This article was written by Dorian Allen and originally appeared on the Collibra Blog here: https://www.collibra.com/blog/7-founding-principles-for-adaptive-data-and-analytics-governance
As new technologies and innovations enter the market, CDOs are faced with two opposing organizational forces– pushing forward and pulling back. Although the enterprise demands business growth through digital transformation and optimization, the enterprise also constantly experiences challenges due to the lack of modern data and analytics governance. CDOs recognize the need for analytics as a critical component to decision-making, but their efforts to take advantage of analytics are often unsuccessful. In fact, according to Nasdaq, 80% of analytics projects fail because of poor data governance. Organizations need to adopt adaptive data and analytics governance to generate business value from their data. In order to take advantage of data and analytics, organizations need to start with 7 critical founding principles for adaptive governance:
- Value and outcomes
- Accountability and decision rights
- Transparency and ethics
- Risk and security
- Education and training
- Collaboration and culture
Organizations need adaptive data and analytics governance
Data governance is the practice of managing data and processes to facilitate collaboration and compliant access to data. Adaptive data and analytics governance takes the concept of data governance further by underscoring data governance’s role in driving business value. Adaptive data and analytics governance is context-aware, promoting flexible decision-making and business outcomes.
“Adaptive data and analytics governance is the organizational capability that enables context-appropriate governance styles and mechanisms to be applied to different data and analytics scenarios in order to achieve desired business outcomes”
Saul Judah and Andrew White, Gartner (ADAG report)
Traditionally, data governance has been a defensive tactic. Adaptive data and analytics governance, on the other hand, addresses offensive and defensive use cases alike. By blending the defensive style of traditional data governance and the offensive needs of data-driven organizations, adaptive data and analytics governance promotes business outcomes while balancing risk.
As organizations move toward digital business transformation, data and analytics leaders will have to look to data to make impactful business decisions. With adaptive data and analytics governance, leaders can unlock the value of their data and overcome roadblocks that prevent efficient data use.
7 founding principles for ADAG
According to Gartner, there are seven founding principles for adaptive data and analytics governance. Below we outline these must-have principles and the benefits for data and analytics leaders looking to establish a successful data governance framework.
Value and outcomes
Governance practices tend to be data-oriented, rather than business-oriented. To solve this problem, data and analytics leaders need to make sure that all governance activity is directly connected to business objectives and outcomes. By connecting data governance practices to business outcomes, organizations can more easily identify the right data and analytics metrics to ensure informed business outcomes.
Accountability and decision rights
Data governance supports effective data-driven decision-making, so organizations need to have clear frameworks and standards for accountability and decision-making. It is essential to create a decision rights model that ensures business leaders understand where and how assets are created, consumed and monitored. Enforcing a decision rights model demands that business leaders are accountable for their new decisions and actions. Of course, this also means that data and analytics leaders are held responsible and accountable for the quality and usability of the data available to these leaders. Thus, data and analytics leaders must establish a framework for accountability and decision rights so that business stakeholders can have confidence in their data and the actions they make based on this data.
Assets come from many different sources:enterprise systems, brokers, line of business and more means some assets are inherently more reliable than others. The data ecosystem is dynamic; data and analytics are created, consumed and controlled in various ways. Governance must support lineage and curation to show how data transforms and flows. Organizations need an inventory of data assets segmented by opportunity, value and risk. This lineage and curation can help ensure data is trustworthy and fit for use.
Transparency and ethics
As organizations introduce new technologies such as AI, transparency becomes more difficult. This includes corporate, legal, HR and finance, which all have personal information data that must be handled ethically. The General Data Protection Regulation requires compliance and transparency through clear, defensible and documented procedures. Data leaders need to define and document principles and standards for ethics and create transparent data governance procedures.
Risk and security
Typically organizations govern business opportunities, risk, and security separately and follow a risk-averse approach. But risk and security are essential for business decisions, and therefore, cannot be addressed separately. Organizations need to be risk-aware, rather than risk-averse. Security and risk are not after the fact activities; they are embedded in productive processes and effective decision-making. Organizations must take a risk-aware approach instead of avoiding risk altogether to get the best outcomes from the business.
Education and training
If people don’t have the right competencies, skills, and attitudes towards data and analytics governance, the initiative will fail. However, education and training is different for various roles within the organization. Data and analytics leaders must identify the specific needs of certain people to ensure the right education and training is conducted. Through webinars, training modules and how-to-guidelines, organizations can create a continuous learning process that ensures the right governance skills at the right time for the right people.
Collaboration and culture
Cultural change is typically perceived as a huge undertaking, and therefore, is usually ignored. But change is crucial to a successful data governance program. The first step to embracing change is realizing that a shift in the governance mindset from control to collaboration is critical for adoption across the enterprise.Leaders must circulate this concept through storytelling about personal experiences– changing policies and standards alone won’t change behaviors. Stories foster trust and collaboration instead of control. Organizations that focus on collaboration and culture instead of control will see the most value from their data governance initiatives.