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RPA for Reporting: Ultimate Guide With 17 Use Cases in 2024

Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Reporting is a vital process in all businesses to gain insights about business finances, growth, employees, and customers. However, reporting is a repetitive and time-consuming task, where 51% of report delivery departments have to deliver the same data several times, and 50% of managers are not satisfied with the speed of delivery.

RPA bots can play a significant role in automating reporting processes because they can:

  • collect data from different business resources
  • generate and send reports based on the collected data.
  • update data in other systems (e.g. CRM) based on reports

These reports can help teams understand their business better and use data-driven forecasts to make better decisions.

Which reporting tasks can be automated by RPA?

Reporting is the process of pulling data from business resources, structuring it in a meaningful way, and delivering it to decision makers or customers. RPA is a great candidate to automate these repetitive tasks because they are GUI-based and can be completed easily by a bot. For example, RPA bots can:

1. Generate reports

Bots can input the collected data into designated business documents (e.g. excel tables, sheets, and datasets), merge columns, rows, or tables, complete operations like Excel’s PivotTable, use API functions, to generate reports and save them in target folders.

2. Collect business data

RPA bots can be programmed to log into business accounts or emails, scan documents and attachments, and identify and collect data relevant to the task.

3. Update business datasets

Bots can be programed to identify parameters included in reports, such as:

  • CRM data: customer ID information, purchases, service tickets, etc.
  • Employee data: employees personal and business information, onboarding and offboarding documents, PTOs.
  • Finance data: invoices, account payable, account receivable.
  • Logistics data: shipments, tracking, delivery.

Bots can update these parameters at a specific time (e.g. daily/weekly/monthly) or at a triggering event (e.g. new email).

4. Send reports

After generating and saving business reports, the bot can be programmed to send the report to a specific person or group (e.g. department, manager, employee, customer). This is a tricky task, especially in enterprises where contact information changes constantly with new clients and employees. However, since bots can access the updated datasets, the report sending process should be error-free.

Bots can send reports via various channels such as email, WhatsApp, Slack messages etc.

Which departments benefit the most from report automation?

All business departments with large datasets that require constant updates and overall management would benefit, especially if data quality and consistency are important. For example:

Finance

It is important to keep financial reports constantly updated and as error-free as possible, because they determine a company’s status in the market, compliance to regulatory and legal legislations, and are the basis of the company’s future forecasting.

The finance department needs to submit several reports on a regular basis which can benefit from automation, including:

  1. Annual and quarterly financial statements
  2. Compliance reports
  3. Profit and loss (P&L) reports
  4. Tax reports

Other than reporting, RPA can automate numerous processes in the finance department. To explore more, feel free to read our in-depth article RPA use cases in finance.

HR

According to a 2016 survey, 97% of HR departments gather employee metrics. These can include:

  1. HR analytics like demographics. For example gender ratios and gender pay gap are important metrics to regularly measure, report and improve
  2. Cost analysis considering
    1. number of FTEs by department
    2. cost per FTE over time and vs benchmarks
  3. Employee satisfaction
    1. Turnover (i.e. churn): Possibly the most important metric on employee happiness. Companies with high churn would struggle to build competencies
    2. Absenteeism can be another metric for understanding employee satisfaction or health.
  4. Hiring (e.g. time to fill a position)
  5. Other topics (e.g. training and satisfaction with training programs)

However, 95% of HR employees have problems in analyzing and reporting these metrics, due to poor integration of data systems. Automating HR data and metrics reporting enable businesses to analyze their workforce and talents, develop recruiting strategies, create a transparent and fair promotion and let go processes, as well as track employee performance and job satisfaction.

HR also benefits from RPA in different areas such as recruiting, operations, and payroll. Feel free to read our detailed article about RPA use cases in HR

Marketing

Bots can pull data from business platforms such as CRMs, ERPs, social media, data warehouses, and company’s digital channels to create business analysis reports about marketing performance, current customers, reviews and rankings, and trending products and services. Leveraging RPA for automating marketing reports enables:

  1. consistent update of CRM data
  2. paid marketing reporting identifying top performing channels etc.
  3. organic marketing (e.g. SEO) reporting
  4. social media reporting

Additionally, automating data reporting in marketing can create consistent datasets for further use in marketing analytics.

Which industries benefit the most from report automation?

Especially industries that provide complex technical solutions as a service benefit from report automation. This is because they need to communicate with their stakeholders using complex data and reports facilitate this.

Telecom

In telecom managed services, operators get services from managed service providers (e.g. Huawei, Nokia) to expand or maintain their broadband or fixed telecom networks. The performance of these networks is complex to analyze as it is a time series data with numerous parameters (call drops, bandwidth experienced by different users etc.). To understand the quality of the service that they are getting, operators require managed service providers to submit many complex reports regularly. For example, during our consulting projects, we worked with a managed service provider in South East Asia to run an audit and identified more than 100 reports generated to serve one operator.

Types of reports can include:

  1. Network roll-out stats when new network equipment (e.g. 5G) is being rolled out
  2. Quality of service for voice or data services
  3. Outages

Marketing agencies

Modern marketing is data-intensive and agencies are building more sophisticated dashboards for their clients pulling data from many different advertising and analytics systems. When there are no API connections provided by a service or if a team prefers to use RPA, RPA can serve in the automation of such reports.

How to make the most of RPA in reporting?

In order to make the best of RPA in report automation, business can follow a few steps:

  • Understand reporting needs
    • which departments spend most time on reporting
    • which report errors cost the most
    • important KPIs to track
    • software integrations and budget
  • Optimize reporting needs
    • Most reports are not read. It is easier to stop producing reports that are not read than to automate them. Based on our team’s consulting experience, companies can reduce their number of reports at least by half by analyzing how much reports are read and how much they influence operations
    • Replace popular reports with dashboards. Dashboards are updated real time and can provide greater visibility. Critical metrics should be available 24/7 and companies can build dashboards rather than sharing reports for them.
  • Choose the right RPA tool
    • There are different RPA tools ranging from full-code with most customization to no-code with less customized built-in reporting tools.
    • It is important to choose an RPA tool relying on criteria like the team’s programming skills and knowledge, Total Cost of Ownership (TCO) etc.

To learn about RPA best practices, explore our 11 step best practice RPA implementation guide.

For more on RPA

To explore RPA use cases in different business areas, feel free to read our articles:

If you want to explore the details of RPA in a comprehensive manner, download our in-depth whitepaper on the topic:

Get RPA Whitepaper

If you want to benefit from RPA, learn about the best tools from our comprehensive data-driven list of RPA vendors, and let us help you:

Find the Right Vendors

This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.

Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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