In recent years, advanced technology like AI, big data, and machine learning is becoming a central component and essential part of data governance. However, many companies still rely on outdated legacy approaches, processes, and systems to govern their data, an ever-important task in this data-driven world. While critically important to corporations, data governance is equally relevant from the customers’ perspective as customers become aware of their data rights and the value of the data that companies collect about them.
According to the Data Management Association (DAMA), “data governance is the exercise of the authority and control (planning, monitoring, and enforcement) over the management of data assets.” It consists of the policies, processes, and organizational structure that support enterprise data management. In recent times, a few major technological trends have initiated a significant shift toward adopting enhanced data governance frameworks.
- Companies’ omnichannel presence provides enhanced user experience generating vast data lakes of consumer data.
- Data science, AI, and intelligent automation have become important in most enterprise domains, making it crucial for governance to include data pipelines and algorithms.
- The growing use of the internet and the Cloud has resulted in an unprecedented amount of unstructured data.
- Self-service analytics are driving real-time insights and supporting the campaign for quality data.
Essentials of data governance
Data governance frameworks typically have a broad reach and usually vary according to organizational needs. However, certain aspects of data governance are vital core ingredients of a framework that can improve operational efficiency and effectiveness.
Accountability – assigning custodians to relevant datasets.
Accessibility – ensuring the access and availability of data to relevant stakeholders.
Security – establishing data safeguard measures to guarantee the security of the data.
Quality – creating continuous monitoring processes for data quality to ensure its conformance to standards.
Knowledge – maintaining data systems and process documentation for transparency and to improve data knowledge throughout the organization.
How AI helps?
Every day, more and more data is added to the endless pool of data accumulating from multiple sources within the enterprise, thus making data management and governance challenging at best. With the unique capability of AI-driven systems to learn from experience and adapt accordingly, enterprises can extract maximum value from their data by setting up these systems that automate the management of data governance policies. AI systems quickly and effectively recognize both best practices as well as inefficient practices.
AI emphasizes providing high-quality data. Organizations can leverage this data and improve their operations. For instance, AI helps in providing valuable insights into customer behavior and purchasing patterns. Whether forecasting demand-supply requirements, predicting maintenance of industrial equipment, detecting a wide range of anomalies, and much more, AI provides the critical intelligence to automate data governance.
AI provides data security. An effective AI-based solution safeguards data from cyber threats. Machine learning algorithms help identify cyber-attacks by recognizing patterns and notifying authorities before data is breached or compromised. AI applies automation to data privacy, security, and compliance. AI and machine learning automate data operations, metadata management, user identification, access management, two-step verifications, data permissions, and many other processes.
AI-powered data governance framework
Addressing the data governance requirements of corporations in today’s operating climate needs a new general framework, custom-built for the organization. An AI-powered data governance framework is vital to this corporate data strategy promising improved transparency and increased efficiency. For an effective AI adoption in data governance, consider the following steps
- Adopt a tailored approach that reflects the unique aspects of AI, machine learning, and automation by considering the reliability, quality, and accuracy of source data.
- Establish an intelligent governance framework adopting algorithms that create audit trails reflecting the data pipeline’s important underlying variables, their selection, and their weights.
- Incorporate data science and AI early into the organization to foster communication to improve data collection, organization, and storage, including capturing insights that facilitate better understanding and optimize consumption from data lakes.
- Tap into in-house expertise to contribute to designing intelligent, AI-driven data quality checks that augment and enrich the data governance process.
- Involve the right people (c-suite executives, subject matter experts, data analysts, data scientists, data ops, infrastructure personnel, and security and legal representatives. The more varied the skillsets, the more diverse the perspectives, and the higher the collective contributions to optimal data governance policy and increased value of governance councils.
- Transform manual processes to digital capable of addressing higher volumes of data and enabling a more responsive enterprise.
- Establish comprehensive, specific, and informed policies (data privacy, security, accuracy) that reflect global regulations across geographies, languages, and time zones.
In this age of cognitive tech, AI, machine learning, and deep learning are as important as the data they process. Better management and governance of data leads to higher value creation and improved insights for the firm, positively impacting revenue and the bottom line. Hence, digital data governance powered by advanced technology is now an operational imperative rather than a “nice-to-have” for the informed organization.
To learn more about adopting AI-powered data governance, send us an email to firstname.lastname@example.org. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with . develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.