Category "Blog"

In the current digital landscape, enterprises are battling with a plethora of challenges posed by explosive growth of data and the need for real-time responsiveness at edge. Traditional cloud computing architectures are struggling to keep up with the demands of modern businesses. It has pushed enterprises to constantly lookout for innovative solutions that can help enhance their operations and gain a competitive edge.

This has led to the rise of Edge Computing, a paradigm that brings computation and datastorage closer to the source of data generation, enabling processing at faster speed and higher volumes. By decentralizing processing power and reducing latency, edge computing addresses several critical challenges faced by enterprises today. As per Statista, the global edge computing market is expected to reach 12 billion US dollars by 2028 at a CAGR of 23.97% from 2020-2028.

In this blog, we will delve into the 5 fundamental challenges that it solves for enterprises, helps revolutionize their capabilities, and how it paves the way for an efficient and agile future.

5 problems Edge Computing can solve for Enterprises.

1. Latency and real time processing
   In the era of immediate access to information, reducing latency and achieving real-time responsiveness has become paramount for businesses. In traditional cloud architectures, data must travel from the edge devices to the centralized cloud servers, causing delays in processing and response times. Edge computing addresses this challenge by moving computational resources closer to the edge devices, thereby minimizing latency. By processing data locally at the edge, businesses can achieve near real-time analysis and decision-making, enabling time-sensitive applications like IoT, industrial automation systems to operate with lightning-fast speed and minimal delay.
   For instance, retail environments with video surveillance of the showroom floor can be combined with actual sales data to discover consumer demands or most desirable product configurations. Similarly, in the healthcare sector, IoT devices enable healthcare professionals to be more watchful and connect with patients proactively. By sharing real-time data collected from IoT devices, physicians can help analyze and identify patients’ health issues.
2. Bandwidth optimization with Edge analytics
   With the explosion of connected devices and the exponential growth of data generation, bandwidth has become a valuable and often limited resource. Transmitting all data to the cloud for processing and analysis can strain network infrastructure, result in increased costs, and lead to suboptimal network performance. Edge computing offers a solution by performing data processing and filtering at the edge devices themselves.
   By deploying servers and storage at the source of data generation, edge computing makes latency and congestion virtually non-existent as storage collects and processes the raw data, while local servers implement edge analytics to pre-process it before sending to the cloud. So, instead of sending raw data to the cloud, only relevant information or actionable insights are transmitted, significantly reducing bandwidth consumption. This optimization not only saves costs but also enhances overall network efficiency, allowing enterprises to make the most of their available resources.
3. Enhanced Data security
   Security and privacy are critical concerns for enterprises, especially when dealing with sensitive data. Centralized cloud architectures present potential vulnerabilities, as data must traverse the network, making it susceptible to interception or attacks. Edge computing, on the other hand, distributes data processing and storage closer to the source, reducing the attack surface. By processing data locally, sensitive information can be kept within the enterprise’s network perimeter, minimizing the risk of unauthorized access and data breaches.
   Any data traversing the network back to the data center or cloud can be secured using encryption techniques. This decentralized approach enhances security and provides enterprises with greater control over data privacy, mitigating potential risks associated with storing and transmitting sensitive information. Additionally, it improves reliability and protects users’ privacy as well.
4. Scalability and cost efficiency
   Edge computing minimizes the capital outlay and operating expenses. Centralized cloud architectures often face limitations when it comes scaling resources to meet evolving requirements. With edge computing, scalability is inherently built into modern architecture. The distributed infrastructure allows enterprises to easily scale their computing power by adding or removing edge devices as needed. Whether it’s adding new edge servers, edge nodes, or gateways, enterprises can dynamically scale their computing resources and distribute the computational load across a network of edge devices to handle increasing workloads or accommodate fluctuations in demand.
   Edge computing offers several cost-saving benefits. First, by reducing the amount of data required to be transmitted to cloud servers. Second, by minimizing data storage costs as it’s stored and processed locally. Additionally, edge computing reduces the need for extensive network infrastructure upgrades as enterprises can make use of existing local network connections. Lastly, by offloading processing tasks to edge devices, enterprises can minimize their dependency on high-cost centralized cloud resources. This enables them to allocate resources more efficiently and avoid the need for overprovisioning, leading to cost savings.
5. Governance and Compliance
   Enterprises operating in heavily regulated industries, such as healthcare and finance, face stringent compliance requirements regarding data storage and processing. Storing and processing sensitive data in public cloud environments can raise concerns about compliance with data protection regulations. For instance, data sovereignty law such as European Union’s GDPR defines how data should be stored, processed, and exposed.
   Edge computing offers a viable solution by allowing enterprises to process sensitive data locally. By keeping critical data within their own infrastructure, organizations can ensure compliance with regulatory requirements and maintain better control over their data. This level of control and adherence to regulatory standards helps mitigate compliance risks, enabling enterprises to navigate complex legal frameworks more effectively.

Wrapping up!

As users continue to generate an ocean of data, edge computing works as a most efficient solution for enterprises. By embracing edge computing, enterprises have the opportunity to optimize resource allocation, improve system performance, and unlock new opportunities for innovation, efficiency, and competitive advantage in the ever-evolving digital landscape. However, when searching for the right partner, make sure to engage with a trusted partner like Sify with deep industry expertise, proven hybrid/multi cloud platform, automation capabilities through AI/ML, and a comprehensive portfolio of services designed to accelerate performance, increase scalability, and strengthen security in your edge deployments.

