In “Driving ROI Through AI,” a recent benchmarking study with input from over 1,200 executives, 38% of respondents cited making sure their IT architecture and data management system can support AI as the most important lesson learned in implementing advanced AI within their organization. Further, over a quarter (28%) said the biggest technological challenge associated with adopting AI in the company is a lack of IT infrastructure to facilitate AI implementation.
The role of the IT architect has never been more important when scaling AI initiatives. IT architects, who manage data integration globally and interact with teams across the organization, are integral to making sure these processes run smoothly (namely, that the systems the data teams are using work and knowing the business implications of these resources). Taking this one step further, they are responsible for making 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.
While it seems the role of the IT architect is a protective one, making sure that backend data plumbing is well-designed (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 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, IT architects need to keep pace, ensuring systems are functioning as they should. They 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: IT architects are heavily involved with compliance and cybersecurity initiatives across the organization. With more data accessible, they need to ensure policies are enforced and audits can be effectively carried out.
3. Support: They support and provide transparency around both self-service analytics (SSA) and operationalization initiatives. With SSA, for example, they make data prep and wrangling accessible to non-IT users through ETL. For operationalization, they make sure production systems are robust and reliable. During times of crisis recovery, this support is particularly critical, in order to avoid data teams having to start from scratch with models that have been deployed.
4. Evolution: IT architects (and their greater IT teams) need to have their fingers on the pulse of the entire technology stack and be ready to make adjustments when necessary. Further, with the rise in data comes higher costs for organizations to use that data. The onus falls on IT teams to regularly determine how to split costs, identify areas where costs can be reallocated, and how many data resources teams are consuming.
Ensuring Agility
Data orchestration, a key function of IT architects, involves automating the process of taking siloed data from multiple storage systems and locations, combining it, and making it available for analysis and insight extraction. To help them avoid falling behind or become overwhelmed with data processing and integration jobs, IT architects can leverage a data science tool to:
- Enable quick understanding of who is using data (as demand for and usage of data is constantly on the rise)
- Handle the proliferation of data and analytics
- Offload data prep and ETL to the teams regularly using data, such as data scientists
- Support elasticity and resource optimization, allowing organizations to process significant volumes of data, large numbers of concurrent usage, and services deployed
- Enable hyper-adaptability for processes ranging from data ingestion and computation to compliance evolution and crisis recovery
While data democratization and times of economic disruption certainly require the entire business to infuse added levels of agility, this is immensely relevant for IT architects. The role is crucial in today’s enterprise, combining diverse datasets from various systems and locations together to equip other teams to extract impact-generating insights. To check out the full illustration on how a data science platform can help IT architects streamline data orchestration, click here.