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Reimagining business with digital transformation

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♦ Information governance: No panacea for data governance, cloud computing actually emphasizes the need for this data management mainstay. “Data governance was a critical issue when all of the data was on our premises, but now it’s between on-premises and a cloud environment,” Patel said. “Doing that metadata, that data lineage, applying appropriate data quality rules, you do it once and deploy it, whether it’s on-premise, hybrid, or cloud.”

Silo elimination

Most of all, the collaborative prowess of the cloud actualizes a reality in which silo culture is eliminated from the enterprise, allowing organizations to revamp their processes for things like mergers and acquisitions. Because digital information is either readily exchangeable via APIs or accessible through both distributed and centralized cloud tools (such as content platforms or cloud data warehouses, for example), the cloud supplies an innate data democratization and reuse of data throughout the organization.

“If you have a common integrated set of tools to monitor and manage everything from an application perfor- mance, infrastructure performance, or user experience, conversions, or revenue standpoint, it will help promote that single and consistent view of the truth,” Patel said. Additionally, there are a number of emergent architectural approaches that utilize cloud storage so it nullifies the effects of silos. Data lakehouses are one such example; certain data mesh implementations support this benefit, as well. “When you have a cloud-native infrastructure, everything is maintained in one pool, if you want it to be,” Toor said. “You can separate things off if you want to do that. But if you want to have universal searchability, you can put everything in one pool, up to hundreds of petabytes of information, and have it all be searchable in one query instead of going from place to place to place.”

Intelligent bots

Digital agents are another viable means of quickly revamping business processes with digital capabilities. Numerous cloud robotic process automation solutions couple these software agents with cognitive computing technologies, exemplify- ing the fact that “cloud computing makes it easier to build data and applications with the flexibility to scale and the agility needed to add capabilities like AI, ML, third-party marketplace tools, and more,” Nirenberg said. Internally, intelligent bots can transform unstructured content into structured content by extracting requisite information from a contract, for instance, and inputting it in the appropriate downstream system. The surfeit of unstructured data organizations are contending with makes this capacity invaluable for scaling processes, Nirenberg noted.

Moreover, the utility of bots for digital transformation suitably expands with the incorporation of AI. Although equally applicable to internal interfaces, the external interface capabilities of digital agents can improve customer interactions altogether. “What we have seen in the last two years when we talk about customer experience, more and more [is] the the adoption of natural language processing and chatbots,” Patel said. “Dramatic improvements in natural language processing (NLP) are making customer experiences richer and more dynamic.” Since NLP can involve either machine learning or machine reasoning, employing intelligent chatbots to interact with customers is a creditable example of how organizations can increase the scale of their operations while decreasing overhead, costs, and latency. “As part of that setup, your customer experience is improving,” Patel said. “We are able to remove friction out of an ordering process or out of a healthcare appointment process, or a customer service setup process, or if I have to setup a warranty call.”

Transformative automation

Regardless of the increase in scale, speed, and accuracy of digital transformation initiatives involving software agents, the cloud, search, and asset enrichment, the goal of these constructs is ultimately to improve the efficacy of human workers—and their business processes. This distinction may be a subtle one, but it’s integral to the overall point of digital transformation, especially when it’s applied to knowledge management. “Finalize the brief is not something a bot can do,” Carmel said. “Finalize the brief means someone’s got to dig in there, do more research, and get it done. But getting it done means I’ve got to get it off my desk and on the next person’s desk. That’s knowledge work.”

For knowledge management, then, what Carmel called “clerical automation”—which digital agents are equipped to do—is merely the launching point for other forms of automation that enable knowledge workers to excel in their jobs. These involve the inherent collaboration mechanisms of the cloud, swiftly cataloging and finding knowledge via search, doing so holistically without the impediments of data silos, and effectively putting tailored content in motion so mission-critical objectives are fulfilled. “That’s why you’re paid more, and that’s the way knowledge work is,” Carmel said. “It’s less purely machine-driven like clerical work, and it’s more humans interacting.”

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