[Advertorial] Most organizations have now had their first experience with the cloud. The next phase is to cultivate maturity. In this three-article series, we’ll share three best practices to get more out of the cloud and analytics. In this second part, we’ll emphasize the importance of having the right people and skills to shape your strategy.

Companies usually have high expectations when exploring cloud technology. Cloud is also a game changer in the analytics space. However, some effort is required before you can realize the true value of the cloud. In a previous article, we looked at the costs and found out how to use FinOps to optimize cloud usage. In addition to technology, FinOps is also a philosophy that people in your organization need to foster. That’s why this time we’re sharing three best practices for building the right skills around cloud and analytics.

1. Identify the skills you need

What skills do your company already have and what do you need to deploy cloud analytics? To figure this out, we first need to break both components apart. There has long been a perception in the analytics space that you need to be good at coding. Fortunately, thanks to recent advances in GenAI, this no longer has to be a barrier. In this way, technology can perfectly help you write code. One solution that supports this is SAS Viya Copilot. This is a productivity tool that gives users tools to understand code and even proactively asks questions about the purpose of the code they want to create. For example, the AI ​​assistant makes suggestions to further optimize the code based on your needs.

For a data scientist, this means that programming is no longer a barrier. This allows you to emphasize the really important hard skills: mathematics and statistics. But did you know that there are also soft skills that should be part of a data scientist’s baggage? Creativity and social knowledge are important factors. To bring AI models into the world in a responsible way, you need to understand the ethical framework and be able to explain what a model does. Being communicative is also a useful skill. For example, you need to translate the results of a model for the public and, conversely, understand what users are looking for.

When we look at the cloud, we have good news and bad news. The good news is that we are seeing a strong cloud literacy among generations coming out of school. The less good news is that many organizations still have a long way to go before moving from a traditional data center to a cloud-based infrastructure. One possible explanation for this could be that organizations are trying to do too much of a one-to-one translation of their traditional environment when making the switch. They need skills and people to keep track of the big picture when crafting a cloud strategy and carefully determine how the analytics story fits into it.

2. Focus on your core business and outsource the rest

To acquire the necessary skills, people can use online learning platforms such as LinkedIn Learning, but of course this is not enough. Some companies have a culture where they outsource part of their activities. This is certainly also an option for cloud analytics, as most of the talent is available on the market. It all depends on how you look at the cloud: would you rather do all the work in-house or leave it to consultants?

There are several ways to integrate external knowledge into your organization. At SAS Innovate, we recently launched SAS Models-as-a-Product, a solution that collects all the knowledge of our experts in specific sectors and integrates it into a product. The focus of the tool is not so much on the processes required to build a model, but on the analytical question that plays a role in the company. Users thus gain access to industry-specific models that significantly accelerate the rollout of analytics projects.

Outsource or keep in-house? There is no wrong approach. But whatever you decide, work iteratively and consider whether you want to continue to follow the same strategy in the future.

3. Work centrally in a Cloud Center of Excellence

Some organizations already have a Cloud Center of Excellence. The approach to this is often more technical in nature, but it can also be the ideal starting point to build a better foundation for cloud and analytics across the organization. Hiring a dedicated data scientist for each department is usually not an option, so it makes sense if you can centralize the knowledge and experience in a Center of Excellence – a body that works across teams and helps people with best practices to make cloud analytics projects a success in the shortest possible time.

A Center of Excellence offers an important advantage: it ensures consistency and creates a community around cloud, data and analytics. If you leave everything to the individual business units, it is impossible to achieve a common goal. And the latter is really important to integrate data and analytics into your company culture. If you want to go fast, do it alone. But if you want to go far, choose to do it together as a group. A Center of Excellence works best when it is promoted from the top of the organization. We find that analytics projects are often more successful in companies that do this.

By the way, you don’t have to work for a large organization to build a Center of Excellence. How do you get started? As mentioned above, it all starts with mapping the people and skills in the company. Only then can you start thinking strategically. In the final article of this three-part series, we will therefore explain in more detail what it takes to gradually build out such a Center of Excellence.

This article is the first in a series of three articles on best practices for getting more out of the cloud and analytics. You can find the first article here: 3 key steps to get started with FinOps. You can find the entire series here.

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