Data centers have evolved into becoming a crucial component of the digital IT infrastructure of many global enterprises today. The demand for data center services in India is experiencing remarkable growth as enterprises aim to deliver superior customer experience, accelerate innovation, and become digitally enabled.

According to 6Wresearch, the data center market in India is expected to grow at a CAGR of 10.7% during 2021-2027. This growth can be attributed to key drivers such as the Indian government’s push towards digitalization, increasing Internet penetration, data sovereignty, accelerated cloud adoption, increasing usage of IoT, and the rollout of 5G. Similarly, according to GlobeNewswire, the value of the data center market in India is expected to rise to $10.09 billion by 2027 at a (CAGR) of 15.07% in the period of 2022 to 2027.

Managing a data center in India is getting more intricate by the day in today’s digital landscape. Enterprises find managing data centers to be complex and time-consuming. They need professional expertise, substantial budgets, and excellence in IT infrastructure execution to manage data centers. To meet the evolving digital demands and ensure business continuity, enterprises are now resorting to colocation, i.e. outsourcing of data center management to professional service providers. This delivers cost savings and a range of benefits, including access to subject matter experts, improved operational efficiency, agility, scalability, risk mitigation, enhanced security, and compliance with industry regulations.

Advantages of colocation of data center management

Here are a few advantages of colocation of data center management for enterprises:

1. Cost Savings: Building and maintaining a data center requires significant capital investment and operational expenses. By leveraging the model of colocation of data center management, enterprises can transfer these costs to a service provider that has already invested in infrastructure, equipment, and personnel. This empowers enterprises to free up capital that can be redirected toward other strategic initiatives. Additionally, it can help businesses avoid losses incurred from downtime and data breaches in the long run. Moreover, colocation positively impacts Water Usage Effectiveness (WUE) and Power Usage Effectiveness (PUE) by promoting improved energy efficiency. Utilizing the capabilities of Artificial Intelligence and Machine Learning, data centers can experience savings of up to 8% to 10% on PUE. This not only contributes to environmental sustainability but also helps enterprises meet their energy efficiency goals, while maintaining optimal performance and increased savings.
2. On-demand Scalability: When a business grows, its IT requirements change significantly and may require additional space, computing power, and capacity. Colocation enables enterprises to easily scale up or down as per requirement without incurring additional capital expenses or disrupting their business operations. Data Center service providers can quickly deploy new servers, storage, and network infrastructure or adjust existing configurations to meet changing business needs.
3. Automation and AI/ML: Implementation of AI/ML requires careful consideration across multiple parameters. Providers of colocation data center management services can handle large volumes of data, integrate with existing solutions, and enable predictive maintenance, which ultimately helps enterprises gain deeper insights for faster decision-making, automating processes, and delivering increased efficiency & security.
4. Access to Subject Matter Experts (SMEs): Colocation of data center management gives enterprises access to subject matter experts who possess specialized knowledge and experience in data center operations. They come with skills, guidance, solutions, and recommendations that can help enterprises optimize performance and minimize risks as well as free up internal resources for performing core business operations.
5. Enhanced Data Security: Data security has always been a major priority for enterprises. With data dispersed across multiple touchpoints in a hybrid work model, enterprises need a multi-layer security framework. Service providers ensure comprehensive security measures across the physical security of a DC and the data hosted across on-premise, colocation, or edge to cloud. Specialized service providers come with the expertise and systems to meet industry regulations, protect sensitive data, and minimize compliance risks. Enterprises must make sure that service providers offer:
   • A designated Security Operation Center (SOC) to ensure robust and resilient security
   • Faster intrusion detection and prevention through multiple protocols
   • Data backup and recovery in different seismic zones
   • Compliance with the latest data privacy regulations and industry standards
6. Adherence to Service-Level Agreements (SLAs): By adhering to the pre-defined SLAs, enterprises can enjoy benefits such as improving operational excellence, accountability, performance monitoring, risk mitigation, and cost optimization. SLAs also ensure service providers deliver the expected quality of service, provide regular reports and establish procedures for addressing issues and non-compliance penalties, which benefit enterprises.
7. Improved Reliability and Business Continuity: Colocation enables enterprises to improve reliability and business continuity through specialized expertise, scalability, planning, and improved security measures. Enterprises can leverage the experience of service providers, scale resources efficiently, and solve issues proactively. This ensures there are no disruptions in business operations.
8. Constant Technological Advancement: The colocation of data center management provides enterprises with constant technological advancement, including cloud on ramp capabilities. Service providers offer expertise in emerging technologies, regular infrastructure upgrades, access to cutting-edge technology, flexibility, and smooth integration through private direct connections to the cloud. It allows enterprises to be resilient and future-ready without compromising on technology and connecting their on-premises infrastructure to cloud computing systems.

Evaluate your data center needs to make the right choice

When an enterprise identifies a service provider for data center management services, it must evaluate its options based on its unique business objectives and digital priorities. Enterprises must go beyond the basic minimum criteria and focus on key differentiating factors. They must choose a service provider that focuses on:

• Cloud vision and strategy
• Hyperscale partnerships
• End-to-end managed services across multi/hybrid cloud environments
• Interconnect services to other sites and partner ecosystems

Colocation of data center management can be a valuable strategy for enterprises looking to maximize their IT resources and stay competitive in today’s rapidly-advancing digital landscape.

