To extract the maximum performance from data environments, both at an operational, commercial or business knowledge level, one of the most relevant aspects is to propose the best practices for data governance and analysis.
Without good governance, investments in data and analytics will fall short of meeting organizational demands, revenue growth, cost optimization, and the best customer experience.
In addition to best practices, practical principles need to be established to create an effective foundation for data and analysis.
It may be the case that you have difficulties in identifying the governance principles to improve, due to not having a clear reference point for the development of best practices in the key principles.
1.- Align government with business results
Governance efforts must be directly related to business strategy and priorities. However, organizations often orient their governance practices around data rather than business, making it difficult for data owners to have constructive conversations with business leaders.
To best support business outcomes, governance policies and standards must be aligned with business priorities, business process metrics, and data metrics.
Business value and business results must be at the heart of governance, with clear business metrics. These metrics must be personally attributed to stakeholders. Finally, open reviews with key decision makers should be arranged and strategies contemplated to improve your business results.
2.- Maintain a Model of Responsibility and Decision Making
A model of responsibility and rights for decision-making is critical to the success of any data effort. This provides the necessary oversight to ensure that the right people are held accountable for the decisions they make and that stakeholders have confidence in the governance decision-making process.
3.- Government based on trust
Data and analytics assets exist everywhere in an organization and vary in nature, so making business decisions based on the assumption that “all information is equal” is not a good approach. It is necessary to establish a governance model based on trust that:
- Supports a distributed Data and Analytics ecosystem
- Recognizes the different origin and treatment of assets
- Help leaders make contextually relevant decisions with greater confidence.
Building that trust requires technologies that allow data to be presented as a catalog, and help discover, assess, and control assets and analytics across the organization's ecosystem.
4.- Ethics and Transparency
For effective digitization, all data governance must operate based on the principles of transparency and digital ethics. Data and analytics governance decisions must be clear, defensible, and documented. The data and analytics leader must establish a digital ethics framework that can be implemented throughout the organization.
It must be ensured that the principles are aligned with the mission, vision and strategic values of the organization, including those of digital ethics. The relevant managers must be designated and the basis on which decisions are made must be clearly communicated.
Data governance and analysis operating procedures must be clearly auditable, highlighting the decisions made, the actions derived from them, the related investments and expenses, and compliance with digital ethics.
5.- Risk Management and Information Security
In large organizations, risks are, in addition to being imposed by the regulator, a working principle. This means that they represent opportunities created by Data and Analysis aligned with risks. It usually happens for historical reasons, that business opportunities and risks are governed separately, and information security is not considered as a key component when evaluating the results.
Data governance bodies must be multidisciplinary so that they can make balanced decisions, giving weight to opportunity, risk and security, and taking into account the long-term interests of the organization.
Metrics to assess governance decisions must therefore indicate business value, future risk and opportunities, and potential information security gaps. To address risks in real time, a control environment is required and integrated into the business information security framework in it.
6.- Training and Education
Data governance initiatives involve new behaviors and attitudes of people, following expectations established by policies and standards. But it's not always clear exactly what these new behaviors should be.
A best practice is to include Human Resources expertise and plan for learning and development to support data governance best practices. Also work with the Human Resources team to discuss government-related roles to get an idea of the skill set required and develop training modules consisting of webinars, blogs, or guidelines to provide relevant and up-to-date learning material. Ultimately assess impact to help people make better governance decisions and make needed development improvements.
Clear, well-defined and measurable goals should be set for the data and analytics role. For example, completing specific training modules on data governance best practices can become part of people's annual goals.
7.- Promote Cultural Change and Collaboration
Since data governance and analytics decisions can be made across the organization, highly collaborative skills need to be developed rather than centralized. The government cannot be perceived as a bureaucratic activity; rather, it should focus on people-to-people interactions, storytelling, knowledge sharing, and innovation.
Taking an internal, candid survey of how data is currently perceived in the organization, attending executive meetings, town halls, and other sessions, finds out what needs to change culturally and develops a story-driven narrative to explain how data governance and analytics can address real challenges that do not lead to digital fatigue.
