Tag: Data Quality

RPA Can Fix Your Data Quality Issues

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Robotic Process Automation (RPA)
can fix your data quality issues

According to Gartner, “the average financial impact of poor data quality on organizations is $9.7 million per year.”  In 2016, IBM estimated  the yearly cost of poor data quality in the US alone, to be $3.1 trillion.  Anyone that works with data that has completed its processing journey understands the impacts, but why are we not talking more about the issue with data quality?

While there are many possible explanations for organizations not addressing data quality, there is no identifiable relationship between data quality and business results (i.e. it cannot be quantified) and most business functions do not have awareness of the impacts that poor data has on downstream processes.

The reality is that executives, managers, employees, accountants, et al, just accommodate the bad data and integrate work-arounds as part of their day-to-day work.  Employees accept bad data since there is little/no incentive and it is easier to accept than trying to figure out where bad data originates then work with the upstream teams to correct the behaviors/actions resulting in the bad data.

These bad data accommodations cost organizations in both employee’s time and their expense since a large portion of their time is focused on the low-value tasks associated with data correction and standardization, rather than the higher value tasks of analysis for insights, risks, and opportunities.  Even non-reporting tasks cost business processes efficiency, since simple tasks like selecting a vendor or supplier could result in a series of try/fails until the right entry is found, because of undisciplined data creation practices.

We use RPA to standardize data entry and validate data to ensure data quality.  We look for the root cause of the data issues to correct bad data practices before we apply RPA to business processes to ensure we do not harden accommodations for bad data, since fixing at the root ensures we do not have to automate bad data accommodations at every related downstream process that utilizes the data.  Also, accurate and standardized data means faster processing which translates to faster report generation, which accelerates Close, Financial Analysis, Analytics, and more.

For more information about how hardening bad data practices adds data debt to your organization, read our Process Automation Approach blog

Author:

Joshua Gotlieb

Intelligent Automation Practice Director, Vigilant Technologies

RPA Implementation Pitfalls

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Navigating the Automation Roadmap Blog Series

Not another “Pitfalls” of RPA

While industry trends are shifting towards ‘Hyperautomation’ to accelerate the automation journey, there are many considerations and reasons to use RPA, but there are also cautionary tales about RPA done wrong, which is not always talked about.  With so many people and service providers posturing about “doing RPA right” and “pitfalls of RPA”, we wanted to present a series of our no-nonsense view of RPA lessons learned from hardened industry veterans.

In our hands-on experience, and after correcting the trajectory of many poorly implemented RPA programs, we offer our top 5 list of considerations.  (We had more to share, but our marketing team forced a limit of 5 points.)

1.    Stop listening to the RPA tool vendor hype and get your RPA teams aligned with the transformation strategy.

Business teams looking for automated solutions are often working around IT, which can work against achieving the desired business outcomes.  Business leaders cannot shy away from IT because IT is feared to be some scary function in the back rooms of the organization.  Business leaders need to embrace IT as a business enabler and help IT better understand their role in supporting business objectives.  Our experience finds that organizations achieve greater results from transformation when the Executive, Business, and IT are all aligned on priorities and objectives.  To be clear, the transformation strategy should set the priorities for the organization, so everyone is aligned on how resources, projects, and obligations are being focused to achieve timely results.

2.    Managing RPA benefit expectations (Learn more)

RPA has been the ‘flavor of the month’ because it holds the promise of creating process efficiencies for business teams to focus on higher-value work.  Many executives hear ‘automation’ and believe it will reduce head count.  The reality is that many legacy business processes have evolved over time, but the business systems have not remained aligned.  Therefore, business teams are manually maintaining lower-value work to support the execution of the business function.  RPA restores balance between diminished system capabilities and (re)focusing business teams on higher-value tasks.  Business benefits are still significant, but not necessarily only for the reduction in headcount executives might be looking for.

3.    Yes, another consideration about selecting the right process candidate for automation.

There are many articles written about how important selecting the right process is for automation.  For an effective automation strategy, additional consideration must be placed on using a building blocks in your automation roadmap to map out reusable components for reducing future development complexity and expense.  This might mean you do not start with the biggest ROI or impact, but foundational building blocks are critical for a successful automation program.  For additional information, give us a call and we can work with you to map out a practical automation program that achieves the transformational benefits you are looking for.

4.    Solution architects will make or break the success of your automation program.  

Quality solution architects know how to better leverage technical capabilities of the robot and maximize the value and benefits of the automation. Skilled solution architects will also know how to incorporate available technology to re-imagine and enhance the automated solution.  Lastly, solution architects will ensure automations are cost-effective when considering reusability, available robot capacity, and capabilities of the automation team.  Underestimating the value of qualified and capable solution architects (and having the right strategy) can be the difference between success and failure of your automation program.

5.    Automating bad data practices is adding to your data debt! (Learn more)

There is a good chance you are creating data debt through the hardening and accommodating bad data practices with automation.  The typical automation approach is to identify a perceived benefit (i.e. ROI) then automate the process (if qualified).  What often gets overlooked is examining the standardization of data inputs into a process to ensure data best practices are being adhered to upstream.  Automating these upstream processes might not be sexy or produce immediate ROI, but they are part of the building blocks to maximizing the benefits of automation and creating business agility.  Be warned, bad data accommodations increase project scope, create technical (data) debt, and increase the cost of support since the automation is built on bad data.

In the coming blog articles, we will drill down into each of the 5 topics and also expand on important points not addressed in this blog, so keep an eye out over the coming weeks and months.
Thank you for reading.  Please reach out on automation@vigilant-inc.com for a spirited discussion on maximizing the benefits on RPA and how we have found the ‘secret sauce’ for achieving success with automating Oracle EBS Financials and accounting operations.  We look forward to your feedback.

Author: Joshua Gotlieb

Intelligent Automation Practice Director, Vigilant Technologies