Sify Technologies has been providing reliable and robust data center services for the last 22 years, with a razor-sharp focus on innovation and new technologies. With the objective of delivering delightful experiences, enhanced efficiency, and desired outcomes to our customers, we have equipped all our data centers in India with automation through AI/ML capabilities. This has resulted in creating a sustainable ecosystem of connected data centers. We offer enterprises benefits such as zero downtime, reduced capital expenditure and operational expenditure, and around-the-clock support through our bankable digital data center infrastructure. Our efforts have delivered up to 20% improvement in the turnaround time to deliver critical projects.

What’s more! We also help enterprises realize up to 300 person-hour savings every month by automating customer billing. Our predictive approach to maintenance helps enterprises realize up to 20% reduction in MTBF, up to 10% improved MTTR, and up to 10% reduction in potential downtime. We comply with all the global standards and policies to prevent enterprises from penalties due to non-compliance.

Learn how our state-of-the-art data centers enable enterprises to achieve their desired business goals.

In the rapidly evolving landscape of the Internet of Things (IoT) and smart devices, reliable and efficient connectivity is paramount. Managed Wi-Fi services play a crucial role in enabling seamless communication and data exchange between IoT devices. The explosion of mobility & BYOD, enterprise virtualized applications moving to the cloud, increased use of multimedia-rich applications within the enterprise, and infrastructure modernization are a few of the core business drivers for increased adoption of enterprise managed Wi-Fi services.

With unprecedented advancements in IoT, data modeling, and AI/ML, there is a consistently growing focus on edge computing and its benefits. Enterprises are accelerating efforts to move to the cloud for on-demand computing, capacity, scale, better reach, scalability, and higher availability. Additionally, the managed Wifi services eliminate the traditional Wi-Fi challenges like lack of uniform access policy, no visibility into the wireless network, inadequate protection against wireless threats, complex or tedious onboarding process, and lack of visibility on user groups.

According to Marketsandmarkets, the global edge computing market size as per revenue surpassed $44.7 billion in 2022 and can rise to $101.3 billion by 2027 at a CAGR of 17.8% for the forecasted period (2022-2027).

This blog explores the significance of Enterprise Managed Wi-Fi services in supporting the proliferation of IoT and smart devices, the challenges they address, and the benefits they bring to businesses and consumers.

1. Connectivity and device management
   Managed Wi-Fi services offer a fully managed, secure wireless platform integrating Information Technology (IT), Operation Technology (OT), and people. It provides a robust infrastructure for connecting and managing a multitude of IoT devices. It offers a cloud-based centralized management platform with control and monitoring capabilities enabling enterprises to configure and manage their IoT devices efficiently. From provisioning and onboarding to security protocols and firmware updates, these services streamline the device lifecycle, reducing operational complexities and enhancing overall device management.
2. Security and data protection
   Managed Wi-Fi services enable a security posture which is crucial in the IoT landscape. The managed Wi-Fi services offer robust security features to protect IoT devices and the data they generate. By processing sensitive data locally or within a private network using network segmentation, edge devices can minimize data exposure to potential cyber threats as they’re isolated from other network resources. Managed Wi-Fi services also enable regular security updates and patches to address emerging threats, enhancing the overall security posture of IoT deployments.
3. Bandwidth management
   With the growing adoption of connected devices, scalability becomes a critical factor as transmitting a large amount of data to the cloud for processing can strain network bandwidth and incur significant costs. Enterprise Managed Wi-Fi services are designed to handle large-scale deployments enabling enterprises to seamlessly scale their IoT networks. Enterprises can prioritize critical applications and optimize network resources using advanced bandwidth management techniques to ensure IoT devices receive the optimal bandwidth for their operations, preventing congestion, maintaining smooth communication between devices, and more efficient network utilization.
4. Reliability and performance
   IoT applications and Smart devices often require real-time data exchange and low-latency connectivity. Managed Wi-Fi ensures high reliability and performance for IoT devices by optimizing network configurations and minimizing interference. They employ techniques like load balancing, channel selection, and Quality of Service (QoS) prioritization to maintain a stable and responsive Wi-Fi network. Edge computing reduces the latency required for sending data to a remote cloud server for processing. By processing data at the edge of the network, closer to the devices generating the data, response times can be significantly reduced. It is important for applications that require real-time or near-real-time processing, such as industrial automation and remote monitoring.
5. Analytics and insights
   Enterprise Managed Wi-Fi enables real-time analytics into network performance and device behavior, giving enterprises decision-making capabilities at the network edge. They collect and analyze data on network traffic, device connectivity, and usage patterns, offering actionable intelligence to optimize network resources and improve overall IoT operations. These insights can help identify potential bottlenecks, optimize device placement, and enhance user experiences. Additionally, analytics-driven predictive maintenance can proactively detect and address anomalies, reducing downtime and increasing the lifespan of IoT devices.
6. Scalability and cost-efficiency
   Edge computing allows for distributed computing resources, enabling scalability and cost-efficiency. Instead of relying solely on centralized cloud infrastructure, edge devices can share the computational load and distribute the processing tasks. It can result in reduced network congestion, lower costs associated with cloud resources, and improved scalability to accommodate the increasing number of IoT devices and data volumes.

Wrapping up!

With the growing digital landscape and IoT integration across enterprises, the need to deliver secure, seamless customer and employee experiences become a top priority. Enterprise Managed Wi-Fi services provide a reliable and secure connectivity infrastructure to deliver seamless device management, ensure scalability, optimize network resources, quick & accurate decision making, enhanced production planning, logistics, complete mobility for your workforce, and lower IT support costs.

