Ever wondered how big platforms consumed by most of the world’s population manage to stay online, flawlessly, despite outages, or even disasters?
They break their own systems on purpose!
Yeah, that’s right!
It might be hard for you to believe, but companies like Netflix practice something called Chaos Engineering, which is a proactive strategy of injecting failures deliberately into the systems to test how they behave under stressful conditions. The idea might look simple but it’s extremely powerful.
Based on simple concept; if you can prepare for failure, you can survive it!
What is Chaos Engineering?
Before diving deeper, let’s quickly break down what Chaos Engineering means.
Chaos Engineering is a disciplined approach to identifying a system’s ability to withstand turbulent conditions. By intentionally introducing failures into a system, businesses can literally test their system’s resilience under stressful conditions.
Instead of waiting for something to break unexpectedly, engineers simulate real-world problems like server crashes, network delays, or entire region outages to observe how the system responds. The goal is to identify weaknesses and fix them before they impact users.
Key Principles of Chaos Engineering
Build a hypothesis – Predict how the system should behave under failure.
Run experiments in production – Or as close to production as safely possible.
Monitor and measure – Analyze how the system reacts.
Learn and improve – Use the findings to strengthen system architecture and recovery processes.
Breaking with Purpose: The Philosophy Behind Chaos Engineering
You could predict how your systems will behave under stress, before the stress hits with Chaos Engineering. It’s not about breaking systems recklessly, it’s about introducing controlled failure to expose weaknesses and strengthen a system’s ability to withstand and recover from disruption. Think of it as a business fire drill for a technology stack, controlled, intelligent, and immensely valuable.
With DRaaS and Chaos Engineering combined, organizations can prepare for disaster. These methodologies validate real-world readiness and uncover vulnerabilities before they can impact operations.
Why It Matters
Prepares systems for the unexpected
Uncovers hidden bugs and vulnerabilities
Improves reliability, availability, and user trust
Helps teams build confidence in their systems
The Secret Behind Netflix’s Smooth Streaming: Controlled Chaos
Netflix, one of the pioneers in the OTT streaming service, doesn’t wait for a system to fail in the wild due to server crashes, network delays, or entire region outages. Instead, it leverages tools like Chaos Monkey, which randomly shuts down services in production to test the system’s resilience against unexpected instances and ensure graceful recovery without affecting user experience.
In simple terms, Chaos Monkey is like a mischievous virtual monkey that randomly causes disruptions in Netflix’s computer systems. It sounds counterintuitive, but the purpose of Chaos Monkey is to intentionally create controlled failures to test the resilience of Netflix’s infrastructure.
For example, they might randomly disconnect a server or overload a system, just to see if everything keeps running smoothly. If it does, awesome! If not, the engineers can swoop in, figure out what went wrong, and make it even stronger for next time.
This way, Netflix ensures your binge-watching never gets interrupted, even when things break behind the scenes.
Next time your favorite show streams seamlessly, remember; Netflix breaks things first, on purpose!
Ready to build chaos-proof systems? Connect with us to explore how Chaos Engineering and DRaaS can future-proof your infrastructure.
The legacy deal in tech? Build it once, build it huge, and hope it can handle everything the digital world throws its way – impressive, maybe, but incredibly hard to change or move. This monolithic approach, while familiar, often struggles to keep pace with the fluid nature of digital transformation, becoming a bottleneck that stifles innovation and delays crucial market entry.
Understanding Microservices Architecture: The Agile Evolution
Think about those moments your typical digital platform truly gets tested. The Black Friday checkout frenzy. The song that suddenly owns everyone’s playlist on Spotify. The Netflix series that sparks a global binge. These aren’t just traffic spikes; they’re the moments that reveal your underlying potential. Can your architecture handle the pressure with grace?
The old answer – the monolith – often defaults to brute force: over-provisioning. Keeping the entire stadium lights on full blast for a handful of people. It’s a costly insurance policy that often doesn’t pay off, and more importantly, it doesn’t inherently make you faster or more adaptable to new market demands.
The Power of Distributed Strength in Microservices
Microservices architecture offers a more elegant and strategically advantageous solution. Build specialized teams, each owning a key capability. Catalog. Recommendations. Payments. Each operates independently, allowing for parallel development and faster iteration cycles. When a new feature needs to be rolled out in the catalog service, it can be developed and deployed without requiring a full system update, significantly accelerating time-to-market. This agility allows businesses to respond rapidly to customer needs and gain a competitive edge. (Richardson)
Netflix, a veteran of the digital frontier, learned this lesson early. The monolithic path was a scaling dead end and a speed inhibitor. Their move to microservices wasn’t just a tech upgrade; it was a strategic embrace of resilience through distribution; the ability to innovate and release features at an unprecedented pace, fueling their exponential growth. Small hiccups in one area don’t disrupt the whole experience, and new ideas can be tested and deployed rapidly. (Netflix TechBlog)
Designing for Connection: The API-First Approach in Microservices
How do these independent pieces work together seamlessly and quickly? Through clear, intentional conversations: API-first development. Design the way they’ll communicate before you build the individual components. This creates clarity, allows teams to move in parallel without waiting on each other, and builds an agile digital platform where every part knows its role and how to connect efficiently. It’s like planning the routes before the explorers set out, ensuring everyone knows the destination and the path. This parallel development directly translates to faster feature releases and quicker responses to market opportunities.
Letting the Cloud Be the Engine: Focus on What Truly Matters
Now, imagine taking away even the worry of the underlying machinery. That’s the quiet power of serverless. AWS serverless lets you build microservices that scale effortlessly with demand. The infrastructure fades into the background, allowing your team to focus purely on delivering value. (AWS Serverless)
For that Black Friday rush, serverless microservices architecture means your checkout scales automatically, behind the scenes. No frantic manual adjustments needed. No late-night fire drills to spin up more servers. The system breathes with the demand, then gently settles back. Smart. Efficient. The hallmark of truly scalable software systems.
The Unseen Hand: Orchestrating Complexity into a Smooth Experience
A network of independent services needs coordination. A service mesh acts like the subtle conductor of your microservices orchestra. It manages the flow of requests, enforces the rules, and provides the insights you need to understand the intricate performance and identify areas for optimization that can support growth. (Service Mesh Ultimate Guide)
Spotify, masters of delivering seamless audio at scale, rely on deep visibility into their vast microservices landscape. Understanding the connections and performance is key to ensuring a smooth experience for millions and identifying opportunities to personalize and enhance the user experience, directly contributing to user retention and growth. (Spotify Engineering)
The Future Isn’t About Size, It’s About Smart Evolution
This move to microservices, powered by API-first development, the agility of serverless, and the orchestration of a service mesh, isn’t about adding complexity for its own sake. It’s about building a digital foundation that’s inherently more adaptable, more resilient, and ultimately, more reliable for your users. Driving business growth through faster time-to-market and the ability to quickly respond to evolving customer needs. It’s the core of intelligent enterprise modernization that directly impacts the bottom line.
