How to Establish a Successful, Sustainable, and Scalable Data Science and AI Capability within an Organization
Editor’s note: Alex is a speaker for ODSC APAC 2021. Check out his talk, “How to Establish a Successful, Sustainable and Scalable Data Science and AI Capability within an Organisation,” there!
With so much interest in data science, with all the promises of AI, and with the rapidly growing number of trained data scientists, why do we often hear of failed data science projects or projects that remain incomplete with unfulfilled promises?
There are several reasons for this, the first of which is a lack of a clear understanding of what data science is: data science is effectively about change — transformational change in an organization to enable a data-driven evidence-based decision-making culture.
The second reason is based on a number of common myths, which result in unrealistic expectations, and a lack of understanding of how to embed data science as part of the strategic core of the organization. Let’s address some of these:
- Deep learning is not the panacea for solving all problems, as I’ve previously discussed. You don’t necessarily need the most cutting-edge AI algorithms, running on large, expensive, and powerful infrastructure, to meet your strategic goals. The aim is to first clearly articulate your problem, then determine how data may be used to solve it. You then start with the simplest approach, and only increase complexity when needed. The fundamental aim is to have clear alignment to the strategic goals of the organization, with measurable outcomes to measure success,
- It’s unfortunately not always easy to establish a data science capability with guaranteed and immediate results — there are too many variables at play ie people, data, tech, problem scope, and definition — all of which need due consideration, and
- Fear of the unknown, fear of change, and no appetite for risk, will hold your organization back. Educate yourself and your staff in data literacy, so you can comprehend and extract value from data and findings, and increase your analytics maturity. Remember, data science is about exploration, so learn to be comfortable with uncertainty.
Some of these issues stem from inexperienced leadership. This includes insufficient technical leadership that can identify the right problems to solve with the appropriate technical solution, and a lack of an empowered decision-maker who can then support and action the results.
Another common issue is a lack of a data culture, that makes it clear to everyone in the organization how data and analytics are used to empower decision-making. Without a culture of innovation, collaboration and trust, and appropriate governance and accountability, it can be challenging to establish and grow a data science capability.
The third common issue relates to technological and data constraints. This includes data scientists being unable to access the data they need (either due to internal silos or complicated disparate data holdings), poor data quality, and inappropriate and unsuitable tools and systems.
So, how do you create a successful, sustainable, and scalable data science capability in your organization? Come along to my upcoming talk at ODSC APAC, “How to Establish a Successful, Sustainable and Scalable Data Science and AI Capability within an Organisation,” where I’ll share my 3x T’s for data science success!
About the author/ODSC APAC 2021 speaker: Dr. Alex Antic
Alex is a trusted and experienced Data Science & AI Leader, Consultant, Advisor, and a highly sought-after speaker and trainer.
He has 18+ years post-PhD experience and expertise in areas that include Advanced Analytics, Machine Learning, Artificial Intelligence, Mathematics, Statistics, and Quantitative Analysis, developed across multiple domains: Federal & State Government, Asset Management, Insurance, Academia, Banking (Investment and Retail) & Consulting.
Alex was recognized in 2021 as one of the Top 5 Analytics Leaders in Australia by IAPA (Institute of Analytics Professionals of Australia). He also holds several advisory roles across industry, government, start-ups, and academia.
His qualifications include a PhD in Applied Mathematics, First Class Honours in Pure Mathematics, and a double degree in Mathematics & Computer Science.
