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Data platform challenges with data processing and analysis are many. Today, data engineers are using the Domain-Driven Design (DDD) through the Distributed Data Mesh architecture to help overcome these obstacles. The Data Mesh approach includes four important pillars: domain-driven data ownership, data as a product, self-serve infrastructure as a platform, and federated computational governance.

It’s crucial to thoroughly understand these components of Data Mesh before implementing them into an organization. Learn about Distributed Data Mesh, the current analytical data platform challenges, and how Data Mesh can overcome them by creating cloud and on-premise solutions through data storage, self-serve tooling, and more.

This paper addresses specific building blocks for approaching a data platform journey for architecting. By understanding the essential elements of architectural design considerations, organizations can aid their decision-making process when evaluating value realization outcomes associated with modern data platform goals.

Read more to learn how to apply these proven methodologies to improve at-scale benchmarking, cost modeling, and operational efficiency. Building and operating a data platform has never been more accessible with the right tools, modern technology, and efficient workflows.

An Unforgettable Experience at Adaptive Spirit 2022

What does it mean to adapt? In business, we have unlimited examples from the past two years. For the U.S National Paralympic Ski and Snowboard Team, they have a lifetime of exceptional adaptations. I recently had the opportunity to meet some of the athletes firsthand—fresh from their stellar performance at the 2022 Winter Paralympics in Beijing. I heard their stories of resilience and adaptability; I witnessed their athleticism that knew no mountain was unsurpassable, and I left with a deep appreciation and respect for them as well as for the telecom industry who makes this event possible—every year for 26 years now!

Adaptive Spirit, held annually in Vail, Colorado, is a rare combination of networking, giving, and business; it’s the premier networking event for Telecom which also raises funds that allow the U.S. Paralympians to remain the top adaptive ski team in the world. In short, it’s good for business and good for the Olympians!

GlobalLogic was honored to be a Silver Sponsor at the 26th annual Adaptive Spirit event. Yes, I connected with customers and vendors throughout the interactive agenda. Indeed, I built business relationships. But most importantly, we gave back. Over the last 25+ years, Adaptive Spirit, a non-profit trade association, has raised millions of dollars for these athletes. This was GlobalLogic’s—and my—first time attending the event.

Getting to Know the Athletes Who Had Just Returned from Beijing

The timing of the event could not have been more perfect. The Paralympic Ski and Snowboard Team had just returned from Beijing and the 2022 Winter Paralympics with a host of medals. As part of the three-day event agenda, we got to interact with them socially and on the slopes. I can personally say now that I have met and mingled with a gold medalist!

Everyone was thrilled as the athletes showcased their amazing talents throughout the weekend. On Friday, they offered pointers and encouragement during a race clinic, followed by a Youth Race for kids 15 and younger who were able to ski alongside the Olympic athletes. And Saturday was Race Day down the Golden Peak Race Arena course.

Interacting with the athletes was definitely the high point of the event and hearing their personal stories of overcoming daunting odds was truly inspiring. For example, the story of Oksana Masters is one of adaptation and resilience. She was born in Ukraine after the Chernobyl nuclear disaster with several radiation-induced birth defects. She was adopted by an American speech therapy professor and began training for competitive sports at the age of 13, winning medals for rowing, cross-country skiing, and cycling at multiple Paralympic events. She brought home three Gold medals and four Silver medals from the 2022 Winter Paralympics!

Masters’ teammates have similarly motivating stories, such as Aaron Pike, who was shot in a hunting accident at the age of 13 and still competes in cross-country skiing. He recently finished second at the 2022 Boston Marathon. Another such story is Ian Jansing who was born with cerebral palsy but went on to become a ski racer and competitor at various Paralympic Games and championships.

Image from L to R: Ed Clark, AVP Client Engagement, GlobalLogic | Ian Jansing, USA Paralympic Athlete | Maneesh Muralidhar, AVP, Client Engagement, GlobalLogic

Image from L to R: Oksana Masters, USA Paralympic Athlete | Poorvi Tikoo, Freshman, Hopkinton High School | Sameer Tikoo, SVP, Communication Services BU, GlobalLogic

Image from L to R: Poorvi Tikoo, Freshman, Hopkinton High School | Aaron Pike, USA Paralympic Athlete | Sameer Tikoo, SVP, Communication Services BU, GlobalLogic

A Movement That Matters—Join Us

The Adaptive Spirit Annual Event lived up to its promise. On the business front, we had the opportunity to network with the world’s leading communication service providers (CSPs) and network equipment providers (NEPs). We were able to share our over twenty years of experience in Communications and our portfolio of cutting-edge solutions in the telecom industry using 5G, IoT, AI/ML and more.