R Systems helps enterprises navigate this crucial evolution. We see beyond the technology to the fundamental shift in how value is created and delivered in the digital age. It’s about empowering your teams and your architecture to be nimble, responsive, and ready for whatever comes next.
These days, customers expect 24/7 availability, and falling short of that standard can result in reputational damage, translating to millions in financial losses.
It’s always-on digital economy, so downtime is no longer a minor inconvenience; it is a significant threat to business continuity. This is why Disaster Recovery as a Service (DRaaS) has evolved into a strategic pillar that protects businesses from the unexpected, such as cyberattacks, system failures, or natural disasters.
DRaaS: The Backbone of Business Continuity
DRaaS has gained momentum in recent years, primarily due to the increasing awareness of the importance of data security and business continuity. As businesses face a growing number of threats, from cyberattacks to natural disasters, traditional recovery methods are proving too slow and too expensive.
DRaaS is a cloud-based solution that delivers on-demand data protection and disaster recovery over the internet on a pay-as-you-go model. It allows enterprises to outsource their entire DR planning and execution to expert third-party DRaaS provider. By replicating and hosting both physical and virtual servers, DRaaS enables seamless failover during disasters, minimizing downtime and reducing Recovery Point Objectives (RPOs) and Recovery Time Objectives (RTOs).
With the need for 24/7 availability and the increasing threat of cyberattacks, DRaaS adoption is accelerating. The global DRaaS market is projected to grow from $10.7 billion in 2023 to $26.5 billion by 2028, at a CAGR of 19.8%, according to MarketsandMarkets.
What makes DRaaS a game-changer for businesses
Cloud-Powered Reliability
DRaaS delivers disaster recovery and high availability through the cloud, ensuring critical applications across platforms like IBM i, AIX, Linux on Power, and x86 remain protected and recoverable, both within your data center and across regions.
Real-Time Replication
DRaaS replicates your critical business data and IT infrastructure in real time to secure, offsite data centers or the cloud. In case your primary systems fail, you can instantly switch to the replicated environment, minimizing downtime and ensuring business continuity.
Rapid Recovery from Disasters
Whether it’s a ransomware attack, power outage, or natural disaster, DRaaS allows businesses to activate recovery protocols swiftly. With no need for additional hardware or facilities, DRaaS slashes CapEx and OpEx. You pay only for the resources you use, with rapid recovery capabilities that dramatically reduce RTOs and RPOs.
Geographic Diversity for Greater Resilience
One of the most powerful features of DRaaS is geographic redundancy. Backup environments are stored in locations far removed from the primary infrastructure, sometimes in completely different countries. This geographic diversity protects against regional disasters that could otherwise affect both your main and backup systems.
Cost-Effective and Scalable
Unlike traditional DR solutions that require significant investment in secondary data centers and infrastructure, DRaaS offers a pay-as-you-go model. You get enterprise-level protection at a cost-effective price, and the solution can scale with your business needs.
Seamless Integration
The cloud is at the core of DRaaS, with both public and private cloud models gaining traction. Modern DRaaS platforms offer built-in and third-party integrations, providing businesses with flexible, plug-and-play solutions that seamlessly align with their existing tech environments.
Future-Proofing Business Continuity with DRaaS
In a world where 96% of organizations faced at least one incident of downtime between 2019 and 2022, preparing for the unexpected is no longer optional. The cyber threats are evolving rapidly, and disruptions have become more frequent, so business resilience in 2025 hinges on proactive planning, not reactive response.
Disaster Recovery as a Service (DRaaS) empowers businesses to protect critical operations, minimize downtime, and recover swiftly, without compromising agility or overspending. It’s not just a safeguard; it’s a strategic advantage.
With deep expertise in cloud-based DRaaS, R Systems helps organizations build strategies that scale with their needs. Whether you’re looking to modernize your infrastructure or partner with a reliable DRaaS provider, R Systems delivers the experience, tools, and cloud capabilities to keep your business streamlined and running.
Safeguard your business with cloud-powered resilience—partner with R Systemsfor smarter, faster disaster recovery.
Migrating from a monolithic architecture to a microservices-based system is a significant step towards better software scalability, flexibility, and maintainability. By breaking applications into smaller, independent services, businesses can accelerate development cycles, improve fault isolation, and leverage the best technologies for each function. However, while microservices offer significant advantages, the transition is complex and introduces several challenges.
To ensure a smooth migration, companies must anticipate and address key obstacles impacting performance, coordination, and cost. Below, we will explore five major challenges businesses face during this transition and provide actionable strategies to overcome them.
Team Management: Restructuring for Microservices Success
One of the primary challenges of migrating to microservice architecture is restructuring your teams to manage independent services effectively. Unlike monolithic systems, where a single team oversees the entire application, microservices demand cross-functional, autonomous teams responsible for different services. This process can be complex and lead to skill gaps, communication breakdowns, and coordination challenges.
To overcome these issues, companies should invest in training programs to equip their teams with the necessary skills to manage microservices. Adopting a DevOps culture can prove very beneficial in fostering collaboration between development and operations, while domain-driven design (DDD) helps align teams with business functionalities.
Monolith Decomposition: Breaking It Down Without Breaking Everything
Breaking down a monolithic system into microservices is a meticulous process that demands careful planning and execution. This decomposition involves identifying and isolating different functionalities within the monolith and transforming them into independent services. Without a clear strategy, businesses risk introducing inconsistencies, breaking dependencies, or increasing system complexity.
A successful migration starts with thoroughly analyzing the existing monolithic system, identifying core components and their dependencies. A detailed migration plan should outline the steps for decomposing the monolith and transforming it into microservices. API gateways can further streamline the process and help manage service communication efficiently.
System Updates: Managing Service Changes Without Downtime
Keeping a microservices-based system up to date without disrupting operations requires a well-orchestrated approach. Unlike monolithic applications, where updates impact the entire system at once, microservices demand careful coordination across multiple independent services. Without careful planning, updates can lead to service conflicts, inconsistencies, or downtime.
To minimize risks, companies should implement continuous integration and continuous deployment (CI/CD) pipelines to automate updates and reduce errors. Feature flags allow controlled rollouts, minimizing disruptions, while blue-green deployments enable seamless environment switching. Regular monitoring and testing further ensure system stability after each update.
Database Distribution: Ensuring Data Consistency Across Services
Unlike monolithic architecture, where a single database serves the entire system, microservices often require separate databases. This approach improves scalability but introduces challenges in maintaining data consistency, transaction management, and system performance. Without a solid data management strategy, businesses risk data integrity issues and inconsistencies across services.
