Over the past decade, data and analytics have completely transformed the business world. While many institutions such as banks and pharmaceutical companies have always relied on historical data to guide their decision making, the advent of real-time, predictive analytics has enabled all types of organizations to optimize decision making from the top down to front line employees.

However, building an analytics practice can be incredibly difficult, especially in industries that are highly complex, unpredictable or regulated. Furthermore, even once the data science team is up and running, ensuring data projects align with business needs and that insights are properly leveraged by the entire enterprise can easily become a never-ending struggle.

With this in mind we invited Albert Suryadi, GM Data Science, Mirvac to share his “5 Keys To Blue Chip Data Science” at the upcoming Data ROI APAC virtual event taking place March 01 - 02, 2022. With well over a decade of leading data science teams at various blue chip companies, he’ll be sharing how you too can ensure your data analytics projects are completely aligned with business strategy.   

Here’s a look at what we have in store for you.

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Seth Adler, Editor-in- Chief, ADA: Connecting the dots between data science and business results can be surprisingly challenging. It requires a deeper understanding of how business processes, human talent and data science all interconnect. What are some of the ways you’ve approached working with the business to help them understand the value of data?

Albert: Everybody talks a lot about the algorithms. But putting a machine learning model into production is only half of the battle. It needs to be put in the hands of humans who will action it to make business decisions.

That’s why AI explainability is becoming a bigger priority and we’re creating new interfaces to help users more effectively interpret data.

I think it's very important for both data science teams and the enterprise to think about how to ensure AI adds value to the business. Think about how people that are working in the business, in a call center, for example, can really use it and can truly interact with it.

Seth: However, to do this you must build a strong foundation. Can you take us through, number one, building that strong foundation, and then number two, making sure that yes, as we go, we certainly evolve, but also remain focused on that foundation so that it evolves along with us.

Albert: I think in a lot of my experience, AI and ML has a lot of potential. We’ve seen what it can do when it's applied correctly. However, I think a lot of blue chip companies often when they start these data science initiatives, they underestimate the level of investment needed to succeed. For example, cloud data engineering.

Data comes before the science. So a lot of them hire smart mathematics practitioners who may not have that much experience with software engineering. As a result, it's very hard for the model to get productionized and even to get data inputs to some extent

And that's why one of the trends that we're seeing in the AI world these days is the rise of ML engineers as well. But again, I think it's very important for everybody to ensure that they get the data right first, as well as the AI and ML initiatives.

Hear more from Albert on March 22, 2022 at the......

2nd Annual Data ROI APAC Virtual Event