But most importantly? We were able to recognize, reward, and support individuals who didn’t allow challenging circumstances to stop them from fulfilling their dreams, and, instead, used these circumstances to inspire others.

Adaptive Spirit is the perfect complement to our Corporate Social Responsibility (CRS) program, The GlobalLogic Foundation, where we focus on Education, Environment, Health & Wellbeing, and Community Service. We also invest in Human Capital, cultivating a multi-faceted culture with DEI at the forefront of our efforts. In fact, our CEO, Shashank Samant, signed the CEO Action of Diversity & Inclusion Pledge, joining over 2000 CEOs committed to advancing diversity and inclusion in the workplace.

As we work towards making a long-term, positive impact across the globe, we look forward to attending and sponsoring the Adaptive Spirit Annual Event for years to come. Co-chair and founding member Steve Raymond told Light Reading that Adaptive Spirit’s success has taken the “work of countless people who felt passionate about the Paralympic movement,” and we encourage other businesses to join us at next year’s event. Donations can also be made to the U.S. Paralympics Ski and Snowboard Team.

We’re excited to be a part of this movement that matters. To learn more about what GlobalLogic stands for and about our consulting and software engineering partner services, contact us today.

Together, we’ll build the exceptional.

In part 1 of this blog series, we looked at the data and analytics evolution across data platforms, data processing technologies, and data architecture. Here in part 2, we’ll take a look at the evolution of the data and analytics space across application development and storage aspects.

Data Application Development Evolution

 

Programming based → Scripting → SQL like → Low/No Code UI

 

Initially, data engineers used programming languages like Java to develop most of the data applications on initial big data ecosystem projects like Apache Hadoop. This was because these frameworks provided interfaces to create and deploy data applications using the Java or Scala programming language.

Soon after, data engineers and analysts could easily use custom scripting languages like Apache Pig for Hadoop or Scalding for Cascading to develop jobs in a more user-friendly way without writing programs in the underlying language.

Due to the widespread use of SQL amongst the data analyst and data scientist communities, SQL and SQL-like frameworks such as Apache Hive for Hadoop, CQL for Cassandra, and Apache Phoenix for HBase became prominent and continue to be widely used by data engineers and data analysts alike. 

Currently, with a shortage of data engineers and analysts, enterprises are increasingly looking at user interface based development that can reduce the implementation complexity and improve productivity. Therefore, the trend for the future is to move towards low code or no-code user interface based applications like AWS Glue, Azure Data Factory, Prophecy.ai, and GlobalLogic Data Platform that minimizes the learning curve for data engineers and accelerates the development for enterprises.

Data Formats Evolution

 

Text / Binary Formats → Custom Formats → Columnar Formats → In Memory Columnar & High Performance Formats

 

In the beginning, analysts stored most of the data in the Hadoop Distributed File System (HDFS) as text files or in binary formats like SequenceFile or RCFile. While some formats like text and JSON are readable to the bare eye, they consume a lot of storage space and are not performance friendly for large volumes of data.

Subsequently, engineers developed many open-source data serialization formats like Apache Avro and Google Protobuf to serialize structured data. They provide rich data structures and a compact, fast binary data. These formats continue to be used frequently for storing data.

Then engineers developed columnar formats like Apache ORC, Apache Parquet, Delta, and Apache Hudi that support better data compression and schema evolution handling. The columnar formats like ORC, Delta, and Hudi can also support ACID transactions to handle data updates and change streams. 

The columnar data formats and storage systems are already the most used across enterprises. The trend for the future will be to use in-memory columnar formats like Apache Arrow or high-performance formats like Apache Iceberg or Apache CarbonData that provide efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. Internally, these formats still use ORC or Parquet to store the data making them compatible with the existing data stored.

Data Storage Evolution

 

HDFS → Hive → NoSQL / NewSQL → Cloud Data Warehouses + Blob Storage

 

HDFS was the initial distributed file-based storage system that allowed engineers to store large amounts of data on top of community hardware infrastructure. For example, engineers run the MapReduce programs on top of the files stored in HDFS. 