To address these challenges, companies should adopt an event-driven architecture to synchronize data across services in real time. CQRS (Command Query Responsibility Segregation) helps separate read and write operations, reducing complexity. Additionally, distributed database solutions like Kafka or Saga patterns enable better transaction management and ensure consistency across microservices.
Cost Management: Balancing Scalability with Budget Constraints
Operating a microservices architecture introduces new cost considerations, as each service comes with its own infrastructure, cloud usage, networking, and monitoring expenses. Unlike monolithic systems, where resources are centrally managed, microservices require independent scaling and maintenance, which can quickly drive up costs. Without a clear cost management strategy, businesses risk overspending on resources they may not fully utilize.
To optimize costs, companies should conduct a thorough cost-benefit analysis before migrating to microservices. Leveraging auto-scaling ensures resources are allocated dynamically based on demand, while serverless computing can further reduce infrastructure costs. Additionally, implementing cost analytics tools helps monitor usage, optimize spending, and prevent over-provisioning.
How R Systems Can Help
Migrating from a monolithic system to a microservices architecture requires careful planning, technical expertise, and a clear strategy. At R Systems, we help businesses navigate this transformation seamlessly, ensuring they unlock the full potential of microservices without unnecessary complexity or disruption.
Our approach focuses on:
Scalability at the Core – Independent services scale dynamically with demand, optimizing resource usage without overloading the system.
Agility for Faster Releases – Modular architecture accelerates time-to-market, reducing development cycles by up to 40%.
Fault Isolation – Failures remain contained within specific services, improving reliability and preventing outages.
Technology Diversity – We select the best-fit technologies for each microservice, ensuring adaptability and innovation.
Future-Proofing – Cloud-native architectures seamlessly integrate with AI, IoT, and other emerging technologies.
With the right strategy and expert guidance, businesses can modernize their applications, enhance scalability, and achieve long-term operational efficiency.
Looking to transform your system? Contact R Systems today to start your microservices journey.
As the world of smart TVs evolves, delivering immersive and seamless viewing experiences is more crucial than ever. At Velotio Technologies, we take pride in our proven expertise in crafting high-quality TV applications that redefine user engagement. Over the years, we have built multiple TV apps across diverse platforms, and our mastery of cutting-edge JavaScript frameworks, like EnactJS, has consistently set us apart.
Our experience extends to WebOS Open Source Edition (OSE), a versatile and innovative platform for smart device development. WebOS OSE’s seamless integration with EnactJS allows us to deliver native-quality apps optimized for smart TVs that offer advanced features like D-pad navigation, real-time communication with system APIs, and modular UI components.
This blog delves into how we harness the power of WebOS OSE and EnactJS to build scalable, performant TV apps. Learn how Velotio’s expertise in JavaScript frameworks and WebOS technologies drive innovation, creating seamless, future-ready solutions for smart TVs and beyond.
This blog begins by showcasing the unique features and capabilities of WebOS OSE and EnactJS. We then dive into the technical details of my development journey — building a TV app with a web-based UI that communicates with proprietary C++ modules. From designing the app’s architecture to overcoming platform-specific challenges, this guide is a practical resource for developers venturing into WebOS app development.
What Makes WebOS OSE and EnactJS Stand Out?
Native-quality apps with web technologies: Develop lightweight, responsive apps using familiar HTML, CSS, and JavaScript.
Optimized for TV and beyond: EnactJS offers seamless D-pad navigation and localization for Smart TVs, along with modularity for diverse platforms like automotive and IoT.
Real-time integration with system APIs: Use Luna Bus to enable bidirectional communication between the UI and native services.
Scalability and customization: Component-based architecture allows easy scaling and adaptation of designs for different use cases.
Open source innovation: WebOS OSE provides an open, adaptable platform for developing cutting-edge applications.
What Does This Guide Cover?
The rest of this blog details my development experience, offering insights into the architecture, tools, and strategies for building TV apps:
R&D and Designing the Architecture
Choosing EnactJS for UI Development
Customizing UI Components for Flexibility
Navigation Strategy for TV Apps
Handling Emulation and Simulation Gaps
Setting Up the Development Machine for the Simulator
Setting Up the Development Machine for the Emulator
Real-Time Updates (Subscription) with Luna Bus Integration
Packaging, Deployment, and App Updates
R&D and Designing the Architecture
The app had to connect a web-based interface (HTML, CSS, JS) to proprietary C++ services interacting with system-level processes. This setup is uncommon for WebOS OSE apps, posing two core challenges:
Limited documentation: Resources for WebOS app development were scarce.
WebAssembly infeasibility: Converting the C++ module to WebAssembly would restrict access to system-level processes.
Solution: An Intermediate C++ Service capable of interacting with both the UI and other C++ modules
To bridge these gaps, I implemented an intermediate C++ service to:
Communicate between the UI and the proprietary C++ service.
Use Luna Bus APIs to send and receive messages.
This approach not only solved the integration challenges but also laid a scalable foundation for future app functionality.
Architecture
The WebApp architecture employs MVVM (Model-View-ViewModel), Component-Based Architecture (CBA), and Atomic Design principles to achieve modularity, reusability, and maintainability.
App Architecture Highlights:
WebApp frontend: Web-based UI using EnactJS.
External native service: Intermediate C++ service (w/ Client SDK) interacting with the UI via Luna Bus.
Block Diagram of the App Architecture
Choosing EnactJS for UI Development
With the integration architecture in place, I focused on UI development. The D-pad compatibility required for smart TVs narrowed the choice of frameworks to EnactJS, a React-based framework optimized for WebOS apps.
Why EnactJS?
Built-in TV compatibility: Supports remote navigation out-of-the-box.
React-based syntax: Familiar for front-end developers.
Customizing UI Components for Flexibility
EnactJS’s default components had restrictive customization options and lacked the flexibility for the desired app design.
Solution: A Custom Design Library
I reverse-engineered EnactJS’s building blocks (e.g., Buttons, Toggles, Popovers) and created my own atomic components aligned with the app’s design.
This approach helped in two key ways:
Scalability: The design system allowed me to build complex screens using predefined components quickly.
Flexibility: Complete control over styling and functionality.
Navigation Strategy for TV Apps
In the absence of any recommended navigation tool for WebOS, I employed a straightforward navigation model using conditional-based routing:
High-level flow selection: Determining the current process (e.g., Home, Settings).
Step navigation: Tracking the user’s current step within the selected flow.
This conditional-based routing minimized complexity and avoided adding unnecessary tools like react-router.
