Data is everywhere, the insights are exciting and it can power the next generation of systems investment. This is the accepted wisdom, but how do you get started? Unless you work in a technology led business, IT teams rarely lead business innovation, yet they are typically the key gatekeeper to unlocking the power of organizational data. The evolution of IT to cloud, dev-ops, micro-services and containers should be keeping the IT team busy. This is even before considering master data, data lakes, governance and moving away from traditional Extract Transfer Load (ETL) procedures.
Exploiting new revenue opportunities and cost savings from data needs to be driven using a business transformation lens. The priorities and needs of the business balanced against speed and risks of implementation are critical success factors for any data science initiative. This is difficult for most people to conceptualize and is why rapid prototyping in "AI Innovation Hubs" is an excellent way to demonstrate concepts and likely benefits. Seeing the results of a prototype along a business case and agile implementation plan is excellent way to rally key stakeholders to further develop and launch the initiative. This should be done outside existing IT and data architecture, but mapped into how it can be "productionized" as part of the plan.
AI Innovation Hubs are key to kick starting new and exciting applied data science projects. Working within the confines of existing data insight normally means working within processes and parameters of existing IT. So it is much better to work outside existing frameworks, but co-developed with data insight teams in the AI Innovation Hub. This ensures:
Overall, these benefits will lead to wider organisation efficiencies from faster access to more relevant data using less processing time and less analyst time. This ultimately results in significantly raising efficacy from insight, and substantially lowering costs.
Want to get started with an Innovation Hub? Get in touch!