As the IoT landscape continues to expand, Managed Wi-Fi services will remain crucial in delivering seamless and efficient connectivity for a wide range of applications, enhancing productivity, efficiency, and user experiences.

During the COVID-19 pandemic, it became increasingly difficult for organizations to manage their in-house data centers due to challenges with scalability, break/fix support, and operations and maintenance staffing requirements. As a result, many organizations moved their in-house data center to a colocation provider and some of their applications to public clouds.

Modern-day data center powerhouses provide not only the necessary infrastructure and state-of-the-art technology but also advanced data analytics and automation capabilities. They offer streamlined operations, business continuity, reduced Capex/Opex, flexible engagement models, superior end-customer experience, support for business expansion, and accelerated digital transformation, among other outcomes.

Selecting the right data center: Key factors in play

Data center service providers typically offer an extensive array of services to accommodate diverse customer requirements. These include on-premise solutions, colocation options, wholesale and retail offerings, hyperscale capabilities, as well as specialized offerings like built-to-suit (BTS) data centers, green data centers, and comprehensive managed services. Businesses can find tailored solutions that align with their specific needs and preferences. It is important to choose the right data center service provider that can provide such integrated services, based on your business objectives and digital priorities.

[Interested to know more about Sify’s world-class data center facilities? Learn more]

Here are some factors to consider when selecting a data center service provider:

• Scalability: Rack space is a critical and limited commodity for many service providers. Selecting world-class data center providers with the capacity to build campuses capable of accommodating 8,000+ racks and 50 MW IT capacity, for example, will enable businesses to plan their scale-up within their choice campus. They also stand to benefit from a robust support/service stack and comprehensive monitoring of the data center infrastructure.
• Security: Be sure to prioritize data centers with robust security systems. IT infrastructure protection, including data security through encryption, firewalls, intrusion detection, and prevention, needs to be implemented – at the very minimum. Your data center service provider should also have multilayer physical security from the perimeter wall up to the cage, including mantraps, turnstiles, and biometric access control, in addition to surveillance cameras and security personnel, to restrict unauthorized individuals from entering secure areas or accessing confidential information.
• Network connectivity: Choose a data center provider with excellent network connectivity, including multiple internet service providers (ISPs) offering IP transit nodes, high-speed connections, multi-cloud connectivity, data center interconnectivity, and connectivity to customer premises. Data centers should have at least 3-4 fiber entry paths to the building, ensuring seamless connectivity and resilience. Additionally, the presence of low-latency cloud on-ramp services, including cloud access nodes, facilitates fast and direct interconnection with public cloud applications. These aspects collectively contribute to creating a robust, efficient, high-performing, and well-connected data center environment.
• Certificates, SLAs, and compliance: When selecting a data center provider, the importance of having the necessary certificates and service level agreements (SLAs) cannot be overstated. Certifications from industry authorities are highly desirable as they validate the provider’s adherence to industry best practices, process reliability, and security standards. A tier-3 data center with ISO 27001, ISO 20000, PCI-DSS, SoC 1, SoC 2 and ISO 1400, and ISO 50000 certifications is ideal. With its robust compliance framework, 99.99% uptime, and clear SLAs outlining performance commitments, Sify’s data centers deliver the highest levels of security, operational excellence, and environmental responsibility to customers.
• Industry experience: Data centers that have subject matter experts across functions can prove instrumental in managing diverse workloads. Extensive cross-industry experience gives them the ability to address the unique requirements of various sectors, such as finance or healthcare. For example, as a leading data center service provider in India, Sify has extensive experience across diverse industry sectors. Sify’s digital data center infrastructure services offer real-time visibility, measurability, predictability, and service support specifically required by different industries to offer customers high availability and seamless experiences.
• Green power: Many organizations today have committed to ESG goals, such as carbon neutrality, waste reduction, and power conservation. In this context, it becomes important to choose a data center provider that is invested in renewable energy, achieved by signing Power Purchase Agreements (PPA). Solar and wind power are increasingly viable options for clean energy. By choosing a data center service provider that adopts sustainable measures including renewable power, energy-efficient equipment, and practices, your business can achieve environmental goals while benefiting from competitive energy costs.

  Adherence to safety practices, rules, and regulations are also key EHS considerations. Leading green data center service providers, like Sify, invest in transparent, environmentally conscious, and ethical business practices, adhering 100% to local and global regulations, and outperforming the competition when it comes to sustainability, corporate social responsibility, and people practices.

  [Going green? Know how Sify’s green data centers are pushing the envelope on sustainability. Learn more]

• Data center footprint: Data centers strategically located in multiple regions ensure low latency and high-speed network connections, enabling efficient data transmission and improved user experience. A widespread presence allows data centers to establish diverse network routes and redundancy, minimizing the risk of network failures or disruptions.
• Partnership with hyperscalers: Hyperscale partnerships enable data centers to offer seamless integration with leading cloud platforms, offering flexible hybrid cloud solutions and enhanced performance. Ensure you choose a service provider that banks on the power of partnerships and leverages the sharing of expertise and resources to stay at the forefront of technological advancements.
• Automation and innovation: AI/ML-driven automation is increasingly important in developing innovations that optimize operations, reduce costs, enhance performance, improve reliability and sustainability, and elevate service quality. Integration of AI/ML in vendor performance evaluation and SLA management, including metrics like MTTR and MTBF, further strengthens operations. For instance, Sify’s AI/ML capabilities have contributed to significant improvements of over 20% in project delivery turnaround time, showcasing the tangible benefits of data analytics in the data center domain.
• Backup and DR: It is essential to consider the risk of natural disasters such as earthquakes, floods, hurricanes, or wildfires. Select a location with minimal risk to ensure the safety and longevity of your IT infrastructure. It is also crucial to select a data center service provider with adequate backup and disaster recovery (DR) capabilities. This ensures that in the event of an unforeseen incident, the data center can quickly recover operations with little or no data loss.