Apache Hive and HBase frameworks followed this development, providing a table-like view of the underlying data and allowing developers to run SQL-like queries on the underlying data. 

Soon after, several NoSQL databases were developed with different characteristics like wide-column, key-value store, document store, graph database, etc., to support specific use cases. Some popular NoSQL databases include Apache Cassandra, MongoDB, Apache CouchDB, Neo4J, Memcached in open source and Amazon DynamoDB, Azure CosmosDB, and Google Cloud BigTable, among commercial versions. 

During this period, engineers introduced an integration of traditional RDBMS with NoSQL as NewSQL that seeks to provide the scalability of NoSQL systems for online transaction processing (OLTP) workloads while maintaining the ACID guarantees. Some NewSQL databases include Amazon Aurora, Google Cloud Spanner, CockroachDB, and Yugabyte DB, among others. 

Most of the cloud storage is HDFS-compliant, and together with the serverless nature of this storage, enterprises are increasingly using them as the blob storage systems. Therefore, the trend for the near future will be to use cloud blob storage like Amazon S3, Azure Blob Storage/ ADLS, and Google Cloud Storage as the landing zone for ingesting data. The data will then be processed and aggregated data will be persisted in Cloud data warehouses such as Amazon Redshift, Azure Synapse SQL Data warehouse, Google Cloud BigQuery, Snowflake, or Databricks DeltaLake. 

Engineers will continue to use the NoSQL databases for specific data use cases as applicable.

This concludes the second part of this blog series. We’ll continue to explore the evolution of the data and analytics space in subsequent blog posts in this series in the coming months. 

Introduction

A data platform is one of many parts of an enterprise city map. Even though it’s not the only platform, it’s a significant piece of an enterprise city map that helps teams meet different business objectives and overcome challenges.

When dealing with a data platform, finding the hidden meaning, relationships, and embedded knowledge can still be challenging when attempting to realize the data’s value.

Handling big data or real-time unstructured data presents challenges across collection, scalability, processing, management, data fragmentation, and data quality.

A data platform helps enterprises move information up the value chain by helping lay the foundation for powerful insights. Not only does a data platform pull data from external and internal sources, but it also helps to process, store, and curate the data so that teams can leverage the knowledge to make decisions.

The central aspect of leveraging a data platform is to consider it as a horizontal enterprise capability. Teams across the organization can use the data platform as a centralized location to aggregate data and find insights for specific use cases.

On its own, a data platform cannot realize its full potential. Are you setting it up for maximum impact?

While the goal of a data platform is to remove silos in an organization, it is difficult to do so until the organization enables a complete data platform. Then different units can leverage the platform functions so departments will have easy data sharing capabilities.

In this post, we discuss the principles that help ensure teams can optimize their data platform for use across the enterprise.

At GlobalLogic, we refer to these principles as the ‘Synthesize and Syncretize Paradigm’ for implementing data platforms.

These principles help weave together composability aspects into the data platform and lakehouse architectures. Additionally, it utilizes data mesh and data fabric principles with appropriate governance. This paradigm allows the implementation of a 360-degree data platform with enablers for easier adoption and uses across the enterprise as it facilitates the synthesis of platform components for syncretic use.

Principles

Enterprise Data Platform as the Core Foundation

The core data platform will form the foundation and own all the capabilities and technology stack to enable the following:

  • Data storage
  • Data ingestion interfaces for ingesting data into the storage layer
  • Data processing during the ingestion and post-ingestion phases to transform and enrich the data
  • Data access interfaces
  • Endpoints for data ingress and data egress
  • Orchestration and scheduling
  • Data governance and data cataloging
  • Control pane, monitoring, and security
  • Data querying and data analytics

Teams will need to enable continuous delivery of new data platform features with centralized governance.

The Interplay of Domains & Data Products

Domains must be first-class concepts in the entire setup.

Teams can link domains to business aspects, data origin, use cases, source data, or consumption. Additionally, teams can enable particular feature sets within domain systems depending on the need.

Domains will vary from organization to organization since businesses closely tie domains to their organization’s structure and design.

The core data platform foundation must be compatible with data products and domains. Teams can build their own data products for a domain on top of the core data platform foundation. Teams can also deliver data products in an agile fashion for incremental business value realization.

