Data governance is an organization’s control mechanism over data. Data governance consultant requires:

(1) A Policy Framework by stakeholders to define how data will be treated,

(2) An Actionable Implementation Plan defining tools & technology and assigning responsibility for data stakeholders,

(3) Commitment to the ongoing assessment of policies and plans about business objectives.

Elements to data governance

Here are some key considerations that will help you design a plan and framework that work.

In alignment with your business objectives, align data governance activities with them. Alignment guides your data governance efforts. It also provides tangible measures of their success. You can be specific with your goals and only invite the key people to help you achieve that goal. For example: If you are looking to improve your sales forecasting, the marketing team could be the primary stakeholder. And the success of government initiatives should be obvious in the increased accuracy.

This alignment will ensure success for all the “key elements” If compliance is viewed as a burden, without demonstrating business benefits, then it is a waste of money. Compliance should be clearly defined so that benefits can be measured and easily seen.

Data stakeholder: Most data gathering, storage, and maintenance activities involve many people. To create a data governance system for a particular domain, all data stakeholders should be involved. It is vital that they fully understand and agree to the upholding and reevaluating of this framework moving forward.

Stakeholder input and buy-in are crucial for data governance’s success in achieving business objectives. It is easier for people to pay attention to data hygiene if they have some input in defining the problem and helping design the solution.

Data Taxonomy: Taxonomy is the systematic and consistent labeling, mapping, and naming of data. Taxonomy clarifies and assures consistency in the data’s meanings. Taxonomy needs buy-in and agreement from the entire organization to ensure that definitions and naming conventions are kept up to date. Inconsistent or inconsistent data taxonomies create silos and prevent data integration between platforms.

A taxonomy that has been agreed on and is understood by all can improve communication in the business. The taxonomy may provide a “source of truth” in semantic meaning disputes. However, Taxonomy will also help data workers gain insight into the larger business and other data sources that are not directly relevant to their work.

Data management and quality: A data’s value will be greater if it is managed throughout its data lifecycle, which includes data creation, storage, modification, deletion, and consumption. The policy framework must establish the quality standards and ensure that technology and tools can be used to maintain them. Based on your business objectives and the data type required, there may be differences in the quality or detail required.

The business can recognize what the lifecycle is and identify data quality weaknesses by adopting a consistent approach. Attention to data quality is beneficial for stakeholders who make data-related decisions. It is possible to establish a level of trust quickly and to modify any processes that may be affecting data quality by having consistent and well-understood assessments.

Data security: Identify the security measures that will protect your data and ensure compliance with applicable laws and standards in the country you are operating.

It is possible to gain insight into the effectiveness of data security measures and privacy measures by doing a thought exercise. Consider what would happen if the breach happened and how the media would react. What headline would this be if the data were made public and published by the media? Protect the data in a manner that is proportional to this risk.

Data accessibility: Security is essential, but authorized parties still need to have access to the data. The best tools and policies will allow for easy access, while still maintaining adequate security.

Who and what data is needed? 

If data is properly managed, it can be quickly and safely made accessible to those who have the right permission. Data access policies specify how this access will be granted.

Implementation 

A data governance framework is created by decisions regarding

(1) the technology required to protect and secure data and

(2) who will manage data inputs and how the technology is used.

Once these decisions are made policies can now be applied and technologies can be implemented for the right data stakeholders.

An effective data governance policy framework has value only when it is implemented, and not its design. It’s better to talk about the governance in terms of more of what you will get than what you have to do. Compliance can be seen as an investment rather than a burden if data workers see it as value.