Wrapping up!

While these parameters will provide you with a solid basis for comparison, allow yourself to make the final decision based on your business’s specific objectives. Remember, there’s no one-size-fits-all approach when it comes to choosing a data center service provider.

As India’s pioneering data center service provider for over 22 years, it has been Sify’s continuous endeavor to innovate, invest in, and integrate new-age technologies. Learn more about how our state-of-the-art data centers have been delivering transformative business value to enterprises across the globe.

Credits: Published by our strategic partner Tenovos.

In an era of increasing personalization, the key to successful marketing campaigns is effective storytelling that reaches the right audience with the right message at the right moment. If this chemistry between audience, content, and timing is the key to success, creative and marketing professionals have to rely on next-generation asset management technology that can guide them toward the right combinations by replacing guesswork with data, and surrounding content with context.

Traditionally, Digital Asset Management platforms (DAMs) have focused on assisting teams to manage their digital assets and move them from inside the organization to external partners and platforms. The premise is simple: a central repository where brands can store their assets alongside relevant metadata to make everything easy to find — photos and videos, logos and tear sheets, and any other brand collateral that needs to be used and reused.

At its most basic level, a good DAM solution enables marketers to do their jobs more efficiently. More modern DAMs employ AI and machine learning to automatically add relevant tags to assets, so teams can spend less time on tedious tasks like tagging or finding assets and more time on the creative and analytical areas of their jobs. The most advanced DAM platforms, however, go beyond managing and moving assets, to actually measuring their performance in their context of use.

Building a DAM for the Modern Enterprise

Over the past 20 years, the pace of change in technology has exploded, while the DAM category has lagged behind in innovation. Brands have come to expect an exceptional, personalized user experience complete with smart insights from their marketing platforms, and DAM should be no exception. As marketing becomes increasingly fast-paced and data-driven, it’s time for a completely new and different DAM experience — one that can meet the demands of an increasingly tech-savvy industry and understands its pain points.

The first and most important question that a DAM provider should ask is, ‘how does my solution help marketers do their jobs more effectively?’ That is, after all, the central goal of a DAM system: to make it easier and more efficient for creators and marketers to collaborate to design and execute successful campaigns. In other words, if finding an asset within the DAM system and searching through emails to find it take approximately the same amount of time, the system is not making teams more efficient — it’s simply adding a layer of complexity to their martech stack.

Creating a Seamless User Experience

Not all DAM platforms are created equal. One common issue that many enterprises face is the inability to seamlessly integrate their DAM platform with the rest of their marketing ecosystem, which can essentially negate the efficiencies gained by using an asset management solution in the first place. Considering that the code base of many solutions currently on the market is over a decade old (older, in some cases), this is a problem that will only get worse over time as marketers look to incorporate new tools and technologies into their workflows. Consequently, it’s important to find and implement a solution that leverages the use of modern technologies — such as AI/ML, micro-services, graph databases, and serverless environments — that will be able to maintain its speed and flexibility in the years to come.

An organization’s ability to collaborate seamlessly with team members across — and outside of — the organization is also a key indicator of the success of a DAM implementation. Marketing doesn’t happen in a single silo; from research to ideation, to creation to deployment, marketing is interconnected and interdisciplinary. A modern DAM platform should connect the enterprise in such a way that it simplifies the creative life cycle and enables marketers to reduce the friction and time required to launch each new campaign.

Data-Driven Marketers Need Data-Driven Technology

The reality is that many of the DAM solutions available on the market currently have not kept pace with the evolving needs of the increasingly data-driven marketing operation; they’re often expensive, difficult to implement, and don’t deliver the user experience marketers and creative professionals have come to expect from their technology. Seen from this angle, it’s not surprising that many organizations are hesitant to invest heavily in a new system that is not capable of demonstrating a return on investment.

Brands need modern DAM platforms that not only enable them to meet the demands of marketing in the digital age, but also help them to demonstrate — and improve — their ROI. Marketers should expect their DAM platform to provide:

• A data-first approach to asset management that allows brands to measure and optimize their processes and their content to provide increasingly personalized experiences
• A seamless user experience that drives adoption and enables teams across the world to collaborate easily
• Performance and optimization capabilities underpinned by artificial intelligence and machine learning
• Continuous improvement and delivery to support the demands of a global omnichannel enterprise

At the end of the day, companies implement a DAM solution in order to optimize their processes and improve their ability to tell the compelling stories that are central to a successful marketing operation. This optimization should come not only in the form of improving the speed of creation, but also the strategy behind a given campaign. A system that has access to all of the contextual data that surrounds your every asset should be able to distill those data into insights that inform the creation of future content.

A modern, data-first DAM should act not only as a content database but also as a source of insight to enable marketers to make smarter creative decisions, which in turn allows them to tell stories that matter to their audience.