Microservices Based Architecture

The core data platform foundation will have a decentralized microservice architecture. This architecture provides API, messaging, microservices, and containerization capabilities for operationalizing data platform features.

The decentralized microservice architecture will enable the enterprise data platform so teams can use it as a central base with a decoupled architecture.

A team can leverage these capabilities to ensure the platform is resilient, elastic, loosely coupled, flexible, and scalable.

This will allow different domain teams to operationalize the data and features across the enterprise for their feature sets.

They also enable data and decision products in a domain on top of the unified data platform to access reliable data ubiquitously and securely.

Composability

The ability for teams to select the tools and services in a frictionless manner for their data products within a domain is crucial since it allows teams to assemble the required components. In addition, a composable architecture will enable teams to fabricate the necessary elements to deliver data and decision products.

This architecture paradigm will utilize both the infrastructure aspects as well as microservices.

A microservices-powered composable architecture for infrastructure, services, and CI/CD processes will allow separate teams and domains to utilize the same data platform infrastructure stack. The key to delivering a composable architecture is when the team focuses on DevOps and automation practices.

This will also enable dynamic provisioning with the definition of scalability parameters during the provisioning process itself.

Self Serve Data Platform Infrastructure

Teams should be able to use the data platform technology stack, features, and infrastructure. Teams can use a “No Code” or a “Low Code” approach with portals and self-service capabilities to enable these functions.

This principle will help teams reduce difficulties and friction when using and provisioning their environment. This will also help teams leverage the data platform to become a first-class asset across the enterprise and become the source of accurate data.

Discoverability & Data Sharing

Discovering and utilizing the platform and data assets elements is crucial to enable ease of synthesizing the right set of necessary components.

Data management is essential to catalog and manage data assets and datasets. Another important component is automation. It’s crucial to use automation for auto-discovering, tagging, cataloging and profiling data, and data classification with relationship inferences. This will enable teams to discover and utilize data assets efficiently.

Similarly, another key to discovering the capabilities is a catalog of available platform elements and features. This can cover the data connectors, existing data pipelines, services, interfaces, and usage guides.

The data platform also needs to have mechanisms for data exchange to ensure teams can effortlessly share data with appropriate access controls applied.

Centralized Governance

Centralized governance is a pillar to enable interoperability between various domains and teams and their data products. It will also ensure proper controls on new data platform features development and operationalization based on the actual needs of the teams so that they can quickly realize business value. This will act in conjunction with the data governance processes, data stewardship, and data management to ensure teams can access and share datasets in a controlled manner.

360-Degree Data Platform to power business with GlobalLogic

A data platform that leverages the above principles enables frictionless platform use and thereby accelerates utilization of the platform capabilities across an organization and value realization.

At GlobalLogic, we help our partners implement end-to-end modern data platforms with our big data and analytics services. Reach out to the Big Data and Analytics team at practice-bigdataanalytics-org@globallogic.com – let’s explore your data platform implementation options and how to drive the adoption of data platforms across your organization.

Sports betting and online gaming companies are racing to offer the best customer experience. The stakes are high, and they have to build a secure, compliant, and reliable platform and mobile apps soon! And working with a matured software engineering partner like GlobalLogic determines who wins the race! With its robust reference architecture and leveraging the power of digital technologies, GlobalLogic can deliver engaging and data-driven experiences. Download the ebook to learn more.

The banking, financial services and insurance (BFSI) sectors are customer-service driven, document-reliant, and compliance-focused. You know the ongoing challenges. Time-consuming, repetitive data entry tasks across multiple platforms can lead to human error, processing delays, and lost opportunities to personalize marketing and cross-sell products. 

Digital transformation fueled by cloud-based technology is changing the game. Artificial intelligence (AI), natural language processing (NLP), machine learning, optical character recognition (OCR), and intelligent automation are reshaping the future of the financial services industry. Here’s how.

Advantages of Digital Transformation in BFSI

A study by Allied Market Research determined the global digital transformation in BFSI market was valued at $52.44 billion in 2019 and is projected to reach $164.08 billion by 2027. Among the factors driving the transformation have been the widespread use of mobile devices, developments in the Internet of Things (IoT), and cloud technology. 

Intelligent automation including AI, NLP, machine learning and OCR backed by cloud technology can:

  • identify new revenue streams through technology
  • attract (and retain) customers through seamless omnichannel experiences
  • improve decision-making through powerful data analytics
  • mitigate risks through fraud detection and regulatory compliance solutions.

