used with permission from IBM Big Data & Analytics Hub

data democratizationData democratization enables users to access data across an organization and empowers them to use data in decision making to gain critical business insights. Data democratization is fast becoming a game changer as it’s moving toward a user-centric, microservices-based architecture.

This user-centric architecture is being driven by user requirements to check the accuracy of data through a self-service interface, use enterprise search capabilities to find reliable data, and ensure any personal data has been masked or removed.

Enabling the modernization of enterprise analytics to drive better insights and compliance requires data quality organizations to invest in platform modernization and simplification to help enable self-serve analytics to accelerate data governance.

Accelerate data quality and governance by making data simple and accessible

For organizations to best use analytics, they should start with an information architecture that breaks down organizational silos in addition to data silos. There is no artificial intelligence (AI) without information architecture. Analytics-infused machine learning and AI are only as good as the underlying data quality.

Data should be so easy to access that analytics becomes part of day-to-day operations without making users think about how to get the data.

What is the value of making governed data easy to access and self-serve? It empowers users to make high-quality data decisions, encourages collaboration and knowledge sharing, and enables shop for data. It can help users protect sensitive data, enforce governance rules and understand the regulatory impact on data.

For organizations following data quality and governance practices, it enables the use of information in new ways that can help drive innovation, increase operational efficiency and protection, and lower risk.

Empower users with self-serve analytics

When organizations deploy end-to-end quality tools, they empower their users to understand data and its relationships; analyze and monitor data quality continuously; cleanse, standardize and match data; and maintain data lineage. Users gain real-time customer data insights while meeting their reporting needs.

What if you provided your users with a way to search across all your data sources to find the information they need? And wouldn’t it be even better if you could use the same natural language search capabilities you use when doing a Web search? Machine learning can look at data and explain it to non-technical users.

These new capabilities empower users to: check the accuracy of data through a self-service interface, use enterprise search capabilities to find reliable data and graphically display its relationships, and ensure personal data is identified for policy-driven masking.

What’s new with IBM InfoSphere Information Server v11.7

IBM InfoSphere Information Server provides a single platform for data integration, quality and governance. The integrated components in the suite combine to create a unified foundation for enterprise information architectures capable of scaling to meet any information volume requirements.

What’s new? InfoSphere Information Server v11.7 has an array of new capabilities, including machine learning and automation of many tasks. Most importantly, it helps accelerate opportunities to deliver self-service analytics and data governance.

Breaking down silos and adopting a collaborative approach to drawing insights from data will provide a competitive advantage help with effective decision making.