In this episode of the Data Show, I spoke with Carme Artigas, co-founder and CEO of Synergic Partners (a Telefonica company). As more companies adopt big data technologies and techniques, it’s useful to remember that the end goal is to extract information and insight. In fact, as with any collection of tools and technologies, the main challenge is identifying and prioritizing use cases.

As Artigas describes, one can categorize use cases for big data into the following types:

Improve decision-making or operational efficiency

Generate new or additional revenue

Predict or prevent fraud (forecasting or minimizing risks)

Artigas has spent many years helping large organizations develop best practices for how to use data and analytics. We discussed some of the key challenges faced by organizations that wish to adopt big data technologies, centers of excellence for analytics, and AI in the enterprise.

Here are some highlights from our conversation:

Adopting big data analytics: Remaining key challenges

For me, the first challenge is that there’s a lack of skills across organizations. We know there’s a global shortage of analytic talent, so it’s not only challenging for a company to acquire the right talent, but also to make sure that this talent is accessible across the organization. It’s usually concentrated in some departments, and it’s very difficult to leverage those skills for the good of the entire organization.

The second challenge I see is lack of standards and lack of governance. You might find that every single data science team uses their own libraries or their own version of code or their own software tools. They are thinking about the benefit for a particular use case, and the best tools and the best models for that particular use case. But this compartmentalized approach cannot scale up; having a variety of versions of libraries and tools make it very, very difficult to industrialize and implement global solutions.

Finally, new skills you need to develop are not only on the technical side—they are mostly on the business side. Decision-makers need to make decisions in different ways. They need to make decisions based on data, based on facts.

Center of excellence for analytics

The analytics center of excellence is a team of business and technical people that can be internal, external, and even crowd sourced. They have some centralized capabilities and also some distributed capabilities and resources, creating a common (online) workspace where they share methodologies, tools, models, and techniques. The objective is to gain efficiency and be able to implement initiatives across to the different business units. We have two main components of these centers of excellence: the business transformation unit and the deployment units.

The business transformation unit (BTU) is the primary link of the center of excellence with the underlying business. We create ambassadors, and these ambassadors, who are part of the BTU, are responsible for identifying and prioritizing all business use cases. Then they connect with the deployment units—which we call cells—and these cells can grow organically during a project. So, first of all, the center of excellence must be connected with business. … We also create a centralized function called the ‘core team’ and an expansion unit called the ‘extended team.’ We have a few types of cells: the analytical cells, the operational cells, and the data utilization cells. So, it’s a way of concentrating the resources, gaining operational efficiency, having a center of know-how transferred to the rest of the organization, and ensuring best practices and methodologies.

… A center of excellence is not a physical place. The center of excellence is a network of people who can be distributed in different geographies.

Related resources:

“The stages of enterprise IoT adoption“: Teresa Tung on building a business case for the Internet of Things

“Data go