Increased Data Handling Capacity in the Cloud

One of the challenges BFSI encounters is the documentation required in day-to-day financial operations. Much of the required information is on paper, in emails or faxes, or on photocopies or even carbon copies that deteriorate over time. In addition, documentation takes a great deal of storage, is not easily searchable, and can lead to delays, errors, and missed opportunities for cross-selling and personalized customer experiences.

Enter intelligent automation. OCR can digitize data from a variety of sources, including faxes, paper, email and notes, making it accessible and searchable. Machine learning and artificial intelligence can “learn” a financial institution’s systems, identifying and flagging areas of weakness or areas of concern. Documentation stored in the cloud is quickly retrievable, yet takes a fraction of physical storage space.

AI and machine learning can scan, analyze, sort, distribute and file documentation, it can flag discrepancies or missing information, send notifications, follow up for information or escalate, and perform the repetitious data entry, which frees the employees to do higher-value work, such as customer retention or investigating more complex issues or problems. 

AI can scan for customer profiles across omnichannel quickly, and flag potential duplicates or fraudulent accounts. OCR and machine learning can detect anomalies in photo identification and flag for investigation in real-time and can research multiple accounts simultaneously. This level of compliance can provide additional security and protection.

Augmented Customer Experience & Support

Robotic process automation (RPA) can employ intelligent automation and natural language processing to provide an enhanced customer experience. For example, Odigo is a world-leading Contact Center as Service (CCaaS) provider that handles 3 billion customer interactions per year. They have partnered with Global Logics to expand their product’s capabilities. 

One advantage of CCaaS is the ability for companies to only purchase the technology they require, to handle customer service inquiries, chat, email and social media, and other messaging using intelligent chatbots and natural language processing. AI with NLP can escalate to an employee at any point during the interaction, and machine learning means the bots “learn” through interactions, providing more complete and robust information to inquiries based on previous interactions. 

AI can input a customer profile, search for other customer accounts across multiple systems, request a welcome letter or package, confirm identification based on compliance protocols, complete Know-Your-Client (KYC) information, and begin to search for personalized recommendations based on information. 

AI and NLP can provide customer service in the customer’s language of choice, in multiple time zones simultaneously, and can scale quickly to meet increased demand or need. AI can operate 24/7/365, providing an enhanced customer service experience with access to financial services on the customer’s schedule, rather than during traditional banking hours. 

Security & Blockchain Applications in BFSI

Cloud technology provides enhanced business continuity, mitigation of risk and cybersecurity measures. More transactions are being conducted digitally using the IoT – for example, insurance packages can now be customized using a vehicle’s telemetry data. As more of these transactions and processing happen at the edge, the need for more secure hardware and data transmission increases. 

Security access protocols such as multifactor authentication, robust identity access management protocols, continuous monitoring, and encryption can allow for secure transmission between data warehouse/analytics in the cloud and processing at the edge. AI can retrieve information from cloud technology in a fraction of the time it takes a human employee to cross-reference and search information, providing enhanced fraud detection and cybersecurity measures.

Security is only as strong as its weakest component, so it is essential for BFSI to invest in secure hardware and employ multiple encryption and security protocols. Cybersecurity in BFSI is becoming more challenging as cyber-attacks become more sophisticated. One of the ways that the financial sector can protect cloud transactions is to combine AI with blockchain applications. 

Blockchain provides a transparent real-time chronology of transactions using a decentralized public ledger. As each transaction creates a block, every person in the network receives a copy of the ledger. This makes alterations difficult and provides a complete audit trail of each transaction. 

Money transfers, direct payments, transaction tracking, and fraud reduction can be completed quickly using blockchain, as the transaction can be monitored by all parties every step of the way, and blockchain encryption provides an extra layer of security. Blockchain can reduce costs and provide enhanced transparency, an enhanced audit trail, and accountability.

Algorithmic Trading

Machine learning and AI can monitor and track trade volumes, analyze historical trade data, and then use the information to formulate recommendations for future investment strategies. In addition, AI can automatically execute a trade based on preset buy/sell/hold instructions which will be triggered when criteria such as time, price, volume or call and put option instructions. 