Want to know what types of data your DAM should be providing? Reach us at marketing@sifycorp.com

Written by Michael Waldron, CMO, Tenovos

 

Introduction

SAP has unleashed its campaign of ‘RISE with SAP’ and it has been received very well by SAP customers and ERP prospects worldwide. RISE with SAP transitions ERP data (in the form of SAP ECC6.0 on-premise or SAP S/4 HANA on-premise) to the cloud (public or private) with less risk and without compromise. The bundle of ERP software, transformation services, business platform and analytics is quite an attractive offer to SAP customers having hosted SAP on-premise.

This article delves into the business platform and analytics which is clubbed under ‘Business Technology Platform’ (hereafter called as BTP).

SAP BTP is a cloud-based platform-as-a-service (PaaS) offering from SAP, which provides a set of tools and services for developing, integrating, and extending SAP applications and solutions. SAP BTP supports various cloud deployment models, including public, private, and hybrid clouds, and allows developers to build, deploy, and run their applications using SAP’s cloud infrastructure.

Four pillars of SAP BTP – It encompasses various capabilities that are categorized into following four pillars:-

1. Integration

This feature gives everything that is needed for agile business process innovation, extension, and integration in the cloud and in hybrid scenarios. You can easily integrate different systems, extend your current application, or create new solutions for your business needs with ideal user experience using the SAP Fiori interface. SAP Extension Suite provides various services that can be leveraged to build and extend SAP solutions. SAP Integration Suite (formally known as SAP Cloud Platform Integration -CPI Suite) lets you seamlessly integrate SAP and non-SAP solutions, both on-premise and in the cloud. SAP Integration Suite covers not only A2A and b2B integration scenarios but B2G(Business to Government) integration scenarios as well.

Currently, SAP provides over 2000+ pre-packed integration scenarios for different business processors. These out-of-the-box integration scenarios are ready-to-use, require minimum development effort, and cover a range of business process integrations. (Check out  https://api.sap.com/  for details).

With the introduction of SAP Integration Suite, SAP PI/PO would be getting phased out in near future.

2. SAP Build

It enables everyone – no matter the skill level – to rapidly create and augment enterprise-grade apps, automate processes and tasks, and design business sites with drag-and-drop simplicity.

SAP Build brings together SAP Build Apps (formerly SAP AppGyver), SAP Build Process Automation (formerly SAP Process Automation), and SAP Build Work Zone (formerly SAP Work Zone) into a unified development experience with new innovations to rapidly build apps, automate processes and create business websites.

Low-code / No-code development – Low-code uses both a traditional programming language-based environment combined with no-code platforms and is used by developers with at least basic technical knowledge.

No-code is simpler, and it fully replaces the traditional programming language-based tooling with a suite of visual development tools (ex. drag-and-drop components) and can be used by technical and non-technical people alike.

3. Data and Analytics

The SAP Data sphere component enables accessing authoritative data, helps harmonizing heterogeneous data and thereby simplifies the data landscape.

The SAP Master Data Governance enables operating on high quality, consistent master data and established a comprehensive master data governance

SAP Analytical cloud – It is a single solution for business intelligence and enterprise planning, augmented with the power of artificial intelligence, machine learning technology, and predictive analytics. It helps everyone in your organization make better decisions and act with confidence.

SAP Analytics Cloud removes silos, empowers business analysts, and unifies a company’s decision-making processes by combining business intelligence, augmented analytics, and enterprise planning into one product. It helps in achieving 360° insights with a single connected analytics platform.

4. Artificial Intelligence

It enables business applications and processes more intelligent with the power of AI on SAP Business Technology Platform. Its pre-trained AI models accelerate infusion of AI into Apps. It helps managing the AI model lifecycle into one central place and ensures AI deployment responsibly with transparency and compliance.

SAP solutions such as SAP Intelligent Robotic Process Automation (SAP Intelligent RPA) and machine learning let you automate the kind of complex, repetitive decisions that make up a significant portion of business processes.

Service Catalog – SAP has come up with a rich repository of readily available 96 services encompassing one or more of the four pillars mentioned earlier. They help in integrating and extending your solutions, optimizing your business processes, and thereby creating an engaging digital experience using SAP Business Technology Platform services. Just to give an idea, some of the services are listed below:

• Automation pilot – Simplify the operational effort behind any cloud solution in the SAP BTP.
• Cloud foundry runtime – It lets you develop polyglot cloud-native applications and run them on the SAP BTP Cloud Foundry environment
• Cloud Integration for data services – To integrate data between on-premise and cloud on a scheduled/batch-mode basis.
• Continuous Integration and Delivery (CI/CD) – It lets you configure and run predefined continuous integration and delivery pipelines that automatically build, test, and deploy your code changes to speed up your development and delivery cycles.
• Identity provisioning – Lets you manage Identity Lifecycle processes for cloud and on-premise systems
• Kyma runtime – Develop and run containerized applications and extensions on Kubernetes. Kyma runtime is a fully managed Kubernetes runtime based on the open-source project “Kyma”. This cloud-native solution allows the developers to extend SAP solutions with serverless functions and combine them with containerized microservices. The offered functionality ensures smooth consumption of SAP and non-SAP applications, running workloads in a highly scalable environment, and building event- and API-based extensions.
• SAP AI core – It enables building a platform for your artificial intelligence solutions. It is designed to handle the execution and operations of your AI assets in a standardized, scalable, and hyper-scaler-agnostic way. It provides seamless integration with your SAP solutions. Any AI function can be easily realized using open-source frameworks. SAP AI Core supports full lifecycle management of AI scenarios.