Handling Emulation and Simulation Gaps
The WebOS OSE simulator was straightforward to use and compatible with Mac and Linux. However, testing the native C++ services needed a Linux-based emulator.
The Problem: Slow Build Times Cause Slow Development
Building and deploying code on the emulator had long cycles, drastically slowing development.
Solution: Mock Services
To mitigate this, I built a JavaScript-based mock service to replicate the native C++ functionality:
On Mac, I used the mock service for rapid UI iterations on the Simulator.
On Linux, I swapped the mock service with the real native service for final testing on the Emulator.
This separation of development and testing environments streamlined the process, saving hours during the UI and flow development.
Setting Up the Development Machine for the Simulator
To set up your machine for WebApp development with a simulator, ensure you install the VSCode extensions — webOS Studio, Git, Python3, NVM, and Node.js.
Install WebOS OSE CLI (ares) and configure the TV profile using ares-config. Then, clone the repository, install the dependencies, and run the WebApp in watch mode with npm run watch.
Install the “webOS Studio” extension in VSCode and set up the WebOS TV 24 Simulator via the Package Manager or manually. Finally, deploy and test the app on the simulator using the extension and inspect logs directly from the virtual remote interface.
Note: Ensure the profile is set to TV because the simulator only works only for the TV profile.
ares-config --profile tv
Setting Up the Development Machine for the Emulator
To set up your development machine for WebApp and Native Service development with an emulator, ensure you have a Linux machine and WebOS OSE CLI.
Install essential tools like Git, GCC, Make, CMake, Python3, NVM, and VirtualBox.
Build the WebOS Native Development Kit (NDK) using the build-webos repository, which may take 8–10 hours.
Configure the emulator in VirtualBox and add it as a target device using the ares-setup-device. Clone the repositories, build the WebApp and Native Service, package them into an IPK, install it on the emulator using ares-install, and launch the app with ares-launch.
Setting Up the Target Device for Ares Command to be Able to Identify the Emulator
This step is required before you can install the IPK to the emulator.
Note: To find the IP address of the WebOS Emulator, go to Settings -> Network -> Wired Connection.
Real-Time Updates (Subscription) with Luna Bus Integration
One feature required real-time updates from the C++ module to the UI. While the Luna Bus API provided a means to establish a subscription, I encountered challenges with:
Lifecycle Management: Re-subscriptions would fail due to improper cleanup.
Solution: Custom Subscription Management
I designed a custom logic layer for stable subscription management, ensuring seamless, real-time updates without interruptions.
Packaging, Deployment, and App Updates
Packaging
Pack a dist of the Enact app, make the native service, and then use the ares-package command to build an IPK containing both the dist and the native service builds.
npm run packcd com.example.app.controllermkdir BUILDcd BUILDsource /usr/local/webos-sdk-x86_64/environment-setup-core2-64-webos-linuxcmake ..makeares-package-n app/dist webos/com.example.app.controller/pkg_x86_64
Deployment
The external native service will need to be packaged with the UI code to get an IPK, which can then be installed on the WebOS platform manually.
WebOS OSE 2.0.0+ supports Firmware-Over-the-Air (FOTA) using libostree, a “git-like” system for managing Linux filesystem upgrades. It enables atomic version upgrades without reflashing by storing sysroots and tracking filesystem changes efficiently. The setup involves preparing a remote repository on a build machine, configuring webos-local.conf, and building a webos-image. Devices upgrade via commands to fetch and deploy rootfs revisions. Writable filesystem support (hotfix mode) allows temporary or persistent changes. Rollback requires manually reconfiguring boot deployment settings. Supported only on physical devices like Raspberry Pi 4, not emulators, FOTA simplifies platform updates while conserving disk space.
Key Learnings and Recommendations
Mock Early, Test Real: Use mock services for UI development and switch to real services only during final integration.
Build for Reusability: Custom components and a modular architecture saved time during iteration.
Plan for Roadblocks: Niche platforms like WebOS require self-reliance and patience due to limited community support.
Conclusion: Mastering WebOS Development — A Journey of Innovation
Building a WebOS TV app was a rewarding challenge. With WebOS OSE and EnactJS, developers can create native-quality apps using familiar web technologies. WebOS OSE stands out for its high performance, seamless integration, and robust localization support, making it ideal for TV app development and beyond (automotive, IOT, and robotics). Pairing it with EnactJS, a React-based framework, simplifies the process with D-pad compatibility and optimized navigation for TV experiences.
This project showed just how powerful WebOS and EnactJS can be in building apps that bridge web-based UIs and C++ backend services. Leveraging tools like Luna Bus for real-time updates, creating a custom design system, and extending EnactJS’s flexibility allowed for a smooth and scalable development process.
The biggest takeaway is that developing for niche platforms like WebOS requires persistence, creativity, and the right approach. When you face roadblocks and there’s limited help available, try to come up with your own creative solutions, and persist! Keep iterating, learning, and embracing the journey, and you’ll be able to unlock exciting possibilities.
Many organizations place a strong focus on collecting as much data as possible. However, being data-rich is not the same as being insight-rich. While collecting data is important, analyzing it to gain insights is invaluable to maintaining the competitive edge and long-term business success.
Armed with insights, organizations can get quantitative and qualitative answers to business-critical questions that enable sound decision-making with number-driven rationale.
Continuous and sustained business success depends on how quickly and strategically organizations can convert their data into insights, then put them into action. If you aren’t able to leverage insights-to-action, the following five factors might be your culprits:
Not Democratizing the Use of Actionable Data
Insight-driven organizations don’t just gather data, they put it to use to create better products, design more effective strategies, and engender a superior customer experience.
In a nutshell, “Data Democratization” refers to hindrance-free, easy access to data for everyone within an organization. Further, all stakeholders should be able to understand this data to expedite decision-making and unearth opportunities for quicker growth.
The distribution of information through Data Democratization enables teams within an organization to gain a competitive advantage by identifying and acting on critical business insights. It also empowers stakeholders at all levels to be accountable for making data-backed decisions.
Concerns that commonly keep organizations from democratizing data include; poor handling and misinterpretation by non-technical teams, which can lead to inept decision-making.
Additionally, with more people having access to business-critical data, the question of maintaining data security and data integrity cannot be ignored. Another concern relates to cleaning up inconsistencies – even in the smallest datasets and files. These may need to be converted into different formats before they can be used.
However, technical innovations – such as cloud storage, software for data visualization, data federation, and self-service BI applications – can make it easy for non-technical people to analyze and interpret data correctly.
Data Democratization is expected to give rise to new business models, help uncover untapped opportunities, and transform the way businesses make data-driven decisions. You don’t want to overlook this!
