10 Golden rules for Data Management

(Lessons from DAMA)

Data increasingly proves value to organizations. Data allows them to improve products, services and processes, reduce risks and gain competitive advantage by being able to predict demand more accurately.

Data Management is the key to optimally profit from the benefits data has to offer. Successful Data Management (DM) delivers the right data at the right stakeholder or process at the right time.

DAMA provides a standardized approach to organize Data Management. DAMA addresses several topics (‘knowledge area’s’) that are separately elaborated in the DAMA/DMBOK (Data Management Body of Knowledge). Think of domains like Data Storage, Data Interoperability, Data-warehousing and Data Security. Each domain describes its own objectives, activities, processes, deliverables and principles – the rules to conduct data management effectively. In total DAMA lists about 150 of those principles.

As this number of rules is hard to live by, we have analyzed all and condensed them into a manageable set of 10 golden rules that together define fundamental critical success factors of DAMA.

1. Data is an asset

Effective DM considers data as an asset, just like buildings, equipment, staff, money and IT systems are. This implies that data has value and unique properties, and cost and risks attached. This requires data to be managed, just like other assets are managed. This includes the management of the life cycle of data (from acquisition/generation to disposal) and the protection of data around all stages. Just like any asset, data is not a goal in itself: its purpose is to serve the business. Hence: Business drives Data.

2. Enterprise commitment

Effective DM requires an enterprise-wide approach: Some data is used around the entire organization, e.g. to support corporate functions like reporting. With this broad scope, executive commitment is mandatory, together with the sponsorship to develop and operate the new data practice.

3. Vision and leadership for change

Effective DM requires a clear vision to determine the direction in which the organization should be heading. A Data Management program is a change process that may stretch over many years. A visionary dot on the horizon is essential. Leadership and a proactive change management approach in order to establish the required change are required to guide the organization on the journey to the right direction. This requires continuous communication with the stakeholders. The reasons, goals and expectations for data management must be made crystal clear to anyone. And everyone actively participating should be trained and coached properly.

4. Collaboration

DM demands several different skills and requires the entire organization to be involved. This involves alignment with the business and other disciplines like IT and Enterprise Architecture. All stakeholders need to be included in the program. The Data management core team should be well balanced and equipped with all skills to be able to deliver all necessary products over the course of the program.

5. Quality

DAMA states: “Managing data is managing the quality of data.” The prime goal of Data Management is to provide data to all stakeholders with a quality that allows them to conduct their business properly. Quality therefore needs to be defined, measured and enforced by processes and guidelines. Dimensions of quality are for instance correctness, completeness and consistency of data. As managing quality involves cost, we may consider quality that exceeds the needs to be considered as waste.

6. Governance

Effective DM requires governance to ensure the quality of data. It primarily involves clear accountability and responsibility for all data assets and processes in the organization. Governance makes it clear who should define and guard the quality of these assets, and who should be called to account in case of data (quality) issues. Besides that, governance defines processes around data to manage the quality throughout its lifecycle.

7. Incremental approach

As said before a DM program usually stretches over multiple years. In times where agility is key we cannot allow to do a multi-year investment without intermediary benefits. That’s why it is eminent to regularly generate value from the DM program in so called “increments”. Think for example of a short-term improvement of data quality in customer data that immediately results in savings for delivery, as more shipments are addressed correctly.

8. Metadata

Where data is required to manage the business, metadata is needed to manage data itself. It is used to define and classify data, describe the lineage and origin, specify the quality and guide where and how to use the data.

DAMA defines measuring as an important activity to determine the values of metadata around data quality and the process effectiveness. This metadata is extremely important to measure the success of Data Management itself.

9. Transparency

Effective Data Management requires transparency for both the program, as for the data assets themselves.

Transparency of the program shows insights in delivered and planned products on the roadmap, and the cost and successes of the program (cost savings, quality improvements, value generation et cetera). It is mandatory to gain trust on the program goals and progress from management, sponsors and other stakeholders.

Transparency on data informs potential owners and users about the characteristics and status of data-products. It shows lineage, quality, classifications, limitation and other information that is important for usage of the data. Metadata is a key prerequisite for this, and transparency determines this data to be published.

10. Principles & Standards

Effective DM relies on principles and standards. They formalize the rules that are agreed upon over the entire organization. They support decision making in case of multiple alternatives and help to stay aligned with the organization’s strategy and goals. Besides that, they may address the cause of an issue or find an acceptable resolution in case of a dispute. Note that principles and rules must be clear, correct, concise and consistent to be of value to the organization.

These 10 golden rules cover a significant part of the 150 DAMA principles. Applying them will drastically increase your chances for success of your Data Management program.

Harald van der Weel10 Golden rules for Data Management

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