After a decade as a data extraction expert, I‘ve seen how challenging analytics can be when data is locked away in legacy systems. Robotic process automation (RPA) offers a compelling solution – but what exactly are the benefits when it comes to analytics?
In this comprehensive guide, I‘ll tap my experience helping clients adopt RPA to explore how it can supercharge analytics capabilities in 2024 and beyond.
The Promise and Pitfalls of Legacy Systems
First, let‘s provide some context around legacy systems in large enterprises. These longstanding, often mainframe-based platforms contain data goldmines. But they predate modern databases and interfaces, making access difficult. IT staff with legacy system skills are also retiring, exacerbating the issue.
This creates major analytics headaches. Critical business data gets trapped in legacy system silos. Decision-makers struggle with partial views of processes. Foundational metrics like customer lifetime value suffer. I‘ve seen it hamper clients across industries, especially in finance and insurance.
RPA offers a lifeline – the potential to easily extract and aggregate legacy data. But as we‘ll see, it‘s just one piece of the larger analytics puzzle.
RPA‘s Role in Data Federation
At its core, RPA allows software "bots" to automate repetitive, rules-based tasks. This includes interacting with multiple legacy systems and databases across the enterprise.
While disconnected data silos can create analytics headaches, RPA bots excel at smoothly collecting data from different sources. This enables data federation – aggregating data from many locations so it can be analyzed as a unified whole.
For example, leading insurance provider Aviva saw a 75% reduction in data aggregation time after implementing RPA. This freed up resources for value-add analysis vs. just collecting data.
Other sources back up the benefits of RPA for data aggregation:
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Gartner: "RPA improves data aggregation across systems, reducing analytics costs."
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Forbes: "Leading companies use RPA to eliminate data silos and access legacy system data."
Unlocking the Power of Process Mining
With aggregated process data made readily available via RPA, companies can better optimize through techniques like process mining. This visualizes real workflows, identifying bottlenecks like over-processing.
For example, process mining at insurer Zurich cut 50% of steps from claims handling. Italian bank UniCredit reduced loan processing from over 30 mins to 5 mins.
And process data from RPA drives more than just process visualization. By applying AI algorithms to the data, further optimizations emerge – ones human analysts would likely overlook.
At global bank HSBC, machine learning analysis of RPA process data recommended order changes that improved turnaround time by 25%. The RPA bot settings were then updated to implement the AI’s recommended changes.
Analysts estimate 70% of RPA‘s business case comes from enacting process optimizations uncovered through mining RPA data.Simulating Major Process Changes with Lower Risk
Beyond process mining, RPA process data enables companies to simulate the impact of major changes before pulling the trigger.
Decisions like aggressive headcount reduction, offshoring, or over-automation often get made during cost-cutting pushes without rigorously modeling the downstream effects. But simulation can reveal the ugly side effects before they damage the business.
For example, leading insurance firm MetLife used process simulation to model impacts of offshoring claims management. The simulation exposed serious risks of delayed payouts and regulatory fines. This allowed MetLife to make a more informed sourcing decision.
Without the detailed process data from RPA, creating realistic simulations would be difficult or impossible. But with RPA, companies can confidently run "what-if" scenarios to stress test ideas.
Additional Analytics Benefits of RPA
RPA delivers auxiliary analytics benefits beyond just fueling process mining and simulation:
Diagnostic Metadata
As RPA bots operate, they log issues faced, system downtime, and other metadata. This creates a performance baseline to enhance bot debugging and optimization.
Legacy System Access
Bots extract data trapped in legacy platforms, unlocking a wealth of analytics opportunities. This can provide competitive advantage if your firm can tap historical data competitors can‘t access.
RPA Doesn‘t Eliminate Other Analytics Needs
However, it‘s important to note RPA has limited direct benefit for analytics capabilities. It mainly provides easier access to data, but organizations still need:
- Skilled data scientists to analyze and interpret data
- Visualization tools to glean insights
- Cloud warehousing for scalable storage
- Master data management for data governance
Think of RPA as an analytics enabler, not a do-it-all solution. It complements other critical technologies like data lakes and business intelligence.
Adopt RPA and Boost Analytics Maturity
RPA provides the connective tissue that breaks down data silos and powers analytics-driven processes. Combined with cloud analytics and data management capabilities, it unlocks more agile, competitive operations.
To learn more about adding RPA to your tech stack, download our comprehensive RPA implementation guide:
[CTA button to RPA implementation guide]With the right strategy, RPA can springboard your organization‘s analytics maturity. The future is bright when people and bots work together to uncover data-driven insights!