Cost Optimization Vs. New Revenue Channels

Typically speaking, data science delivers business value in two different ways: reducing operational costs by increasing efficiency AND helping the business identify new revenue streams.

Though both are worthy pursuits, for those looking for a quick win, cost optimization is they way to go. Applied AI Speaker, Shiv Kumar - Head of Analytics, Schlumberger, explains, “If you compare both the opportunities for the organization, with operational optimization and cost reduction, you will have the highest or the maximum data which is available within your organization. Once the data is available to wrangle, you're just going to add the new set of data and analytics to find out those opportunities.”

In other words, cost optimization analytics organically lead to the more advanced analytics that shed light on new services or products. However, since that takes more time and is a more complex process, it’s best to start with data science projects that enhance efficiency.

External Data is the Key to Innovation

Though internal data is key to optimizing internal operations, when it comes to identifying new areas of business, you must look externally. 

As Shiv explains, “You need to collaborate outside your organization to discover new data streams. This will bring more insights to the table regarding what the next, new product or service should be.”

By adding in external data, one can increase the power of algorithms. “With internal data, I can predict outcomes with an 80% accuracy. But by adding external data, the algorithmic power is enhanced by 5 - 7% which can have a significant impact on accuracy,” Shiv tells us. 

External data also helps organizations make sense of their internal data, especially after the last year when customer behavior went off the rails. Though external data is not without its challenges, when combined with AI and internal data, it can unleash powerful predictive and prescriptive insights.

Establish a Data ROI Framework

Calculating the ROI of data science or AI projects can be incredibly challenging to hammer out. However doing so is essential to securing resources for continued innovation.

When it comes to data science, what does your organization value the most?  What types of insights and the changes that result from them is your leadership team embracing? What’s motivating the business to adopt data analytics tools?

These are all things you should be looking at as you develop your data ROI framework - or guidelines for aligning data science with business objectives. And remember, not all benefits will be tangible or immediate. According to Shiv, “You need to define the framework where you can factor some of the intangible KPIs and some of the tangible or direct KPIs out of any analytics initiatives. It’s essential for any investment that you can clearly identify what is the likely impact that I'm going to drive using data science and analytics. So, hence we say that there can be direct outcome, there can be a long-term strategic advantage generating the competitive edge for the organization, which may not be very evident then and there, but it can be a long haul process.”

Want to hear more from Shiv Kumar, the Head of Analytics at Schlumberger? Attend his session, “Data Science Driven Intervention For Innovation” at the Applied AI virtual event June 29-30, 2021.