SAP BTP Deployment – Salient points:

• Regions – You can deploy applications in different regions. Each region represents a geographical location (for example, Europe, US East) where applications, data, or services are hosted. A region is chosen at the subaccount level. For each subaccount, you select exactly one region. The selection of a region is dependent on many factors: for example, application performance (response time, latency) can be optimized by selecting a region close to the user. The global account itself is also running in a region.
• Environments – Environments constitute the actual Platform as a Service offering of SAP BTP that allows for the development and administration of business applications. Environments are anchored in SAP BTP on the subaccount level.

Each environment comes equipped with specific tools, technologies, and runtimes that you need to build applications. So a multi-environment subaccount is your single address to host a variety of applications and offer diverse development options. One advantage of using different environments in one subaccount is that you only need to manage users, authorizations, and entitlements once per subaccount, and thus, grant more flexibility to your developers.

• SAP BTP can have one or more global accounts. Global accounts are associated with license or contract which your company has with SAP BTP. Global account takes care of the license and contract and whatever the activities you perform or how you are billed is managed by global account.
• Global accounts are linked with entitlements which are passed down to subaccounts. Entitlements are the kind of resources provided to you based on the license that you purchased.
• Sub account is the place where you will be creating your PaaS environment(cloud foundry/Kyma).
• SAP BTP Cockpit – The SAP BTP cockpit is the central user interface for administering and managing your SAP BTP accounts as a platform user. To access the SAP BTP cockpit, you need to open a specific URL ‘https://cockpit.<region>.hana.ondemand.com’. You can replace the <region> with the one you are operating in (for example: eu10, us10, ap10) to have a lower response time and latency to the cockpit. After logging in with your user credentials, you might get prompted with a pop-up to choose the global account you want to access. Of course, you are able to switch between the global accounts as and when needed.
• Working with the SAP BTP cockpit is the easiest way to manage and administer your SAP BTP accounts.

Conclusion:

SAP has come up with a lot of helpful resources related to BTP including use cases, case-studies, readily available services, pre-packed integration scenarios, tutorials etc. BTP offers a rich load of tools/services which would enable your organization’s business transformation and expedite your digitization journey. It needs to be fully leveraged when you opt for ‘RISE with SAP’.

Data centers have emerged as a crucial component of the IT infrastructure of businesses. They handle vast amounts of data generated by various sources, and over the years have transformed into massive and complex entities. Of late, data analytics has emerged as a necessary ally for data center service providers, powered by the growing need to improve parameters like operational efficiency, performance, and sustainability. In this blog, we will discuss the different ways in which data analytics and AI/ML can help enhance data center management and empower data center service providers to deliver better service assurance to end-customers.

How data analytics and AI/ML can help service providers in data center optimization

Today, data center service providers are leveraging data analytics in various ways to optimize data center operations, reduce costs, enhance performance, reliability and sustainability, and improve service quality for customers. They employ a variety of methods to collect data from colocation, on-premise and edge data centers, which include physical RFID/EFC sensors, server, network and storage monitoring tools, security information and event management (SIEM) systems, configuration management databases (CMDBs), API integration, and customer usage data. The data collected is then fed into a centralized monitoring and analytics platform, which uses visualization tools, dashboards, and alert systems to analyze the data and generate insights.

Furthermore, by integrating IoT and AI/ML into data center operations, service providers are gaining deeper insights, automating various processes, and making faster business decisions. One of the most critical requirements today is for analytical tools that can help with predictive assessment and accurate decision-making for desired outcomes. This is achieved by diving deep into factors such as equipment performance, load demand curve, overall system performance, as well as intelligent risk assessment and business continuity planning. Selection of the right tools, firmware, and application layer plays a major role in making such an AI/ML platform successful.

The relationship between analytics and automation from the perspective of data centers is rather symbiotic. Data centers are already automating routine tasks such as data cleaning, data transformation, and data integration, helping data center service providers free up resources for more strategic analytics work, such as predictive modeling, forecasting, and scenario planning. In turn, data analytics provides valuable insights that enable data centers to implement intelligent automation and optimization techniques. This may include workload balancing, dynamic resource allocation, and automated incident response.

Here are some of the key areas where data analytics and automation have a significant impact:

