data management, these insights can be democratized and made easily accessible so that build / measure / learn cycles can run faster and more effectively. This enables companies to develop and launch better products and services to customers (36% of companies implementing data management see this effect). Moreover, data itself can be the basis of new product features and customer services. For example, companies can use data analytics to identify patterns and trends in customer behavior, which can lead to the development of new products or services.
Practical Examples
This approach has been successfully adopted by Nike as part of its direct-to-consumer strategy. The sport equipment manufacturer is using predictive analytics to offer its customers tailored services for their physical products. Fitness apps ("Nike Running"), personalized workouts, and new loyalty features such as the "SNKRS" app are a selection of new, data-driven customer services.
An optimized pricing strategy
In addition, data management is a crucial prerequisite for an optimized pricing strategy (43%). With the help of effective data management, companies can better assess (e.g., based on historical data) and predict (e.g., considering weather data, holidays, or events) the demand for their products. The price can be dynamically adjusted according to the demand, and procurement can be optimized early on. Overall, profitability can be significantly improved as a result.
Operational excellence
As the amount of data processed continues to grow, so does the effort required for storage, maintenance, and protection. With the help of well-integrated data management tools and processes, companies can automate and optimize many of these processes. This not only reduces manual effort in data querying (35%) and data maintenance (25%), but also ensures high data quality and fast availability.
Your start into data management
You want to learn more about data management? Our Data Strategy Playbook gives a comprehensive overview on the four data management dimensions and provides an approach to build up data capabilities in your organization!
Author: Niklas Deistler
Niklas is a data expert, analytics trainer, and innovation enthusiast with a passion for smart technologies and agile work practices. At neoverv, he helps organizations unlock the full potential of their data. Learn more here: neoverv.com
