IT Architects Are Critical to Bringing Hyper-Agility to Organizations’ AI Strategies

Scaling AI Catie Grasso

While concepts like collaboration and iteration are (and always have been) cornerstone to any successful data and analytics practice, one that has come to light in more recent years is agility.

A required skill in data and analytics, agility is pivotal both when it comes to optimizing existing systems and processes as well as finding completely new ways to accomplish business goals and meet objectives. If you are an IT architect, you are a key stakeholder in this technical, agile adaptability — it is your responsibility to make sure that the systems are agile and can adapt to the needs of either the users or the organization at large, which, ultimately will allow the business to work and grow more seamlessly.

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Let’s Step Back 

Through data orchestration, IT architects can effectively support both self-service analytics and operationalization in the following ways:


Data projects can be quite challenging when it comes to pushing to a production environment, especially since their requirements in terms of data access and computing power are specific. On top of this constraint, data scientists may not be familiar with production-grade configurations. Both reasons can lead to projects becoming unstable and hard to manage. It is the role of the IT architects to ensure that:  

  • Production systems are robust and reliable.
  • There are enough resources for the projects to run properly.
  • These resources are properly monitored and in line with what has been budgeted for a given project.
  • The processes and tools used to deploy a production system are not too heavy and encourage collaboration between the IT and data teams.

Self-Service Analytics 

By making data prep and data wrangling accessible to non-IT users through ETL, IT architects will be able to foster self-service analytics and reduce the burden on their own team. Further, IT architects define in advance everything that line-of-business professionals and analysts need to know and rules to follow. 

Diving Deeper

While it may sometimes seem your role is a protective one, enabling data teams to effectively leverage data and nimbly monitoring systems architecture to ensure each part of the IT ecosystem is functioning properly), it is much more than that. The role is critical to the organization’s AI strategy and scalability overall (with data democratization being an important piece of that) in a multitude of ways:

1. Data demand: As more and more people begin to work with data inside an organization (and even non-data teams begin to work with data more regularly), you need to keep pace, ensuring systems are functioning as they should. You need to be able to quickly respond to backend demands, including adding and removing users, orchestrating data ingestion and connecting data sources together, and data computation.


2. Security: The security aspect of your role means you are deeply involved with compliance and cybersecurity initiatives across the organization. With more data accessible to more people, they need to ensure policies are enforced and audits can be effectively carried out.


3. Support: You support and provide transparency around both self-service analytics and operationalization initiatives. Not only do you help build an environment where self-service analytics and operationalization are possible, but also hassle-free, as they are both required to generate tangible business value.


4. Evolution: With the support of your teams, you are ready to make adjustments to their organization’s technology stack when necessary. Further, with the rise in data comes higher costs for organizations to use that data, and the onus falls on IT teams to monitor resource consumption and cost management.

IT Teams Have Arguably Never Been More Important

Making sure IT architecture and data management systems can support AI is often a big takeaway we hear from executives implementing advanced AI within their organization. Often, one of the biggest technological challenges associated with adopting AI in the company is a lack of IT infrastructure to facilitate AI implementation.

All of this supporting evidence leads to one core takeaway: Your role has never been more important, particularly when it comes to data integration. While data democratization (and macro-economic flux) certainly require the entire business to infuse added levels of agility into both their day-to-day and long-term initiatives, this is particularly relevant for you, IT architects — you regularly help mitigate the organizational burdens outlined above through hyper-adaptability.

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