Data Governance with Cloud Pak
Here in this blog, we are going to learn about data governance with cloud pak.
Data fabric is an architectural approach that helps ensure quality data can be accessed by the right people at the right time.
In addition to providing a strong foundation for multicloud data integration, 360-degree customer intelligence and trustworthy AI, the data governance and privacy capability of a data fabric strengthens compliance with automated governance and privacy controls, while maintaining regulatory compliance no matter where data resides.
Strong governance makes the right, quality data easier to find for those who should have access to it, while allowing sensitive data to remain hidden unless appropriate. Having insights into your business and customers is a competitive advantage. The Forrester Analytics Business Technographics Data And Analytics Survey, 2020, found that advanced insights driven businesses are more likely to have adata governance strategy that involves defining, executing, training, and overseeing compliance than beginner and intermediate firms, to have an executive in charge of their data governance, and to use AI to crowdsource and embed data stewardship in everyday data engagements.
Strong privacy parameters help increase readiness for compliance and data protection anywhere, on-premises or across clouds. They allow businesses to understand and quickly apply industry-specific regulatory policies and governance rules on data wherever it resides.
In this guide, we’ll look at the most common governance and privacy challenges modern organizations face, the building blocks of an effective solution/approach, and the technology components you’ll need to build an automated, integrated data governance and privacy layer across all the data in your enterprise. We’ll also provide helpful resources such as a data governance and privacy trial.
Why establish automated data governance and privacy
As organizations strive to establish cultures of data-driven decision making, the ability to rely on quality data that is compliant with a dynamic regulatory environment is critical. Such an approach allows organizations to deal with challenges such as:
1. The need for data privacy at scale
The risks of non-compliance (such as legal penalties, loss of customer trust, and loss of reputation) are real. More than 60 jurisdictions around the world have enacted or proposed privacy and data protection laws, and by 2023 more than 80% of companies worldwide will face at least one privacy-focused data protection regulation.
Rather than responding to each challenge individually, a proactive approach to privacy and data protection is an opportunity for organizations to build customer trust. But to do it, data leaders need to build a holistic privacy program across the organization.
2. The need to improve data access
Secure data sharing is a crucial factor when multiple teams require access to enterprise data. That data must be traceable and only visible to those who are authorized to use it. Yet 7 in 10 organizations are unable to secure data that moves across multiple cloud and on-premises environments.
Without being able to ensure compliance at scale and from one environment to another, teams hesitate to share data between business units, deepening silos. This causes IT teams to have to protect and ensure each data repository on an individual basis and can lead to groups spinning up their own repositories (shadow IT), which only leads to more complexity.
3. The need to maintain data quality standards across the organization
Only 20% of business executives completely trust the data they get.5 Every year, poor data quality costs organizations an average $12.9 million, according to a recent Gartner report. Gartner predicts that by 2022, 70% of organizations will rigorously track data quality levels via metrics, improving it by 60% to significantly reduce operational risks and costs.
For all users throughout an organization to be able to fully understand and have confidence in the data they are about to use, a data governance foundation of business definitions and metadata is essential. This foundation includes business terms, data classifications, reference data, associated metadata, and the establishment and enforcement of data governance policies and rules.
4. The need for data lineage and traceability
Once analytics teams have built and deployed data products (such as dashboards, reports, and machine learning models), they need to be able to look back and see where the data product came from. For auditability and compliance use cases (often in regulated industries), an analytics team may be required to show all the steps taken in the life of the data as it has been transformed from the transactional system where it was originally created into its final form as it is used to support business decision-making. And for end users, being able to see the data sources and transformations can save a great deal of time as they build their own customized version of the dashboard.
5. The need to facilitate data consumption
To leverage the innovative and disruptive power of data, enterprises need to enable self-service data consumption. The ability to simplify data access and consumption is predicated on a robust framework and architecture that ensures data users in an organization can easily find and use the right data with a rich and metadata-driven index of cataloged assets. Data governance and privacy proactively enables enterprises to satisfy the need to drive innovation and meet business outcomes.
The building blocks of governance and privacy
1. Data cataloging
The quality of your data determines how confidently you can act on insights. If low quality data goes into AI models, it could lead to inaccurate, noncompliant or discriminatory results. Getting the best insights means being able to access data that is fresh, clean and relevant, with a consistent taxonomy. A data catalog can help users easily find and use the right data with a rich and metadata-driven index of cataloged assets.
2. Automated metadata generation
Metadata tracks the origin, privacy level, age and potential uses of your data. Manually generating metadata is cumbersome, but with machine learning, data can be automatically tagged with metadata to mitigate human error and dark data. Automatic tagging of the metadata allows for policy enforcement at the point of access, so that more sensitive data can be used in a nonidentifiable and compliant way. In addition, metadata is used to establish a common vocabulary of business terms that provide context to data and to link data from different sources. This context adds semantic meaning to data so that it becomes more findable, usable and consistent within the organization, a key factor when seeking data for analytics and AI.
3. Automated governance of data access and lineage
Data lineage shows how data has been accessed and used and by whom. Knowing where data comes from is useful not only for compliance reporting but also for building trustworthy and explainable AI models. And it can be automated without complicating access. With restrictions built directly into access points, only the data users are authorized to access will be visible. Additionally, sensitive data can be dynamically masked so that models and data sets can be shared without exposing private data to unauthorized users. This clarity around what data can and can’t be used supports self-service data demands and allows organizations to be nimble in responding to line of business needs.
4. Data virtualization
Data virtualization connects data across all locations and makes the disparate data sources appear as a single database. This helps you ensure compliant access to the data through governed data access, regardless of where it lives, without movement. Using the single virtualized governed layer, user access to data is defined in one place instead of at each source, reducing complexity of access management.
5. Reporting and auditing
Enterprises must comply with a wide variety of changing regulations that differ according to geography, industry and data type. They need to be broken down into a catalog of requirements with a clear set of actions that businesses must take. Regulatory information should be automatically ingested, deduplicated, and applied to workflows.
The secret to harmonizing all these data privacy and governance needs with business opportunity is aligning the technology components with a global data strategy and an open and holistic architecture.
