D&A (Data and Analytics) Platform Governance – Data Democratization, Governance, and Security

D&A (Data and Analytics) Platform Governance

A data and analytics platform governance covers the set of integrated data and analytics technology capabilities of the platform. This platform enables governance and stewardship for a range of policies and procedures spanning security, quality, access, retention, and privacy. This includes execution and enforcement of policies for all stakeholders (e.g., business and data stewards, business analysts, internal/external business users, and governance board members).

Analytics teams require the following:

•    Ability to find critical data and linkages among data relationships;e.g., workflow of data, identification of data sources, association of business terms to technical artifacts

•    A platform for fast processing and storage space irrespective of the size of data

•    Easy access and interoperability to multiple sources within organization or external data

•    Availability of high-quality data as source of truth. Auto-generation and execution of DQ (data quality) rules by implementing DQ rule engine

•    Effective support in governance and enforcement of policies, such as privacy, security and data quality. Also data definition and automated classification of sensitive data

•    Transparency in creation, consumption, and control of data

•    Easy access to capable reporting and dynamic visualization tools An organization needs to have federated but distributed, integrated, automated, synchronized, and cost-effective governance solutions. All of these requirements can be provided by a single platform. There are multiple technology platforms and vendors available in the market that can satisfy these requirements. Single platform does not necessarily mean single tool; it can minimize the number of tools and solutions if it has all the mentioned capabilities.

Tools: Collibra, IBM, Informatica. SAS

Analytics Platform Governance

As organizations are adopting advanced analytics including AI, the platform for analytics is marked with wide data, Big Data, and lake houses. All the major cloud platforms

has the capability of self-service, advance analytics, predictive models, and artificial intelligence. The platform is capable of MLOps and rapid prototyping of analytics outputs by users closer to the point of build decisions.

An analytics platform governance charter is validated with policy setting and enforced through stewardship and policy execution through management, along with your analytic tools. This will reduce redundancy, save money, and lead to improved outcomes.

The tool can be enabled by lineage, but tracking data lineage is not data governance. That is nice, but not sufficient. Organizations need to prioritize and select the most important domain in the organization’s data and analytics that drives its most important business outcomes.

Analytics governance tools can utilize data and analytics platform tools. Data lineage and third-party and regulatory compliance with data privacy, security, access, and quality can be covered by tools available in the market.

Suggested Tools: Collibra