What is your data strategy?

In February 2019, only half of the attendees at a McKinsey summit had a digital strategy aimed at business results. In addition, cross-industry research reviewed by Harvard Business Review indicates that less than half of an organization’s structured data is regularly used in decision-making — and less than 1 percent of the unstructured data is analyzed at all. Organizations with a well-organized data strategy could achieve massive leads over their competitors. This article elaborates on three major benefits of having a solid data strategy to enable innovation and increase your business value.

Data-driven organizations are about the democratization of data, where everybody has easy access to the same data. This means everyone - who is allowed to - can quickly see what's going on. This shortens the feedback loop, enabling organizations to learn faster than their competitors - based on facts.

For instance, the COVID-19 pandemic caused a unique situation that required the agility of businesses’ supply chains. Organizations with a data-driven strategy could quickly spot changes in customer behavior and, as a consequence, were able to survive. Organizations with a slow feedback loop of, for example, three months, had a harder time keeping their heads above water.

So now you know what a data strategy is. But why should you implement it? We have collected three major benefits of having a data strategy. First of all, let’s state the obvious: data-driven organizations can thoughtfully assess and improve the effectiveness of their internal divisions and marketing channels. Underperforming business areas can be optimized, modified, or eliminated, with cost optimization as a result. By reducing redundant tasks from their most valuable employees, they now can spend that time on real challenges, allowing them to innovate. 

Second, organizations with a data-driven approach are more flexible in adapting to customer and business trends, enabling them to remain the brand of choice for purchasing trending items. By evaluating customer purchasing habits and exploring new approaches, organizations may find new markets or ways to develop customer loyalty. 

Third, most fields are constantly changing, with new products and strategies emerging almost daily. Using a data-driven approach, organizations can spot the most promising opportunities at the earliest stage before others have adapted them. Using state-of-the-art analysis tools, they can also keep informed of any change or whether they have missed out on profitable opportunities.

One of our customers with a great data strategy is ASML, a Dutch firm grown from modest beginnings into a global leader in the semiconductor industry. The company's machines allow giants such as Intel, Samsung, and TSMC to mass-produce silicon patterns, enabling large-scale innovation in the tech sector.

With the launch of ASML’s new breed of machine learning product, the company was able to predict process performance per device layer. However, because the device layer and manufacturing processes change frequently, the product needs to train itself. And to add more complexity, there’s no connection between the product and ASML, so the product needs to monitor its own accuracy and retrain accordingly.

ASML's initial on-premises solution worked but couldn't adapt fast enough to data growth and model- and software build complexity. They partnered with Google Cloud Partner Rackspace to implement a cloud architecture and extend its secure environment into the cloud, a process completed within weeks. ASML also partnered with machine learning specialist ML6, which sent Google Cloud experts to train staff on products such as BigQuery, Google Kubernetes Engine, and Cloud Datalab. It’s an experience that has turned out to be more collaboration than coaching due to the ease of adopting Google Cloud.

The gains from moving to Google Cloud have been transformative, says Arnaud Hubaux, Technical Program Manager AI/ML. ASML engineers were spending hours every day parsing and preprocessing data. Now the time spent on such tasks is down to zero, says Hubaux. Overall engineering efficiency, including the time to market, improved by up to 40% by moving to Google Cloud. By dedicating a BigQuery and Kubernetes cluster to data ingestion with auto-scaling, the AI team is able to obtain data faster, saving each engineer about four hours per day.

Crucially, this has translated into shorter product release cycles, enhancing ASML’s competitive edge. “We went from months between releases to biweekly release cycles now,” says Arnaud. “The fact that we’re now able to reach this pace is really amazing.”

ASML is writing the next chapter in its partnership with Google Cloud faster than imaginable. The company has completely moved to AI Platform Notebooks from Google Cloud Datalab. ASML explores ways to be a beta tester for the Google Brain Team deep learning initiative. It's a partnership that goes beyond technical support: "I'm more keen on building a healthy relationship," Arnaud says.

A good data strategy is essential for every organization. Are you your industry’s next disruptor?