Data is a predictor of human behavior, the glue that holds sales and marketing teams together. In fact, data is the single most powerful growth tool in any business arsenal. On average, customers across the globe generate 2.5 quintillion bytes of data per day. Within the next five years, businesses will need to analyse a massive 150 trillion gigabytes of data to gain the insights needed to stay competitive.
In the short term, ‘big data’ can help your business automate critical processes, reduce waste, and maximise value for the customer. Essentially, big data can be leveraged to help you identify key areas in your workflows for automation. It can also help you eliminate waste through predictive modeling. The latter allows you to pinpoint gaps in inventory systems and remove workflow frictions that are decreasing productivity levels.
In the long term, data facilitates growth by giving you visibility into critical KPIs. Undeniably, it’s become increasingly apparent that big data is key to helping businesses optimise processes. And, most importantly, Lean, DevSecOps, and Agile are all intertwined with your ability to analyse data at scale.
As businesses continue to adopt robotic process automation to streamline workflows and create additional business value, finding ways to gather and analyse data is key to creating an RPA-driven lean environment. To do this, it’s important to understand the deep relationship between RPA and big data and leverage that relationship to make consistent changes in your value stream and the processes that define that value stream.
What is Robotic Process Automation?
Robotic Process Automation (RPA) is a technology that automates processes and reduces human intervention via the use of “robots”(software-based AI). Leslie Willcocks — Professor of Work, Technology, and Globalization at the London School of Economics and Political Science — defines RPA in perhaps the simplest terms.
“RPA takes the robot out of the human.” — Leslie Willcocks
Essentially, RPA takes mundane, repetitive routines that workers have to perform daily and automates them. Still, workers invariably fear that robots will replace their jobs. A recent report shows that the fears may not be unfounded. However, the same report also finds that a “1 percent increase in the stock of robots per manufacturing worker leads to a 0.1 percent boost to productivity…enough to drive meaningful economic growth.” All things considered, a 30% rise in RPA would boost global GDP by 5.3%. RPA actually empowers workers to engage in higher-value tasks, especially those that require creativity, critical thinking, and emotional intelligence.
For businesses, the value of automating business processes is immediate. This includes reduced error rates, increased productivity levels, and reduced worker strain. In all, 98% of IT leaders agree that RPA is vital in driving business benefits in today’s digital ecosystem. For workers, these repetitive processes are a barrier to higher productivity. Not surprisingly, 13% of employees cite repetitive tasks as their biggest time-waster (ahead of meetings, emails, and workplace distractions).
What is Lean?
Originally created by Toyota, lean is a method of waste reduction and continuous improvement. There’s a marriage-of-sorts between Lean, Agile, and DevOps, however. If Agile is product management through increased team collaboration and DevOps is software management with shift-left product testing, then lean is the overarching process that facilitates both.
- Understanding the value that customers want out of your product/service
- Discovering the value stream for the product/service and identifying steps that generate waste within that stream
- Ensuring all service processes are congruent and efficient
- Reducing inventory waste and making data available for the entire value stream
- Implementing continuous improvement
In other words, lean involves assessing the value of your product, mapping out the value stream that facilitates the making of that product, ensuring all business processes work seamlessly, leveraging data to improve those processes, and consistently enhancing your value stream.
What is Big Data?
Big data is two things. It refers to all of the structured and unstructured data that your business accumulates. And, it refers to the field of analysing that data via predictive models and algorithms. Specifically, big data refers to both your data itself and how you handle it. Chances are, you have data and lots of it.
Nearly 90% of all data was created over the last few years. Every day, consumers generate more and more data. And, subsequently, your business needs to process that accumulating data. As far back as 2015, businesses that invested heavily in big data saw their profits grow by a baseline of 8%. In today’s digitally transformed landscape, however, big data isn’t a defining feature — it’s a necessity.
Your data is the gas that powers your business engine. It helps you make decisions, uncover customer behaviors, and drive profitability within your processes. And, big data also fuels RPA — which in turn helps you adhere to the lean methodology.
Understanding the Relationship Between RPA, Lean, and Big Data
When most businesses think of RPA, they generally think in terms of human processes. But RPA robots can also be used to sort and analyse data, as well. One of the primary issues with big data is quality. 95% of businesses cite unstructured data as their primary barrier to successful big data adoption.
RPA bots can analyse, sort, and examine data before passing it on to human controllers (or other bots). This means that RPA tools can be used to sift through a huge big data framework to identify critical data — which helps you analyse data faster and create more realistic and accurate predictive models.
Most importantly, these models can empower your lean methodology. Essentially, lean is rooted in waste reduction and continuous improvement. By using RPA to identify “good” data in your big data framework, you can better understand your overall value stream and uncover wasteful, time-consuming processes.
Not only can RPA collate data across your big data architecture, but it can also do it in a hyper-structured manner. This means you can pinpoint and extract the most valuable data in your architecture and immediately leverage that data to make impactful decisions — like pinpointing the most business-critical process to automate.
Leverage Shibumi to Implement Lean With RPA and Big Data
RPA, lean, and big data are all different value levers in your organization. Together, they enable each other to boost performance, reduce waste, and breed efficient business practices. If you’d like to leverage all three, Shibumi can help. With Shibumi, you can track and prioritize RPA opportunities across your big data architecture. Need actionable insights from your big data stack to guide you towards leaner, more efficient RPA utilisation? If so, Shibumi can find those insights and deliver them directly to your dashboard. Correspondingly, are you ready to maximise the value of your RPA processes? Then, don’t wait to check out our free demo today.