We’re in a digital economy where data is your most valuable asset. Data management can literally make or break your business.
Data governance is essential to an organization’s overall strategy for data management and as part of a complete DataOps practice. Data governance practices provide a holistic approach to managing, improving and leveraging data to help you gain insight and build confidence in business decisions and operations while meeting regulatory requirements.
While digital transformation, compliance and other data-driven initiatives are desired outcomes, few organizations know what data they have or where it is, and they struggle to integrate known data or achieve compliance. But when IT-driven data management and business-oriented data governance work together in terms of both personnel, processes and technology, decisions can be made, and their impacts determined based on a full inventory of reliable information.
Respond to audits and demonstrate compliance with data regulations through proper reporting and documentation, including lineage.
Failing to establish vision in Data Governance will result in increasing costs and reputation thread.
Stop wondering about those results. When you have trust in your data you don't have to double check all the time.
Take full advantage of data analytics tools and ensure the validity of those reports and dashboards because the underlying data is accurate.
Strengthen data privacy and security policies. Protect your data assets and be transparent about their usage. Assess the value and risk of personal data. Help secure PII, PCI and PHI.
Ensure adherence to those standards, so you can ensure compliance, avoid data breaches or other missteps.
Use machine learning to curate metadata, manage assets and share knowledge.
Build consistency in data standards and business definitions. Ensure everyone involved is data literate, understands the meaning and is fluent in how your organization defines and talks about it.
Activate business-ready data for AI, new products and services or ensure existing ones are delivering properly.
Know your data to modernize business, open new opportunities and grow.
Whether you work in the CDO’s office, in the IT department or as a LOB data scientist or analyst, you and your colleagues share a common goal. If you can establish data governance that really delivers on its promises, you could not only make your own jobs much easier and more productive. Additionally, you could play a key role in giving your business a competitive edge that few organizations can currently rival.
Data quality capabilities help you to improve the quality of your data and make high-quality data available. Governance policies are automatically set and enforced—so when you find a data set, you know whether and how you are allowed to use it.
Drives collaboration and transforms data into trusted enterprise assets through dynamic data policies and enforcement. A single interface gives you access to every data set your organization owns, regardless of where it’s stored.
Optimize storage costs by avoiding the expense of ingesting low-value data sets into your data lake or data warehouse. Prioritize the ingestion of new data sources based on users’ demand, helping you integrate the most valuable sources first.
Automatic data discovery reduces the time and effort you need to spend adding metadata for new data sets and also reduces the business glossary creation time. Intelligent, AI-powered search helps you find the data you need within seconds, instead of waiting weeks for another team to provide.
A single source of truth and a single point of access Reduce time to automate data discovery, quality and governance by up to 90% with market-leading capabilities:
Find relevant assets quickly and at scale based on intelligent recommendations.
Organize, define and manage enterprise data to provide the right context and drive value across needs like regulatory compliance and data monetization.
Protect data, manage compliance and audit-readiness, and maintain client trust with active policy management and dynamic masking of sensitive data.
Track lineage and quality scores across structured data, unstructured data, AI models and notebooks.
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