For True End-to-End Process Automation: Evolve Your Data Strategy and Architecture

Today, organizations have ever increasing options to automate processes and streamline workflows. But this growing array of automation tools presents tremendous challenges of integration. Let’s explore how enterprise teams can make the most of their new and existing automation investments – from human systems and Robotic Process Automation (RPA), to AI/ML and other modern technologies. We’ll see how doing so helps achieve true end-to-end process automation that delivers enhanced performance, security and cost benefits for the enterprise.

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The Evolution of Process Automation Creates Integration Challenges

Process automation is essential for bringing repeatability and scalability to workflows and operations so that organizations can reap benefits in productivity, quality, compliance and auditability. And the concept is not new; the origins of process automation go back decades to an era when relatively simple tools for structured workflows and business process management were introduced, typically in support of small teams performing back office functions.

Over time, automation became more sophisticated – including RPA to automate repetitive tasks; modern integrations that substitute human tasks with calls to an API; and eventually AI/ML tooling to automate complex workflows and even self-optimize through process mining in proactive continuous improvement loops. The irony is that this long and successful evolution in process automation has led to modern integration challenges that stand in the way of business value and efficiency from those investments.

Enterprises find themselves struggling to understand how newer, more complex automation tools can integrate with legacy or simpler solutions – the latter of which remain a fact of life in most organizations. Transformation teams rarely have the option of simply replacing existing investments; and failure to reconfigure legacy process automation tools to align with their more modern counterparts leads to technical debt and underutilization that negatively impact the balance sheet.

Evolving Data Management for Stronger Integration

Poor integration of process automation leads to security and visibility gaps; and the underlying stumbling block that perpetuates this is insufficient data management. Too many organizations struggle to align automation tools without the benefit of consistent data definitions, interoperability protocols and standardized access rules.  This has a cascade effect where governance and compliance are degraded along with security and performance because of incomplete, fragmented and duplicative data.

Amid these challenges, organizations need to adopt a more data-centric approach, where core workflows, core data and core customer use cases are all tied together within a coherent, granular and standardized data architecture. This typically involves rationalizing data with business context and applying common standards for metadata and interoperability. For example, a legacy RPA system, an API integration and a modern AI suite for automating CRM functions may all be working with different definitions and data attributes to characterize what constitutes a “customer.”

By standardizing such definitions, these various process automation systems can better work together.  This also facilitates unfettered data access by the more advanced AI/ML platform components of this ecosystem to do advanced analytics or perform proactive functions like alert management or real time authentication tasks. Transformation teams should take care to modernize access protocols across both human users and automated authentication systems, as this helps eliminate knowledge gaps that can degrade analytic power and complicate compliance and reporting.

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Achieving True End-to-End Process Automation

The data management capabilities described above serve as an essential foundation to integrate various tools for end-to-end process automation. But these advanced capabilities must, in turn, be founded on an equally advanced data infrastructure. The best approaches establish an underlying data fabric architecture that accesses data where it resides and virtualizes it through an abstraction layer on a unified, vendor-agnostic platform for an enterprise-wide view of data.

Process automation is highly collaborative across various business units and stakeholders in the enterprise. This is why data infrastructure should ideally be outfitted with accessible low code interfaces that render data and processes in the context of business functions to facilitate easier collaboration among diverse analyst teams. Applications built with low-code also support integration of various process automation tools with functionality that allows code, device standards and system updates to be continually updated in the background.

All these capabilities make true end-to-end process automation a reality – giving a complete view of customer and employee journeys and aligning automation capabilities throughout these full journey lifecycles. Imagine a financial team connecting automated processes not just for underwriting or credit determination, but also reaching back to the loan applications and all the way forward to the decision, to the approval letter, or to the rejection letter and the additional CTA to keep that customer engaged.  Such use cases demonstrate the awesome power and value that end-to-end process automation can deliver.

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Conclusion

Generations of process automation tools have created complexity and integration challenges for transformation teams. Success requires instituting a strong data management strategy supported by low code systems and an underlying data fabric architecture. This approach modernizes legacy assets within a coherent data framework that also serves as the foundation for AI/ML and other advanced technology platforms to operate. These successful integrations enable true end-to-end process automation that deliver exponential ROI for the organization.

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