How to Search, Explore and Visualize Neo4j with Linkurious - Jean Villedieu @ GraphConnect SF 2013

Operational Data Governance is more than a stewardship process for critical Business Assets. As organizations build structure around KPI’s and other critical data, a workflow develops that revolves around the sources and supply chain for that critical data. There can be many aspects to changes and inconsistencies affecting the final results of the supply chain. Inaccurate usage of data can result in audit penalties as well as erroneous report summaries and conclusions. Is it coming from the correct authoritative source? Has the data been profiled? Has it met it’s threshold? Gaps in the supply chain from incorrect pathways may lead dead ends or lost sources. The value of understanding the entire supply chain cannot be overstated. When changes occur at and point, end users can validate that correct business standards, rules and policies have been applied to the critical data within the supply chain. Your organization can rest easy that you are not at risk for exposure due to improper usage, security, and compliance. Join this webinar to uncover how companies are using data lineage to accomplish data supply chain transparency. You’ll also see the direct value clear data lineage can give to your business and IT landscape today.

Data lineage is a regulatory and internal requirement with potential to deliver significant operational and business benefits, but financial institutions can find it difficult to implement and complex to maintain as systems and regulatory requirements themselves, change quickly. The importance of understanding where the true source of the data is coming from, where the data flows to and what has changed cannot be overstated. The webinar defines data lineage and discuss implementation through the eyes of those that have implemented and sustained successful lineage solutions with significant benefits. Listen to the webinar to find out about: - Data management for data lineage - Winning buy-in for projects - Best practice implementation - Operational and business benefits - Expert practitioner advice

This document outlines the framework and progress made in the first year of a metadata management and data lineage program at a large healthcare organization. The objectives are to establish information stewardship, improve data quality, and document enterprise data flows. In the first year, documentation requirements and templates were developed, training was provided, and some database teams began documenting data flows in PowerDesigner. While progress has been made, fully documenting all enterprise data flows is a multi-year effort.

The document discusses developing an effective data strategy. It begins by introducing Micheline Casey and Peter Aiken, experts in data strategy. It then discusses what a data strategy is, why it is important to have one, and key characteristics of an effective data strategy. The document outlines the process for developing a data strategy, including pre-planning, aligning with organizational goals, prioritizing initiatives, and performing assessments. It emphasizes the importance of implementing foundational data practices before advanced practices. The presentation concludes with discussing challenges to developing a data strategy and taking a question.

Apache Atlas provides centralized metadata services and cross-component dataset lineage tracking for Hadoop components. It aims to enable transparent, reproducible, auditable and consistent data governance across structured, unstructured, and traditional database systems. The near term roadmap includes dynamic access policy driven by metadata and enhanced Hive integration. Apache Atlas also pursues metadata exchange with non-Hadoop systems and third party vendors through REST APIs and custom reporters.

In the world of big data, legacy modernization, siloed organizations, empowered customers, and mobile devices, making informed choices about your enterprise infrastructure has become more important than ever. The alternatives are abundant, and the successful Enterprise Architect must constantly discern which new technology is just a shiny object and which will add true business value.