Data sharing to support analytics was once perceived as a one-off activity—data extracts were handled on a case-by-case basis and near real-time access to transactional data was unavailable. Data changes were frequent, support was non-existent, and every request was a favor.
Today, data sharing and analytics are no longer backroom activities. Analytics has become a commonplace activity that most businesses require to support day-to-day operations. Unfortunately, most data management methods haven’t evolved.
Data needs aren’t limited to the systems within the company’s four walls; users require content from cloud applications, social media, third-party providers, and business partners. Supporting corporate data as an asset and managing today’s diverse sources, large volumes, and demanding users requires a new and different approach.
A modern data environment should enable a business person to identify, distribute, process, and analyze data without requiring technologists to support each individual need. IT data specialists must be elevated to focus on expanding the corporate data catalog and simplifying data delivery and usage to strengthen users’ data capabilities.
In this session, Evan Levy will discuss new and innovative approaches being taken to address the explosive growth of data content, data sharing, and data usage.
You Will Learn
- The business data ecosystem and the changes in data usage and sharing inside today’s companies
- The five basic dimensions of data usage
- An approach to aligning your data architecture to support your company’s unique data requirements
- A new data architecture template to support the massive growth in data source needs and analytics diversity
- Tactics for managing data movement within (and outside) of your company
- Methods to review tooling to simplify and automate data access and usage
- Processes for positioning users as stakeholders in data improvement processes (quality, correction, monitoring, etc.)
- Ways to manage data content at the enterprise, organization, and user levels
- CIOs and chief data officers; IT program managers; business sponsors and end users; BI program management; data management staff