Not Forming a Single View of Customer Data
With organizations using the multichannel customer service approach, customers have the option of using a number of two-way channels to communicate with brands. These typically include email, phone, live chat, social media, online forms, and so on. It, therefore, becomes difficult for customer service teams to unify customer data received from these sources for analysis and interpretation.
Enter Single Customer View (SCV).
SCV enables organizations to track customers and their messages across channels, which in turn, helps with:
Unifying customer data on enterprise-wide internal systems and using it meaningfully.
Capturing customer activity across channels and devices.
Using customer information to engage with them across touchpoints.
Enhancing sales figures and improving future customer interactions.
Improving customer retention and conversions, as well as enriching customer lifetime value.
United Airlines, upon merging with Continental Airlines in 2012, wanted to integrate the two companies’ websites. United also wanted to ensure that its analytics and marketing pixel tagging was accurate, and ultimately, work towards a single customer view across all channels. They unified tagging across all digital touchpoints, including mobile apps and kiosks.
United managed to combine all customer data, which left them with cleaner datasets, greater consistency across applications, and the elimination of inefficient data silos. They also achieved higher ROI, as well as enhanced analytics and optimization programs that unified customer data and enabled greater mobile marketing agility.
Creating SCV isn’t easy. Some major barriers include:
Legacy systems that deter data integration and standardization.
Outdated, redundant data that lacks quality and accuracy.
Operational and departmental silos that prevent the delivery of seamless customer experiences.
Mentioned below are a few steps organizations can take to overcome these barriers and form a single customer view.
Employ customer journey analytics: This empowers organizations to skim through innumerable complete customer journeys and connect several touchpoints across channels and timelines.
Integrate customer data: This refers to putting together all customer data from different touchpoints – such as data warehouses, POS systems, marketing automation programs, and other data management systems. Customer data includes demographics, web and mobile activities, preferences, sentiments, interactions with customer support teams, social media, transactions, and so on.
Connect data with specific people for customer identity matching: Identifiers that can isolate people who engaged in specific interactions include email address, credit card number, device code, transaction number, cookies, IP addresses, agent ID, salesforce ID, and more.
Empower Your CX Team: CX teams can benefit greatly from accessing real-time customer information to deliver exceptional experiences. Industries that receive unending customer queries (like banking and telecom) can use SCV to resolve them quickly, leading to enhanced customer satisfaction.
Employ customer journey analytics: This empowers organizations to skim through innumerable complete customer journeys and connect several touchpoints across channels and timelines.
Integrate customer data: This refers to putting together all customer data from different touchpoints – such as data warehouses, POS systems, marketing automation programs, and other data management systems. Customer data includes demographics, web and mobile activities, preferences, sentiments, interactions with customer support teams, social media, transactions, and so on.
Connect data with specific people for customer identity matching: Identifiers that can isolate people who engaged in specific interactions include email address, credit card number, device code, transaction number, cookies, IP addresses, agent ID, salesforce ID, and more.
Empower Your CX Team: CX teams can benefit greatly from accessing real-time customer information to deliver exceptional experiences. Industries that receive unending customer queries (like banking and telecom) can use SCV to resolve them quickly, leading to enhanced customer satisfaction.
Reserving Innovation Only for R&D
Frequent technological advancements and industry disruptions have necessitated digital transformation in organizations. This, in turn, has given rise to new opportunities for growth and exchange of innovative ideas that transcend the borders of the R&D department.
If organizations are to encourage an enterprise-wide culture of innovation, they need to redefine metrics and incentives accordingly. New ventures and initiatives cannot be evaluated with traditional metrics to measure success.
Most managers agree that taking calculated risk is crucial to innovation, but putting this thought into practice is easier said than done. Hence, the focus needs to be on encouraging teams to take smart risks. It helps to clearly define a “smart risk” for teams and departments to distinguish the areas where risk is encouraged (and where it isn’t).
Of course, taking smart risks in business involves using advanced data analytics, Internet of Things, images, annotations, RFID, telematics, audits, among others. Every team brings unique perspectives to the table, which can provide ideas and insights to solve business problems. These insights are at the heart of driving successful innovation.
Lack of Data Consolidation
If your data is in multiple silos, gaining actionable insights from it can be a mammoth task for your organization. More often than not, the lack of customer insight is the result of the inability to consolidate customer information across channels.
The biggest challenge here is the inconsistent collection of customer information in each channel. For example, a global hotel brand may have collected customer data in a bid to improve customer service. However, because the data was collected from various sources, it resulted in some serious inaccuracies and inconsistencies.
However, after consolidating each customer’s data in one place, hotel staff can provide them with enhanced services and experiences across properties. Staff can guide a yoga-aficionado guest with a list of local studios and class times; or simply stock the mini-bar with their guest’s preferred beverages. Such steps will result in improved customer satisfaction and increased customer lifetime value.
Challenges related to data consolidation can be mitigated by enhancing data collection methods, in terms of accuracy and consistency. This also applies to how and where the information is stored upon being collected.
Organizations will do well to use cloud-based data consolidation tools. These tools are especially designed to provide speed, security, scalability, and flexibility, regardless of the place or in the form in which your data exists. These systems ensure that complete and accurate datasets are available at your disposal at anytime from anywhere.
Not Measuring Success on a Customer Level
Modern organizations use multiple channels to connect with and engage customers, but struggle to derive actionable insights from all the available data. It is necessary that organizations gauge quantitative and qualitative data to arrive at measurable and countable answers, which can be converted into numbers and statistical data.
This, in turn, will help decipher customer motivations, indicate their preferences, and highlight the scope for improvement.
Advanced technologies – such as Artificial Intelligence, Machine Learning, Augmented Reality, and Blockchain – are being leveraged to engage customers and provide them with seamless, connected, and hassle-free experiences. These solutions can also measure customer satisfaction using quantitative and qualitative data, which can be gathered through questionnaires and surveys. Combining survey answers and hard data will present the most direct picture of customers’ experiences.
The most crucial elements of success with customer experiences when implementing these technologies are: putting data at the center of your customer experience and seamlessly merging the digital and the physical (i.e. merging data from in-store and online experiences).
It also helps to use data analytics to find meaningful success metrics like revenue per visit, average user duration/average user time on site, cost per acquisition (CPA), and cost per lead (CPL) for gaining real-time feedback. Looking through CRM and lead platforms and working out total conversions for a particular time period can prove helpful.
Once these aspects are taken care of, organizations should be able to find answers to their most burning questions.
Steps to avoid the slowdown of Insights-to-Action in your organization
Analyze Data with Business Analytics
Business Analytics helps collect and analyze historical data, then employs predictive analytics and generates visual reports in custom dashboards. Predictive modeling can forecast and prepare businesses for future requirements/obstacles.
