Only a small percentage of potential analytics users have any meaningful access to data.
In addition, many data and analytics initiatives fail to deliver the business goals originally set.
Knowledge workers spend too much time looking for and manipulating data, and not enough time analyzing information.
IT is kept busy with growing demands for data – harvesting, cleaning, staging.
Requirements change way too fast for IT and traditional technology to keep up.
These challenges are heightened as generative AI tools become more widely available and adopted.
How to quickly provide access to data, enable analysis (self service) and still maintain governance?
Key Architectural Considerations:
- Connect data across your heterogeneous environment
- Integrate data and take a measured approach to data quality
- Build data assets for end-user access
- Gain visibility to and manage end user created content
- Scale analytics from the desktop to the datacenter
Technology Capabilities (Control)
- Connect and provision
- Monitor and Manage
- Deploy and Future Proof
Key Analytics Capabilities (Agility)
- Find and Access
- Mash-up and Analyze
- Share and Collaborate