Data & Analytics Leadership and Vision for 2019

Gartner

- Establish a trusted foundation of data management
- Increase the data literacy of all roles in the enterprise
- Scale data and analytics capabilities for the complexity of modern digital business
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Spotlight

The honeymoon of business and Big Data is over. Lately, Big Data has been the target of a bit of backlash, including from the New York Times, Harvard Business Review, Wired and the Financial Times. That’s probably because we’ve reached a moment of truth—a point where, early vision aside, companies must figure out conclusively how to use Big Data analytics in profitable ways.

OTHER ON-DEMAND WEBINARS

A Review of the Top Game Changing Data Trends for 2018

infogix

During this interview-style webcast Infogix’s Emily Washington, SVP of Product Management looked ahead in 2018 and predicted what trends will come to fruition, what new concepts will graze the horizon, and what may fail to launch.
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The Missing Link in Data Governance

infogix

Key topics discussed during the webinar: *Data in Motion pitfalls and opportunities *Validating data doesn’t equate to slowing down your process *Comparing and contrasting approaches *The missing link in data governance explained
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Financial Analytics Fundamentals Use Data Science And Analytics In BFSI

analyticsindiamag

For a data analyst, financial analytics is one of the most useful skill sets to inculcate given the rapid growth of roles in this booming sector. A specialisation in this field will be a value-add given the rapid rise of fintech in India’s digital ecosystem. Financial analytics helps companies uncover deeper insights into the company’s finances and can help in finding pain points and new streams of revenue. Financial Analysts work in investment firms, banks, insurance sector and digital payments companies.
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Data Observability / DataOps using AI

Modern-day systems are transforming into complex, open-source, cloud-native services running on various environments and being developed/deployed at lightning speed by distributed teams. When working on these systems, identifying a broken link in the chain can be near impossible. Everything fails at one point or another, whether due to code bugs, infrastructure overload, or changes in end-user behavior or market driven factors or errors in data collection. This has led to the rise of DataOps with a focus on changing the organizational speed and trust in delivering data pipelines and the related artifacts by co-creating “decision quality” data with the consumers. This development has led to the idea of observability that includes monitoring, tracking, and triaging incidents to prevent downtime of the systems and around several factors such as freshness, distribution, volume, schema, lineage.
Watch Now

Spotlight

The honeymoon of business and Big Data is over. Lately, Big Data has been the target of a bit of backlash, including from the New York Times, Harvard Business Review, Wired and the Financial Times. That’s probably because we’ve reached a moment of truth—a point where, early vision aside, companies must figure out conclusively how to use Big Data analytics in profitable ways.

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