Organizations can begin using business analytics by asking measurable, clear, and concise questions. This should be followed by setting realistic measurement priorities, and then collecting and organizing data. The next steps involve the analysis of trends, parallels, disparities, outliers, and finally, interpretation of results.
The primary advantage of harnessing Business Analytics is to decipher patterns in data to gain faster and more accurate insights. Doing so enables organizations to track and act immediately, as well as formulate better and more efficient strategies to drive desired business and customer outcomes.
Simplify the Complex with Data Visualization
In any organization, Data Analytics should not be the forte of only data analysts and data scientists. Other stakeholders must also be empowered to make sense of critical data. Proper, user-friendly Data Visualization is the answer when organizations want to process and translate large volumes of datasets into meaningful insights.
Organizations must realize that there is more to Data Visualization than displaying information in a particular format. It also enables the use of visual instructions that guide users to process the material easily, with business-critical insights prominently featured on the top of the visual hierarchy.
Data Visualization also empowers organizations to easily decipher hidden patterns and make sense of the bigger picture in the ocean of data. With more meaningful data at your disposal, you will see improved decision-making (and revenue growth), as well as customer satisfaction and failure-aversion strategies.
So, you need to make Data Visualization a key skill of all data scientists in your organization. The goal is to make every single insight and decision crystal clear for all stakeholders to absorb.
Use AI to Close the Gap
Traditionally, organizations resort to historical data, spreadsheets, and business tools to make sense of their data. However, with different variables coming into play and constraints to consider, doing so across multiple channels can become increasingly complex and error-prone.
By bringing AI into the mix, however, management of data has now become quicker and error-free. Organizations can easily analyze their performance across the value chain in real time. With AI-powered operations, businesses can predict elements such as risks and customer behavior, then devise strategies to improve performances and approaches.
AI makes it possible for data-driven organizations to compare performance and trends, as well as analyze every dataset to gain business insights. These can then be turned into actionable plans that enable businesses to optimize their approach to enhance ROI and better meet customer needs.
AI helps to close the gap between insight and action by increasing scale, speed, and efficiency. Organizations can close the gap by analyzing customer data to derive key information, plan how to implement it, then focus on key performance drivers. Once this is done, organizations must track the progress of their plan and manage risks. After this, the desired outcomes can be achieved.
Decision-making fueled by AI can be done proactively, as well as more efficiently and effectively. Business insights can be embedded into predictive models that enhance business outcomes way beyond what was thought possible with traditional approaches.
Conclusion
The process of transforming raw data into actionable insights can be daunting. However, doing so is crucial if you want to stay competent and remain ahead of the curve. To successfully lead data-driven initiatives, organizations must overcome the challenges of data accumulation, analysis, and action.
Integrating data sources and leveraging advanced technology for faster and more accurate analyses is imperative. The future belongs to organizations that are driven by data, and only the optimal extraction and application of insights can give rise to the finest business outcomes.
Running Windows workloads on-premises stops a company from swiftly adapting to shifting market needs. So, moving data and workloads to the cloud is vital for the digital transformation of a company. And resisting is like fighting gravity.
Increased attention on the migration of workloads has echoed in multiple reports, with one claiming that 62% of organizations have a migration and modernization strategy in place.
Your Windows workloads can be better in the cloud, specifically in Amazon Web Services (AWS). AWS is a broadly adopted cloud, offering over 200 fully featured services to businesses, improving their agility, efficiency, and innovation faster. Here in this blog, we’ll look at the primary advantages of hosting Windows workloads on AWS and why it’s a good idea for the fastest-growing startups, largest enterprises, and leading government agencies trying to improve their operations.
Major Benefits of Moving Windows Workloads to AWS
Business transformation is never easy. Ideally, migrating to the cloud is part of an organization’s adoption of a more modern, agile management strategy. Moving your Windows workloads to AWS makes your business operations more aligned. With modern infrastructure and cloud capabilities, your IT workforce can be freed up to focus on core tasks that are important for your company’s growth.
Let’s take a look at the benefits of having Windows workloads on AWS and how easy it is for you to get there.
Cost Reduction
One of the most evident benefits of transforming Windows workloads to AWS workloads is cost reduction. According to stats, running Windows workloads on AWS cuts the 5-year cost of operations by 56%. Businesses no longer have to worry about the price of developing and maintaining expensive infrastructure since AWS takes care of these costs.
Reduction in Downtime
Businesses that run Windows workloads on AWS notice a 98% reduction in downtime. Amazon provides a highly available and robust cloud infrastructure, as well as a variety of services and tools to minimize downtime. AWS-hosted apps can withstand traffic surges, disperse traffic over several instances, and remain operational in the case of an outage.
Increased Productivity
Various statistics prove that AWS increases business productivity. A Salesforce survey found that businesses that move to AWS cloud experience an average 26% improvement in their productivity. With the ability to access cloud-based software and services from anywhere, your business workforce can efficiently work remotely and collaborate more effectively. Additionally, AWS provides automated tools and services that help expedite processes and cut down on the time and labour required for manual tasks.
Better Security
Security is vital for a business, whether it has the in-house infrastructure or uses a managed Windows server. When it comes to delivering high security to your data, AWS ticks all the boxes by providing around 230 security, compliance, identity and access management, network security, and governance services, among many others. It also provides encryption across 116 distinct AWS services, five times more than other large cloud-based enterprise-level service providers.
Higher Availability
AWS cloud has 77 Availability Zones (AZ) spread across 24 locations. More than 350 Amazon EC2 instances are also present. Because of the high service availability, your AWS workloads are maintained continuously with minimal downtime. It was discovered in 2018 that AWS offers 7X higher uptime than the next-largest cloud provider. Businesses can ensure that their apps and services stay available to consumers without any risk of disruptions and possible revenue losses.
Easy Migration Process
AWS has helped thousands of businesses all over the world to adopt the cloud to move their Windows workloads. Migrating workloads to the AWS cloud platform is an easy process if done by experts. AWS Cloud Formation, which enables customers to build and manage AWS resources using code, and AWS Systems Manager, which streamlines hybrid cloud administration, are tools Amazon offers to assist businesses to optimize their Windows workloads on AWS without any challenge.
Accelerate Innovation- Move to Cloud with R Systems
Moving Windows workloads to the cloud will definitely accelerate your company’s innovation and growth. By hosting Windows workloads on AWS, businesses can achieve greater flexibility, scalability, and agility. R Systems has expert professionals to deliver bespoke cloud services to companies desiring to accelerate innovation with cloud-native technologies. We are an AWS Advanced Tier Services Partner offering solutions tailored to meet the specific needs of businesses of all sizes and industries.
This is What Banks Need Most to Be Transformational in the Digital Landscape
The Value of Advanced Analytics to Today’s Banking Industry can never be underestimated
So How Impactful is Advanced Analytics for Banks Worldwide?
