The Complete Guide to RPA‘s Benefits for Analytics in 2024

If you‘re looking to take your analytics capabilities to the next level in 2024, combining robotic process automation (RPA) with your analytics strategy could be a game-changer. In this comprehensive guide, we‘ll explore how RPA can supercharge analytics across your organization.

An Introduction to RPA

First, let‘s quickly explain what RPA is for those who aren‘t familiar. RPA involves using software "bots" powered by artificial intelligence to automate repetitive, rules-based tasks. Think of RPA bots as virtual workers that can login to systems, move files, copy data, fill out forms and complete many other tedious processes automatically.

According to Gartner, the RPA software market surpassed $1.4 billion in 2021 and is showing no signs of slowing down, with growth forecasts of nearly 20% CAGR through 2025. It‘s being adopted across many industries, with the biggest uptake in finance, insurance, manufacturing and healthcare. Common processes being automated with RPA include claims processing, HR/payroll tasks, data entry, reporting and more.

Now that you have an overview of what RPA is and its growth trajectory, let‘s look at the specific ways it can take your analytics and big data capabilities to the next level.

Access Data Trapped in Legacy Systems

One of the biggest challenges in analytics is accessing quality data locked away in legacy systems and databases. Most large, established companies rely on many outdated systems that hold decades of mission-critical data.

According to a survey from Advanced, 92% of senior IT leaders said their organization has multiple legacy systems and databases. But mining the data from these systems is incredibly difficult and time-consuming. This results in data gaps that limit the scope of analytics initiatives.

This is where RPA can make a huge impact. RPA bots can seamlessly integrate with legacy systems to extract the needed data. For example, by logging in, scraping data, mapping it correctly and placing it into your analytics environment. This "data federation" essentially liberates legacy data and makes it usable.

Let‘s look at a few examples:

  • A retail bank used RPA to access 20 years of transaction data stored in mainframe systems to improve customer lifetime value models.
  • An insurance firm tapped into decades of premium and claims data from legacy policy systems, which enabled more sophisticated risk models.
  • A manufacturer combined 40 years of production data in legacy shop floor systems with real-time data to optimize predictive maintenance algorithms.

As you can see, the use cases are endless! But the key is that RPA solves that age-old analytics struggle – efficiently accessing quality data trapped in legacy systems.

Consolidate Data from Disparate Systems

In addition to legacy data, RPA also helps consolidate data from various other disconnected systems across your organization.

The average enterprise today uses around 175 different apps according to Blissfully‘s 2021 SaaS Trends report. And these apps house data in their own databases. Add on legacy systems, custom platforms, APIs and more – and companies end up with data massively fragmented across hundreds of sources.

Manually extracting and integrating all this disparate data is extremely cumbersome. It requires extensive coding and an army of data engineers for constant ETL (extract, transform, load) work.

With RPA, bots can be configured to handle these data centralization tasks in a fraction of the time. For example:

  • Using scheduled times, triggers or on-demand calls, RPA bots can routinely extract data from different source systems via APIs or by logging in and scraping it.
  • Data mapping templates can transform the extracted data into consistent schemas and structures required for analytics.
  • Bots then load the transformed data into your unified analytics database or data warehouse environment.

This automation provides a scalable "set it and forget it" way to keep your analytics data pipelines flowing smoothly with the latest data from all source systems.

One large insurance firm used this approach to bring together client data from 30+ disconnected policy, claims and call center systems into a single view. This produced a much more comprehensive analytics foundation.

Fuel Advanced Process Mining

Process mining allows you to model and analyze processes in order to identify bottlenecks, waste, risks and other improvement opportunities. But it requires very granular data on process steps, issues, durations, variants and more.

RPA can generate extremely detailed process audit logs as bots execute tasks. These event logs capture every click, keystroke, screenshot and error as bots process transactions, move between systems, identify documents, fill forms and more.

This complete picture of how processes operate provides invaluable fuel for process mining algorithms. With these rich RPA-generated event logs, you can uncover insights like:

  • The most common process variants by region, product, customer type etc.
  • Steps where bots get stuck or processes slow down indicating pain points
  • Processes with high deviation and lack of standardization
  • Opportunities to streamline steps based on outliers with faster run times

One insurance firm combined RPA logs from claims processing bots with process mining. This revealed claims routing workflows were highly inconsistent across regions, leading to higher error rates and costs. Identifying this variability allowed them to standardize on the optimal workflow.

Discover Data-Driven Process Optimization Opportunities

Process mining provides a retrospective look at how current processes operate and where improvements may lie. But RPA data can also enable optimization recommendations by uncovering correlations.

For example, by applying machine learning algorithms to RPA process data combined with business performance metrics, you can identify connections like:

  • Increasing the batch size of payables processed on Thursdays by 20% improves the number of early vendor invoice settlements by 10%
  • Routing claims for policies sold by Agent A to Processor 1 lowers resolution time by 30% versus other processors
  • Adding an additional automatic password reset step when onboarding users from Acme Corp reduces login errors by 50%.

These are optimizations human analysts likely won‘t spot given data complexity. But smart algorithms can pinpoint optimization opportunities rooted in the data. This allows you to experiment, simulate changes and implement proven optimizations.

Model Impacts of Change With Process Simulations

Executives often consider major process changes like outsourcing tasks, offshoring roles or automating manual work. But determining the downstream effects of these shifts is difficult.

This guesswork can lead to suboptimal decisions. But what if you could model different scenarios and see the quantitative outcomes before actually changing anything?

This is possible with process simulation using RPA data. For example, you could:

  • Adjust process maps and task times based on removing specific manual steps assumed to be automated.
  • Quantify the cost, throughput and compliance impacts of modifying workflows.
  • View dashboards showing projections of headcount needs, capacity bottlenecks and opportunity costs under each scenario.

Having this insight allows you to make decisions on transforming processes with confidence based on simulated outcomes vs educated guesses.

Additional Analytics Use Cases

Beyond the major advantages we‘ve covered, RPA data can power many other analytics use cases like:

  • Forecasting: RPA provides consistent operational data to improve demand forecasting and predictive analytics models over time.
  • Anomaly detection: Deviations in RPA process logs can reveal unusual transactions or activities for further investigation.
  • Predictive maintenance: Logging bottlenecks around aging equipment can help predict maintenance needs before shutdowns occur.
  • Auditing: RPA logs give transparent records of all transactions for improved compliance and auditing.

The applications are nearly endless given the expansive data RPA provides on process execution.

Realizing the Full Potential of RPA for Analytics

In summary, RPA opens up game-changing analytical possibilities:

  • Finally tapping legacy system data to close insight gaps
  • Consolidating dispersed data into unified analytics platforms
  • Enabling sophisticated process mining from comprehensive RPA logs
  • Discovering correlations and data-driven process optimizations
  • Modeling the outcomes of process changes before executing them

The full value of RPA extends far beyond simple task automation. It can take analytics capabilities to new heights.

To learn more about capitalizing on the power of RPA for analytics, download our comprehensive RPA whitepaper:

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Or feel free to reach out to our team and we‘d be happy to discuss how RPA can transform analytics performance for your organization. The possibilities are endless once RPA and analytics join forces.

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