Data automation: Creating impact and long-term value

In today’s ever-evolving business landscape, companies are increasingly acknowledging the growing need for high-quality data. Clean, integrated and trusted data is crucial for driving more accurate insights from data and effectively leveraging artificial intelligence solutions to enable more effective decision making. Therefore, organizations need to evaluate their data strategies and how they leverage emerging technologies to gain a competitive edge and create long-term business value.

Organizations today usually fall under two categories: those with a more traditional approach towards business intelligence, driven by people, operations and sales insights, versus those that are keeping up with AI and leveraging predictive analytics, automation, AI agents and statistical tools. However, regardless of the approach, both are directly dependent on the data quality.

Jason Proto, RSM US principal,  and senior associate, Elisabeth Henhapl along with Lucas Karcher, Boomi data management partner in sales, and technical product marketing manager in data integration, Jeffrey Li, recently shared actionable insights and best practices to enhance efficiency and transform business operations during RSM and Boomi’s webinar “From ad-hoc to automation: Data pipeline automation for quicker insights.”

Driving value through data

In today’s fast-paced business environment, marked by supply chain disruptions, trade concerns, as well as other global uncertainties, quickly transforming raw data into timely, actionable insights is essential for enhancing efficiency, reducing errors and enabling faster, data-driven decisions. High-quality data is a currency that adds value across multiple functions, including:

• Operational value: Effective data can enhance operations monitoring, process automation, static dashboards, agentic AI solutions, and productivity and key performance indicator (KPI) management. 
• Healthy organizational culture: With trusted data, companies can strengthen risk and compliance monitoring, data governance oversight, internal and external audit capabilities, AI risk assessments and controls monitoring.
• Strategy and growth: An actionable data strategy elevates financial, quantitative and qualitative reporting and enhances AI capabilities for financial planning and analysis.
• Innovation: With growing volumes of trusted data, companies can confidently take advantage of customer service automation, predictive analytics, machine learning and advanced AI tools. 

“Many organizations operate in an ad hoc manner without any cohesive platform that leads to repetition and multiple back-and-forths between business teams and IT,” says Proto. “Automation helps reduce time, offers better data analytics buckets and is more self-service, ultimately driving long-term value.”

Hidden cost of ad hoc data work

Undoubtedly, the power of automating data pipelines can be truly transformative for businesses across all industries. To stay competitive, companies must transform from manual, ad hoc data processes to fully automated systems, as unstructured data work comes with multiple challenges and becomes problematic on several levels, including:

• Data silos: Information is scattered across tools, teams and formats with no unified view, making it difficult to align on a strategy.
• Custom-built insights: Insights derived from the data silos are highly customized, with logic reinvented for every new query.
• Inconsistent KPIs: Each team creates its own logic for key metrics, making it difficult to agree on a single version of the truth.
• Conflicting results: Due to the fundamental differences within each team, outcomes and reports vary depending on the source system or the analyst.
• Loss of trust: Confusion and inefficiencies cause business users to hesitate to act on insights they may not fully believe, resulting in trust issues.

“The true cost of ad hoc data isn't just time to get to a certain result,” says Henhapl. “It’s lost trust, unreliable insights and analytics that don’t scale.”

Furthermore, the operational complexity within ad hoc data stems from often relying on Excel as the primary reporting layer and the lack of automated data feeds. This approach leads to excess time spent and duplicated efforts chasing data issues, resulting in slower responses and a growing reporting backlog.

“The lack of a unified data model or semantic layer results in conflicting results, duplicated work and reduced trust,” says Henhapl.

How application integration solutions support data automation

Application integration solutions help businesses thrive in the era of AI-driven automation. They are low-code, highly scalable and can be deployed in the cloud, on premises or in hybrid environments with advanced security.

Through application integration, organizations can begin their digital transformation journey at any point, with core capabilities in integration and automation, data management, API management and AI enablement. It allows for seamless integration of business applications, supports trusted data for AI training and includes AI agents to streamline workflows and boost productivity.

Application integration tools help firms embrace innovation and data automation through key steps and milestones. They enable AI-readiness plans, system cleaning and synchronization, and AI deployment across people and platforms. Ultimately, these capabilities optimize integration and automation at scale, leading to market leadership, stronger customer experiences and new business opportunities, further leading into the age of agentic AI transformation.

“Agentic transformation happens when intelligent AI agents, which are fueled by trusted automated data, are able to leverage cloud and no-code tools and autonomously perform tasks, make decisions and drive new business models,” says Karcher. “The goal is to achieve hyperproductivity, which will significantly enhance a business’s ability to manage, analyze and act on data from all the systems and data sources.”