Banks Can Claim Lost Revenue Avenues through their Improved Analytics Focus
Advanced Analytics is Imperative for Today’s Banking Success. Do You Agree?
What’s your perception of banking success?
Banks must transform to fit in well with the Evolving Digital Ecosystem and Advanced Analytics will help them get to it with ease and precision… Or else … they will be losing out on their market share and profitability!
This is What Banks Need Most to Be Transformational in the Digital Landscape
Today’s banking systems are getting more complex than ever. To overcome this complexity, banks must stay abreast of the best way to mitigate risks, enhance security systems, ensure regulatory compliance and meet customer needs effectively.
To launch the right products for the right customers in a secure, dynamic approach, banks must invest in certain frontiers that will pave their way towards success in the high-end digital future:
Make data work by enabling communication between disparate data formats that existed in the past and are the language of the future
Rely on people who possess the skills to derive insights from data. Empower them with the analytics and communication tools for collaborative decision making and meaningful information discovery
Form correlations between data and visualization of patterns and relations, as it is critical to advanced, transformational business planning
The Value of Advanced Analytics to Today’s Banking Industry can never be underestimated
In the end, it’s all about innovation and precision risk assessment, which will directly impact your financial bottom line. To expand your opportunities and be transformational while reducing costs, there is no better way to differentiate and charge through your competition rather than by driving decision making through analytics. Advanced analytics is an indispensable tool for generating sales leads, carrying out risk management or revenue management. Not only does analytics redefine core functions, but it an essential tool when it comes to marketing, budgeting and planning your business in general.
So How Impactful is Advanced Analytics for Banks Worldwide?
By the year 2020, close to 40 trillion gigabytes of data is expected to be generated, be it tweets, Skype calls, YouTube videos or emails.Sifting through this data and listening is imperative to realize important insights and come up with targeted strategies for customer acquisition and retention. It helps banks accomplish accurate reporting and ensure regulatory compliance and project their system as profitable and competitive.
Clearly, this is not as easy as running queries on a database. It requires the use of advanced analytics – to address the variability and volume of available data.
Precision Analytics can help calculate risks.
Banks must find a way to manage risks, given the broad spectrum and depth of investments they engage in.Analytics in banking is hardly limited to the financial domain. Data pertaining to many areas, from their target market to the viability of their securities can be instrumental in determining, whether their investment would be worthwhile or not. Besides, it helps deliver better services to customers through their financial need analysis.
Trends Can Unravel Important Data for Effective Future Planning:
Analytics can be the source of determining key performance indicators and reporting can be an important source of responding to customer demand and strategic planning for the future.Visualization of critical data, customizability in extracting selective data sets and historical data analysis cannot be accomplished without analytics. Eventually, banks must remain competitive, and the two main factors that directly impact their market position – compliance to regulations and compliance to customer requirements. Both of which are entirely dependent on deep analytics.
Precisely, 96% bankers acknowledge that the banking world is witnessing the organization of a digital ecosystem. However, the downside is that 87% of the surveyed banks admit that their systems are not smart enough to flow with the digital tide.
Banks are losing out by maintaining a status quo and incrementally upgrading their analytics strategy to address a current need. Partnering and collaboration in conjunction with “agile, scalable systems” and “real-time data analytics” are the door to a successful, thriving banking business in the digital ecosystem.
Banks Can Claim Lost Revenue Avenues through their Improved Analytics Focus
Analytics directly impacts a bank’s market domination. It is rather critical for banks to change priorities and analytics approach and match their market position to currently prevailing trends.
The Banking Top 10 Trends 2016 report sheds further light on this aspect. Charging optimally for every service delivery is critical and suboptimal or overpricing is commonplace without the use of advanced analytics.A pricing decision which is not based on analytics will create the means to give away appreciable portions of their revenue pie to players even outside of their domain. Eventually, banks become less informed about their customer expectations and therefore less profitable.
In addition to becoming agile and adopting a service-oriented architecture (SOA), Advanced Analytics is one of the critical trends for banking success. It is a key factor that helps drive customer insights, curtail fraudulent activity and manage risks better.Banks need the intelligence that helps frame effective path-breaking strategies. Banks can take advantage of a number of analytics realms in prediction, visualization, simulation or optimization to address their specific business architecture needs and strategic requirements.
Advanced Analytics is Imperative for Today’s Banking Success. Do You Agree?
Banks must ensure that their digital strategy is not limiting to make the most out of data discovery from Advanced Analytics. Legacy infrastructure and the inability for effective data communication produce great obstacles.
The inability to address this and other surrounding constraints prevents banks from successfully breaking into the digital.
Banks will be able to understand customers better, retain customers, acquire new customers and reduce attrition through their improved analytics focus.
Better analytics helps deliver targeted products and services, convert and serve customers better and market themselves better.
At the core, it helps drive better decisions and best in the market opportunities.
All this translates into better profitability and a drastic upsurge in the financial bottom line.
What’s your perception of banking success?
Is Advanced Analytics the answer to profitability woes in the banking sector in today’s disruptive digital dimension?
Share your views on social media and let other’s get a peek at the banking success factors!
Lingering around the start line- Deciding on what, when and how to use the Big Data?
The Big Difficulties of Big Data Analysis
Analysis of data and implementation of findings is what matters
Data Analytics is the science of examining, concluding and implementing the useful data for organization’s growth. In today’s connected world, data is available everywhere. Travis Oliphant, CEO of data analytics firm Continuum Analytics, suggests data is more available now than ever, with “people connecting through the Internet, their mobiles, social media, business partnerships and personal friendships and associations.”
Globally, 4.6 billion mobile subscriptions and around 1 to 2 billion people are accessing the Internet on a daily basis; therefore, the potential for data collection is enormous.
The structured and unstructured data are enormously available, but they are seldom used by organizations to be benefitted in annual growth. The big data is continuously used by technology industry for strategizing annual goal.
Why is Data Analysis Useful to Your Business?
“Something is always better than nothing.” – To weave a strategy for growth of the business, the data availability is always a basic requirement. The voluminous data give the clear structure to carve-out the plan to cover the deficient areas in the business. Data Analysis can help give you not only an insight into your customer’s habits, preferences, and behaviors but can also be applied to help your business grow. For example, if launching a new product, analysis of current customer behaviors can help identify a need for your product, potential future customers, how to market to these customers and how to retain these customers.
Already well established, with over 89% of US businesses saying they use data analytics, data analysis has been adopted by many industries across the globe including:
National Governments – In 2012, US Government announced the Big Data Research and development initiative to examine specific issues within government. At present, there are 84 programs.
