Distill both business and IT down to their cores and you end up with a common foundation: data. Arguably, the entire digital transformation trend is fundamentally involved with capturing, securing, analyzing, reporting, visualizing, and distributing both structured and unstructured data to the people and applications needing it.
Organizations are exploiting this valuable digital resource to drive strategic insights, operational efficiencies, and business success. In short, there’s good reason why data is often characterized as the lifeblood for all business and IT operations.
Predictably, there are no shortages of challenges associated with tapping data’s inherent value. Given the demands of rapid business responsiveness as well as elevated customer expectations, IT departments must perform much of the necessary data collection, processing, and analysis in near real-time. And, unlike many valuable resources, data isn’t diminishing or in short supply. To the contrary, data generators and sources, as well as data volumes, are all increasing at a mind-boggling pace.
The combination of the volume and criticality of data is why CIOs and their IT teams already devote much of their expertise and time to managing the complex data landscape. Collectively, there are hundreds, probably thousands, of individual processes associated with different stages and functions of the data management lifecycle, and many of those processes remain partially or fully manual.
Without something changing, IT departments may find they have little capacity for doing much other than managing, securing, analyzing, and distributing data.
Fortunately, robotic process automation (RPA) offers a ready and powerful solution to this data management conundrum. One major way in which RPA helps is by enabling business users to automate periodic reporting, conduct ad-hoc analyses, and perform other data-dependent tasks using a self-service model that frees up IT and data engineering resources. Developers can also use RPA to rapidly and accurately deliver the right data to the right automation processes.
Indeed, it is only by automating many manual data management processes that organizations can hope to meet the challenges, and extract the full value, of the digital data now engulfing them.
Artificial intelligence (AI) and machine learning (ML), discussed in an earlier post, have become important enhancements that can make data process automations “smarter.” Ironically, AI and ML are themselves fully dependent on access to the large and accurate data sets needed both to train AI models and to refine the models’ accuracy and power during their operational lives.
Of course, it’s not just AI and ML that are data dependent. Virtually all of the functions a modern RPA platform delivers also rely on data collection and analysis. Those functions include analyzing existing processes to identify automation candidates, extracting information from forms and other documents, tracking automation usage and the resulting process improvements, and a host of other data-based activities.
All data-dependent processes can benefit greatly by ensuring that they have ready access to data that is accurate, consistent, timely, and secure. To this end, UiPath Data Service gives organizations a drag-and-drop storage interface with which they can pull data from multiple sources and aggregate it in a single, scalable, and secure repository.
With just a single click, developers can integrate data from the repository into UiPath Studio, App Studio, and AI Center. In this way, developers can easily ensure that each automation has the optimal data needed to execute its intended function. These developer-focused capabilities, combined with the self-service functions RPA can deliver to business users, can drive significant business and IT benefits enterprise wide.
For more information about the complete, data-driven UiPath Platform, and how UiPath automation solutions can help your organization tackle its own data challenges, check out our website.