As trading volumes increase and client expectations become more complex, the pressure on trading desks to improve execution performance is steadily increasing. Machine learning enables algorithms to “learn” how to make different decisions and consider myriad data points to make smarter trades. Core trading algorithms will become increasingly intelligent and complex, evolving into a sort of contextual playbook versus a strict set of rules.

Final Thoughts

Financial services firms are now using machine learning to predict cash flow events, fine-tune credit scores, and detect fraud, among other important functions. This refactoring of the financial services industry, being driven by advancements in technology and rapidly evolving customer expectations, will propel businesses that are positioned to capitalize on the opportunities to the next level.

With 15+ years in BFSI, including 1200 dedicated engineers and expertise in regulatory compliance and control, Global Logic is helping its partners reshape their businesses – and the industry as a whole. How can we help you embrace these digital trends and transform your business? Get in touch and let’s find out.

Over the years, digital coupons have become more popular with customers and throughout enterprise marketing strategies. Companies can distribute coupons throughout their website, apps, and social media to promote discounts and create opportunities to maximize their revenue.

Additionally, companies utilize third-party services with blockchain, distributed ledger technology, and smart contracts to minimize coupon cost management and distribution.

There are numerous advantages and use cases for companies to utilize blockchain technology and platforms for coupon campaigns. Learn about the impactful ways to incorporate blockchain into your coupon marketing strategy and the critical components behind it. 

The last two years have upended the global marketplace and the manufacturing sector is no exception. As the global pandemic rolled across the world, plants shut down, supply chains were disrupted, and widespread socio-economic instability ensued. In an effort to minimize future disruptions, mend operational inefficiencies revealed by the pandemic, and get ahead of changing consumer expectations, the manufacturing sector is undergoing a digital transformation. Welcome to the era of Industry 4.0.

Digital transformation is driving big gains for businesses by improving operational efficiency, reducing costs, improving product quality, and enabling quicker responses to evolving market requirements and customer demands. There are benefits as far as eco-positivity via reduced energy, lower material consumption, proactive monitoring, etc. as well.

These innovations mean we’re now seeing robotic processing automation, artificial intelligence, machine learning, augmented reality (AR) and virtual reality (VR) all working together in the Industrial Internet of Things (IIoT) to provide manufacturers resilient, agile solutions for persistent challenges. 

A recent Gartner survey found that 36% of manufacturing enterprises realize above-average business value from IT spending in digitalization at a reasonable cost when compared with peers. Is your manufacturing operation on trend? Let’s take a look at what cloud-driven smart manufacturing and Industry 4.0 look like in practice.

Digital Twins

One of the challenges of innovation in manufacturing is the sheer size of equipment, space, and logistics. Shutting down a production line to repair, replace or add a part or piece of equipment is expensive, potentially hazardous, and time-consuming.  In addition, despite the best measurements, fixed structures, wires, overhead beams or doors can be missed in the design phase, requiring costly changes and repeated shutdowns until the repair or part is installed, tested, and completed. 

Artificial intelligence uses augmented reality (AR) and virtual reality (VR) to create a 3-D model of the equipment, component or space, and then developers and engineers can work with this digital twin technology to design, tinker, adjust and perfect the equipment in virtual simulation before it is built and installed. 

According to a study by Gartner, 13% of organizations that have implemented Industry 4.0 and IoT are employing digital twin technology, and a further 62% are in the process of implementation. 

Some of the benefits of digital twin technology include reduced risks, accelerated production time, remote monitoring, enhanced collaboration, and improved fiscal decision-making thanks to advanced analytics and rapid testing in the cloud. 

GlobalLogic is a leader in building digital twin technology. Learn more about how it works in “If You Build Products, You Should Be Using Digital Twins.”

Predictive Maintenance

Equipment breakdowns and malfunctions are costly, time-consuming, and potentially dangerous to employees. One advantage of Industry 4.0 and digital twin technology is the ability to perform predictive maintenance in VR/AR. Unlike preventative maintenance, which is performed on a schedule whether the servicing is actually required at that point in time, predictive maintenance relies on data to predict when the maintenance should be performed. Successful predictive maintenance capabilities are dependent on the use of artificial intelligence, sensors, and cloud solutions. 

According to the US Department of Energy, an investment in a PdM strategy can reduce maintenance costs by up to 30%, reduce the number of unexpected breakdowns by ¾ and reduce the number of downtime hours by almost half. If properly implemented, it is also 25% cheaper compared to preventive maintenance.