Healthcare Sector – In the UK, data analysis of prescription drugs showed a significant discrepancy in the release of new drugs and the nationwide adoption of these treatments.
Elections – In India, the BJP winning campaign for the General elections in 2014, relied heavily on big data analysis.
Media – Relies completely on big data to fetch precise information, specifically where figures play a significant role. Media dominates the market by presenting the data as a secure and inevitable witness.
Science – Science and technology are correlated and share especial configuration. The huge amounts of data produced during experiments such as the Large Hadron Collider are analyzed using data analysis. The systematic data analysis cut shorts the risks engrossed.
Sports – Sports sensors are used to assess athletes and sportsmen’s condition, guide training and even predict injury. The sports related data analytics is required to be precise.
Collecting data is not the issue, in their video, Big data what’s your plan? McKinsey suggests that companies struggle with data analysis in three key areas:
Which data to use and where to source it?
Analysis of the data, plus sourcing the right technology and people to carry out that analysis,
Implementation of the analysis findings to change your business.
So let’s start with number one…
Lingering around the start line- Deciding on what, when and how to use the Big Data?
Data is now more accessible than ever. To improve the efficiency and other services, every organization collects the related information; however, very few analyze this data to implement in the direction of improvement or change.
Data trends can highlight success, identify problems and help provide alternative ways of working. And while most businesses know that data analysis can make them more efficient, productive and even help predict future market trends, it is scarcely used to its full potential. So why aren’t more people using data analysis?
The Big Difficulties of Big Data Analysis
Due to the large volume of structured and unstructured data, it often becomes difficult to manage and procure the relevant information from them. On the other hand, the traditional data analysis, which constitutes difficult methods become too wary to analyze. Traditionally, companies use to visualize datasets in programs such as Microsoft Excel which has a great capacity for simple datasets or employ a free tool such as Qlikview, but with Big Data things change.
With over $15 billion spent solely on companies focusing on data management and analysis, companies are forced to employ data analyst or data scientist specifically for data analytics. In 2010, the industry was estimated to worth more than $100 billion and predicted to grow at approximately 10 % a year. So big data is big business.
Analysis of data and implementation of findings is what matters
To apply data analytics to your business first you need a plan or strategy. For example, if you want to improve your company’s effectiveness and efficiency, it is important to manage performance. To manage performance, you need to measure it. But the measures of performance you take need to be meaningful, and link to the desired outcome or goal.
Therefore, the idea to employ a data analyst and specific software, to collate data and develop a plan of how to implement the required changes is quite synchronized.
Ready to trap the Big Data?
Using Data analytics provides potent information which can be used to achieve high merits of success and tangible solutions with great accuracy. It is not only great for your business, but data analysis can also identify customer preferences and behaviors, allowing you to personalize your products and business to your customers.
In today’s connected world, data analytics is becoming vital for businesses who want to gain a competitive edge over others. And with the increasing amount of data available, never before have you had so much access to what your target market wants and needs.
So get out there and see how data analysis can change and improve your business, you might just be wonder why you haven’t exploited data analytics potential before.
Robotic Process Automation (RPA) has become a hot topic for organizations in the last few years. These days, many organizations are embracing RPA to automate their repetitive, high-volume tasks and cut headcounts. Though it serves as a useful tool to optimize business processes, but when used in isolation, it’s more likely to disrupt the processes than improve them.
How to make RPA work for you
One of the biggest barriers to RPA is identifying the right process to automate as automating the wrong process can magnify inefficiency. This is where Process Mining gets into the picture. It is an approach that aims to discover, analyze, monitor, and improve business processes by extracting valuable information from the data to remove bottlenecks and inefficiencies.
While there is wide acceptance of the fact that Robotic Process Automation (RPA) and process mining augment each other, many companies have not been successful in putting both technologies to good use in their businesses very effectively.
Challenges enterprises face scaling their automation program
Companies struggle to scale their automation program at an enterprise level for various reasons. Keeping the organizational dynamics aside, many businesses find it overwhelming to analyze enterprise-wide processes and identify the right candidates for automation.
The next challenge firms encounter comes with understanding the processes and estimating the associated benefits and costs to achieve the desired ROI by prioritizing high-value, low-effort opportunities. Studies have shown that 40-50% of the Bot Development Lifecycle is spent on identifying, prioritizing, and documenting the processes, with the rest of the time split among bot design, coding, review, unit testing, integrated testing, UAT, pre-deployment configuration, and deployment activities.
The role of process mining in achieving process excellence
Process mining gives a business a complete picture of their state of processes ‘as-is’, which in turn can be used by our RPA team to turn into actionable automation. Process mining helps you highlight the best automation candidates, enabling you to determine the extent to which RPA can be implemented in legacy processes and systems.
Additionally, process mining tools often provide a capability of executing business-rule-driven automated actions, but they are generally limited in terms of the type of actions such as sending emails, pushing a report, or alerting business users for further actions. Using these process mining tool actions to kick off RPA bots gives you unlimited power of end-to-end automation.
While RPA tools allow you to measure post-automation indicators of accuracy and productivity, process mining software provide pre-automation historical values, as well as the upstream and downstream impact of automation.
Maximizing benefits by using RPA and process mining together
RPA bots generate detailed logs of each and every data element that they touch or use in decision making. Process mining software can benefit from such detailed logs to provide greater visibility into the process performance. Thus, these technologies truly complement each other to further your business goals. A recent Gartner report on Complemented RPA (CoRPA) even mentioned that: “A significantly improved version of the current RPA development tool known as the process recorder, that has UI interaction record and playback capabilities, will dynamically generate the RPA script based on lessons from process mining and process discovery.”
Recognizing the multiplier effect of combining these two powerful concepts, one major RPA tool provider, UiPath, acquired process miner ProcessGold in 2019. Major process mining tool providers, like Celonis and Minit, also boast their capabilities to augment the power of automation. In addition, Nintex (Process Mapping and Analytics company) bought Foxtrot (RPA company) in 2019, and Appian (Process Management company) purchased Jidoka (RPA company) in 2020 to leverage the power of both technologies. Thus, it is becoming evident that Process Automation projects are more likely to succeed with the addition of process mining.
Robotic process automation (or RPA) is a form of business process automation technology based on metaphorical software robots (bots) or digital workers. RPA systems uses application’s graphical user interface (GUI) to perform manual tasks directly in the GUI.
Process mining is a family of techniques in the field of process management that support the analysis of business processes based on event logs. During process mining, specialized data mining algorithms are applied to event log data in order to identify trends, patterns and details contained in event logs recorded by an information system. Process mining aims to improve process efficiency and understanding of processes. The term Process Mining is used in a broader setting to refer not only to techniques for discovering process models, but also techniques for business process conformance and performance analysis based on event logs.
