OBSTACLES
What obstacles continue to inhibit the movement to data-driven enterprises?
Technologies such as cloud, data mesh, data fabric, and AI also come with potential issues, starting with regulatory compliance, data privacy concerns, security flaws, sovereignty, and disruption to business continuity, industry leaders cautioned.
A high-functioning and secure architecture requires “robust data governance, risk reduction, and a data quality assurance testing strategy that continues throughout the data lifecycle,” O’Dell advised. This is built on “end-to-end coverage of the data processes, automation in the testing for 90% or higher data coverage, and that the process be continuous—event- and schedule-driven.”
Data governance and data security “are the biggest challenges data-driven organizations are facing today,” said Regensburger. “Increasing the flow of data as well as the number of data users means that the probability for unauthorized access, usage, or data leaks significantly increases. Manually managing data access is not scalable and leads to change management issues.” Enterprises need to automate as many of these functions as possible, he added.
Data governance tends to be overlooked too frequently, Varshney agreed.
“Governing data correctly, ensuring that it is of high quality, compliant, and access to it is securely managed. has always been critical to streamlining data-driven innovations. This is critical in the era of AI, where an unprecedented level of data assets will be required to inform AI models and applications.”
The adage “garbage in, garbage out” becomes more relevant than ever as “the reliability of AI depends on the reliability and accuracy of the data fed to it,” Varshney added. “Get this wrong, and the consequences are becoming increasingly dire.”
Data quality, in particular, is a vexing challenge, as it has been for years, through countless technological waves.
“Bad data or, worse, untrusted data made up of data or inconsistencies, ultimately erodes the entire decision-making process, hinders business outcomes, and hurts customers,” said Bruce.
Ensuring data quality “requires a deep understanding of the meaning and collection processes associated with the data,” Bruce continued. “Surfacing actionable lineage, both in the business and technical context, establishes a chain of provenance demanded across enterprises.”
Another obstacle is building a culture that is more data-centric. “This involves ensuring [the company provides] the right tools, proper training, and a strong organizational structure—such as having the right data leaders, like a chief data officer,” said Ghai.
“But not everyone is ready to dance to this tune, and some still cling to old hunches and gut feelings,” said Flynn. “Where tradition reigns supreme, the only way out is through the top. Organizations need leaders and executives at the helm steering the ship toward data-driven horizons. That’s the ticket to the dance and the open invite to stay afloat in a hypercompetitive environment.”
CLOUD-FIRST
“Cloud-first” is now the rule for many data-driven enterprises, and industry leaders agree it is a prudent policy.
“On-premises systems often require substantial upfront investments and demand more eort for maintenance,” said Bruce. “In contrast, cloud services provide access to cutting-edge technologies without the operational burden of managing internal infrastructure. While on-premises systems offer control, the complexity involved in managing them at scale can be resource-intensive.”
Advantages of such a strategy include “scalability, cost-effectiveness, and flexibility, enabling them to harness the full potential of cloud computing for their data-driven initiatives.”
Cloud-first also leads to greater innovation, Bruce added. “Cloud providers consistently roll out new features and services, granting access to cutting-edge technologies without the burden of overseeing and upgrading internal infrastructure.”
There are cases in which cloud-first architectural strategies are evolving to cloud-only platforms—and with good reason, its proponents state. “The only way for enterprises to properly manage their data is to be cloud-first,” said Ghai.
“Going ‘cloud-first’ helps businesses connect, manage, and unify data across virtually any multi-cloud or hybrid system, democratizing data and enabling enterprises to modernize and advance their strategies,” Ghai continued.
“Cloud technology is able to leverage cutting-edge technologies, like AI and machine learning, to ensure expandability, adaptability, and precision in data management.”