The IoT sensors generate big data in real-time, and artificial intelligence and machine learning can analyze, flag anomalies, and initiate repair protocols before a problem halts production. Digital twin technology can scan a production operation from all angles continuously, and make recommendations for predictive maintenance that can be scheduled rather than completed on an emergency basis. This saves time, decreases production downtime, increases efficiency and safety, and mitigates risk.

Robotics & Autonomous Systems

Whether it is a full-scale automated robotic processing system, or a single station collaborative robot (cobots), robotics and autonomous systems have been changing the manufacturing landscape. 

Since the pandemic, however, robotics and autonomous systems have been driving digital transformation. Robots can work 24/7/365, they don’t take vacation, sick days or personal time off. They can provide rapid ROI and improve productivity while freeing human workers to do higher-value tasks. 

In recent years, the incorporation of AI, VR/AR, and machine learning has been employed to work side-by-side with human workers, and cobots with end-of-arm-tools equipped with machine learning can be moved by a worker in “teach” mode, and then it operates autonomously, becoming more efficient as it “learns” the task. 

The next innovation in robotics is individual microsystems, designed to work as autonomously as possible, while still collaborating with other microsystems. That way, if other microsystems crash, the others can continue to operate. Each microsystem is “choreographed” to work with others in collaboration while doing its part. It can be easily scaled and coordinated. Think of it as a colony of bees, each worker autonomous, but contributing to the whole synergy. Check out “Collaborating Autonomous Systems” to learn more about GlobalLogic’s work with microsystems. 

Connected Devices and the IIoT

Just as the IoT connects your smartphone to your thermostat, television, tablet, or speakers, the Industrial Internet of Things (IIoT) connects smart applications in manufacturing and industry. For example, the IIoT connects sensors on a cobot with the engineer’s tablet in another building, or the alarm system that activates if a sensor detects a chemical spill or heat increase. IIoT relies on cloud technology so that the data can be accessed from anywhere.

The data from these many sensors, controllers, and attached servers is often distributed across many remote locations. The data is uploaded continuously to the cloud, allowing for real-time updates at any time across multiple locations. McKinsey predicts that IIoT will be a $500 billion market by 2025.

One advantage of the IIoT is it provides simultaneous data from multiple locations and sources, whether within the same manufacturing facility or spread across multiple facilities or geographic locations. The cloud allows for centralized management of all the IIoT resources, but that management can happen from anywhere in the organization or the world. It provides business continuity and resilience if one location experiences an emergency or natural disaster, as operations can continue at the other locations, and real-time updates allow for quick response. 

Traditional IT storage requires hardware, system ware, servers and massive databases, and if the location goes down, the data can be lost. With IIoT cloud technology, the data is protected and accessible, while being encrypted and safeguarded by the cloud cybersecurity protocols.

IIoT is a form of edge computing, where the goal is to bring the resources from traditional data centers and bring them as close as possible where they are needed while maintaining safety, data protection, and guarding against cyber-attacks. 

GlobalLogic’s “Immunizing Edge Computing” takes a more in-depth look at how to protect data when working on the edge.

Conclusion

These are just a few examples of how cloud technology is transforming the manufacturing space. Intelligent automation, including VR/AR, artificial intelligence, machine learning, automated robotic processing, and autonomous microsystems are leading smart manufacturing innovations.

As more automation moves to edge computing – whether it’s a sensor, a pump, a car or a gateway – this trend will continue as the costs of computing power and related resources continue to decline. Determining precisely how to use the cloud and what can happen at the edge is an integral part of your smart manufacturing strategy and working with an expert in cloud technology is an important part of your intelligent automation business plan.

As innovation continues to evolve, the “edges” will get smarter, allowing for more powerful collaboration. With machine learning, the more the edge nodes “learn”, analyzing data, sensing the environment, and processing data, the more information will be available to share, whether peer-to-peer or through a network. IIoT will allow for smart edge collaboration in one form or another. 

Single station cobots, warehouse robots, and self-driving autonomous cars will continue to be innovation-driven, representing the span of collaborating autonomous systems, with no limits on the horizon. Intelligent automation, the IIoT, and other applications will continue to evolve robust, scalable, powerful systems with nuanced behavior. 

See how we can help you harness the power of cloud to engineer products to scale your manufacturing here.