‘Collecting the data is only half the battle’

Headlines have been dominated by previously unknown data points, such as the R rate of the virus. Our first line of defence has been the data scientists who predicted the future based on the information available.  

In the process, we have all become ‘citizen data scientists’, caring more about epidemiological trends and statistics than any of us could have possibly imagined.  

Leading the data product charge

This is just one example of how data products – defined as those fuelled by data and machine learning (ML) – are an increasingly central aspect of how we live and work. 

“What does it mean to lead in a world driven by data and data products?” 

All businesses, including of course retailers, have identified the need to adopt artificial intelligence (AI), ML and data-driven approaches to transform how they operate and the experiences that they can deliver to customers. 

As almost every business leader speaks about how their companies will become AI/ML-driven in the near future, what does it mean to lead in a world driven by data and data products?  

A team sport

Creating successful data products requires a new type of collaboration between the business users and the data science team.

The emphasis has to be on co-creation: bringing business know-how together with data know-how to jointly solve problems. That means no more siloed thinking and leaving the data whizzes to do their thing.

An article by economist Emily Glassberg Sands in Harvard Business Review emphasised just that point: “Business leaders traditionally have the strong intuition and domain expertise to identify key unsolved user and business needs.  

“Meanwhile, data scientists and engineers have a keen eye for identifying feasible data-powered solutions and strong intuition on what can be scaled and how.” 

The article urges businesses to “bring these two sides of the table together”.

Business-first

So where do you get started to make sure that your data products achieve their potential? With a clearly articulated business issue to solve and the outcomes that you want to achieve. 

It’s essential to understand the scale of the opportunity and the value that it could realise. That business challenge then has to be translated into a problem that data can solve.

Too often, though, businesses start by collecting as much data as they can, gathering it into a data lake and then seeing what they can do with it. 

But that is building a solution in search of a problem. And it’s not going to deliver.

Data products live

Building a brilliant data product is not a one-off exercise. The data product needs to be evaluated, managed and adapted to continuously iterate and improve. 

In that way, data products are no different from their physical counterparts. They are dynamic, not static. They have to be part of the living organisation, managed in much the same way as people in the business. When change is needed, they change with it.

We are all data-savvy now

The world of AI, ML and other advanced technologies can appear a daunting place. The urge to leave data to the data scientists is understandable. 

“The higher the data, AI and ML literacy of the functional business users the better they will be able to co-create, collaborate and continually evolve the data products with the data science teams”

But it’s much too important for that. The business has to be in the driving seat to ensure that data products achieve their potential and deliver real differentiation. 

It is imperative that leaders and their teams upskill in data. The higher the data, AI and ML literacy of the functional business users the better they will be able to co-create, collaborate and continually evolve the data products with the data science teams. Businesses need to develop the skills and capabilities of their workforce.

At Google Cloud, we are known for our AI and ML capabilities. That’s what our business is at its very core.

However, when we work with our retail clients, it has to be in true partnership – combining the retailer’s deep knowledge and understanding of their business and the challenges they face with our ability to build data products.   

In a world where harnessing the power of data assets is increasingly the difference between success and failure, that’s the only way.