Data Leaders Who's Who: Felipe Flores

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Felipe Flores

Founder, Data Futurology

Felipe shares on why prioritising change management is key to getting AI products into production, and the importance of stakeholder engagement in lifting data capability across the organisation. Here are his broad insights to help in uplifting the strategy, execution, and future of data science.

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During this interview, Felipe shares a wealth of information in answering the following questions:

How do you devise a data strategy? What sets apart the good from the bad?

One of the simple definitions I like of a data strategy is that firstly, it gets people aligned on where the organisation is today, in terms of both data and analytics maturity and secondly where they want to be in the future. Finally, the data strategy creates an execution path to get from point A to point B. Several use cases will emerge from that execution path, and these will need to be prioritised.

In terms of what sets apart the good from the bad, a lot of it is the internal alignment, and whether the strategy has been able to create the desire for the organisation to change. That desire starts with awareness and an understanding of where the organisation is now. Why it needs to change and where we need to get to. Then the goal is to adopt an aspirational outlook. “Aspirational” here doesn't necessarily mean it has to be very exciting, futuristic, or difficult to achieve. That's not necessarily aspirational from a data strategy perspective.

Rather, aspirational could still be realistic, it’s just important that there’s the goal to step up from where the organisation is today. In essence, a data strategy is broader than analytics and tech strategies and must encompass everything including culture, technology and aspirational goals. The shared understanding of the organisation in terms of where the organisation is today and the aspirational goals is a key component that sets apart a good data strategy. Also, where another way to lift your data strategy to is to ensure that data leaders have a shared consensus on the project prioritisation for future use cases and a preliminary road map with logical groupings of use cases according to business impact or customer desire.

Felipe, project prioritisation is a really important point. How do you determine which projects to use AI for? How do you go about prioritising your projects?

AI can be used for operational decisions and for strategic decisions. They both need to be handled differently.

Operational decisions happen thousands, if not hundreds of thousands of times per day. Hence there is a lot of work that can be done at the ML ops and ML engineering area that can help us improve the scale at which these services are delivered.

On the other side, there are relatively few strategic decisions. If you think about an acquisition, for example, it is an important strategic decision for an organisation, but it would happen a couple of times a year at most. But there's a lot of analysis and insights that needs to go into large strategic decisions and AI can be used for insights to assist there.

In terms of which projects to use AI for – I’m usually thinking about operational. We need well prepared, high quality data presented to the model creation part which will create better models over time. With strategic AI it is much slower, in small groups, using AI/ ML to create insights and consult or advise on decisions.

There are four components that help with project prioritisation: 1. Feasibility – can it be done with the data, technology, people and resources we have now? 2. Desirability – does the customer want this? Can we quantify that market desire and how important that feature is for the market? 3. Viability – is there a financial return on this? Is there a path to profitability? 4. Organisational readiness – is the organisation ready to bring the product to life for the customer or ready to consume that product? This readiness encompasses two sides – technical readiness and also the people side – are people aware that this is a problem or that customers want this and do we have the momentum to bring this product to life? Timing here is critical.

Felipe also shared his thoughts on:

• How have you found success in raising data literacy in your organisation? How do you get involved in educating peers and execs?

• What do you wish senior leadership knew or understood?

• Operationalising AI. How do you deliver AI at scale and get more models into production?

• What are some of the lessons learned you’ve encountered when getting AI products into production?

• What is the best way to structure your data and analytics teams? What processes and methodologies are key to underpinning analytics project success?

• We are seeing a real demand across the industry for ML engineers, do you see that changing in the long term? Will ML just become a fundamental component to the data scientist's role?

• How do you ensure you are leveraging new tech for innovation, rather than tech for tech’s sake?

• What new technology and innovations do you see as being the most critical to the industry over the next 18 months?

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