As with any development project, implementation of a Big Data or Data Science initiative should align with real business needs. Assess organizational readiness in relation to critical success factors:
- Business Relevance: How well do the Big Data / Data Science initiatives and their corresponding use cases align with the company’s business? To succeed, they must strongly enforce a business function or process.
- Business Readiness: Is the business partner prepared for a long-term incremental delivery? Have they committed themselves to establishing centers of excellence to sustain the product in future releases? How broad is the average knowledge or skill gap within the target community and can that be crossed within a single increment?
- Economic Viability: Has the proposed solution considered conservatively the tangible and intangible benefits? Has assessment of ownership costs accounted for the option of buying or leasing items versus building from scratch?
- Prototype: Can the proposed solution be prototyped for a subset of the end user community for a finite time-frame to demonstrate proposed value? Big bang implementations can cause big dollar impacts and a proving ground can mitigate these delivery risks.
Likely the most challenging decisions will be around data procurement, platform development, and resourcing.
- Many sources exist for digital data stores and not all need to be in-house owned and operated. Some can be procured while others can be leased.
- Multiple tools and techniques are on the market; matching to general needs will be a challenge.
- Securing staff with specific skills in a timely manner and retaining top talent during an implementation may require consideration of alternatives including professional services, cloud sourcing or collaborating.
- The time to build in-house talent may well exceed the delivery window.
