All I want for Xmas is a data science team... or is it?

21 December 2023

Are you a transformative team leader that wants to get ahead with AI? You know using data and AI will make things much more effective, but you are not really sure how to get started? Hiring a data science team is generally not the best way to start. Sounds odd? Let us explain why and suggest how you should get started.

A lot has changed in AI. Recent advances and approaches mean that what was leading edge approaches 1 or 2 years ago have now been superseded. This also means that data science skills and requirements have also shifted dramatically. For example, no-one builds a chat bot from scratch or builds object detectors from scratch nor checks text compliance using LSTM deep learning networks. In summary, AI is eating AI. Recent developments mean that applied AI and how to get the most from it have evolved. The balance of skills has shifted to design, implementation and iteration rather than model build and hyper-parameter tuning. This means you should not hire a “data science team” and expect them to prioritise and solve your business problems. What you need is applied AI specialists. This will get you fast tracked in the right direction and give you a roadmap for how to build and evolve “smart applications” driven by AI. It will also help you determine what should be done in house and how you should use external specialists.

There are typically three ways of accelerating time to value from applied AI. The right approach depends on where you are in your data journey and experience with AI.

If you are just starting out, for example, evolving from silo-ed Excel based reporting, then you should use a fast track transformation approach:

  1. Determine your key drivers and key performance indicators
  2. Aggregate and link your data (drill-through reporting)
  3. Forecast what is going to happen - predictive analytics
  4. Map what to do – prescriptive analytics
  5. Determine a prioritised road map for AI development

If you know where you should focus first, then the second approach is to add intelligence to your existing processes and tools. For example, a data driven ordering tool that links evolving sales predictions with order constraints to optimise what and when to order from who.

The third approach is more leading edge and exploratory. This approach defines new automated ways of working and pilots new tools to get the most from leading edge AI. Examples could include automated document summarisation, image compliance checking or service personalisation.

Algospark are applied AI specialists. Our team have experience spanning data science, analytics, data design, data engineering, software development, service design, business analysis, finance and technology. We can help and advise the best way to get started and build solutions that get you and your team fast-tracked in the implementation of applied AI. Get in touch and let us help you deliver for Xmas and well beyond!

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