• Enhancing operational reliability: Data analytics, AI/ML and automation can enable data centers to ensure optimal performance. This involves using predictive maintenance, studying equipment lifecycles for maintenance, and incident history analysis to learn from past experiences. In addition, AI/ML-driven vendor performance evaluation and SLA management incorporating MTTR and MTBF further strengthen operations. Leveraging these metrics within the ITIL framework helps data centers gain valuable operational insights and maintain the highest levels of uptime.
• Performance efficiency: Data centers consume a substantial amount of energy to power and maintain desirable operating conditions. To optimize services, track hotspots, prevent hardware failure, and improve overall performance, modern data centers analyze data points such as power usage, temperature, humidity, and airflow related to servers, storage devices, networking equipment, and cooling systems. Prescriptive analytics can take this a step further by providing recommendations to optimize utilization and performance.
• Predictive maintenance: Predictive analytics is a powerful technology that uses data to forecast future performance, identify and analyze risks and mitigate potential issues. By analyzing sensor data and historical trends, data center service providers can anticipate potential hardware failures and perform maintenance before they escalate, with advanced predictive analytics enabling them to improve equipment uptime by up to 20%.
• Capacity planning: Businesses today must be flexible enough to accommodate capacity changes within a matter of hours. Data center service providers also need to understand current usage metrics to plan for future equipment purchases and cater to on-demand requirements. Data analytics helps in optimizing the allocation of resources like storage, compute, and networking while meeting fluctuations in customer needs and improving agility.
• Security and network optimization: Data centers can use analytics to monitor security events and detect vulnerabilities early to enhance their security posture. By analyzing network traffic patterns, data analytics tools help identify unusual activities that may indicate a security threat. They can also monitor network performance, identify bottlenecks, and optimize data routing.
• Customer insights: Data centers collect usage data, such as the number of users, peak usage times, and resource consumption, to better understand customer needs and optimize services accordingly. Analytics helps providers gain insights into customer behavior and needs, enabling them to build targeted solutions that offer better performance and value. For example, through customer-facing report generation, organizations and end-customers can gain valuable insights and optimize their operations. Additionally, analytics accelerates the go-to-market process by providing real-time data visibility, empowering businesses to make informed decisions quickly and stay ahead of the competition.
• Environment sustainability & energy efficiency: Data centers have traditionally consumed significant power, with standalone facilities consuming between 10-25 MW per building capacity. However, modern data center IT parks now boast capacities ranging from 200-400+ MW. This exponential growth has led to adverse environmental impacts, such as increased carbon footprint, depletion of natural resources, and soil erosion. Using AI/ML, performance indicators like CUE (Carbon Utilization Effectiveness), WUE (Water Utilization Effectiveness), and PUE (Power Utilization Effectiveness) are analyzed to assess efficiency and design green strategies, such as adopting renewable energy, implementing zero water discharge plants, achieving carbon neutrality, and using refrigerants with low GHG coefficients. For example, AI/ML modeling can help data centers achieve 8-10% saving on PUE below design PUE – helping to balance environmental impact with an efficiency better than what was originally planned.
• Asset and vendor performance management: The foundation of the AI/ML platform lies in the CMDB, which comprises crucial data, including asset information, parent-child relationships, equipment performance records, maintenance history, lifecycle analysis, performance curves, and end-of-life tracking. These assets are often maintained by OEMs or vendors to ensure reliability and uptime. AI/ML aids in developing availability models that factor in SLA and KPI management. It can provide unmatched visibility into equipment corrections, necessary improvements, and vendor performance. It can also help enhance project models for expansion build-outs and greenfield designs, accurately estimating the cost of POD (point of delivery) design, project construction, and delivery.
• Ordering billing and invoicing: AI/ML plays a vital role in enhancing the efficiency and effectiveness of order, billing, and invoicing processes. Its impact spans various stages, starting from responding to RFPs to reserving space and power, managing capacity, providing early access to ready-for-service solutions, facilitating customer onboarding, and overseeing the entire customer lifecycle. This includes routine processes such as invoicing, revenue collection, order renewal, customer Right of First Refusal (ROFR) management, and exploring expansion options both within and outside the current facility.

Selecting the right data analytics solution

The implementation of data analytics and automation through AI/ML requires careful consideration as several parameters, such as data quality and level of expertise play a crucial role in delivering efficient end-results. To succeed, businesses need to choose user-friendly and intelligent solutions that can integrate well with existing solutions, handle large volumes of data, and evolve as needed.

At Sify – India’s pioneering data center service provider for over 22 years, we continuously innovate, invest in, and integrate new-age technologies like AI/ML in operations to deliver significant and desired outcomes to customers. We are infusing automation led by AI/ML in our state-of-the-art intelligent data centers across India to deliver superior customer experiences, increased efficiency, and informed decision-making, resulting in more self-sustaining and competitive ecosystems. For example, leveraging our AI/ML capabilities has been proven to lead to over 20% improvement in project delivery turnaround time. Our digital data center infrastructure services offer real-time visibility, measurability, predictability, and service support to ensure that our customers experience zero downtime and reduced Capex/Opex.

How do Sify’s AI-enabled data centers impact your business?

• Person-hour savings: Automation of customer billing data and escalations resulting in up to 300 person-hour savings in a month.
• Reduction in failures: Predictive approach for maintenance and daily checks yielding up to 20% reduced MTBF, 10% improved MTTR, and 10% reduction in unplanned/possible downtime.
• Cost savings: Improved power/rack space efficiency and savings on penalties to deliver up to 8% reduction in customer penalties by maintaining SLAs and 10% reduction in operating cost.
• Compliance adherence: Meeting global standards and ensuring operational excellence and business continuity.

To know more about our world-class data centers and how they help enterprises expect positive business outcomes, visit here.

What if human beings could better their brain, built across millennia through evolution? Gourav looks at the possibility of just such a technology and its implications.

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We all think, act, react, ponder, decide, and memorize with the help of our brains. It is a very intriguing, interesting, and exciting part of our human body and contributes drastically to our human ecosystem.

It is also still a mystery as to how our brain, one of the most complex systems found in nature, functions.

Imagine an artificial copy of our human brain that can do the same without our help. If such a machine is created, then the boundaries between a human and a machine would grow thinner bringing to the fore its advantages and disadvantages.

The Brain and Mind Institute of École Polytechnique Fédérale de Lausanne in Switzerland did exactly that when they thought up a project called Blue Brain Project aimed at creating an artificial brain. This project was founded by Henry Markram in 2005.

What is Blue Brain Project?

The Blue Brain Project is bleeding-edge technology research that aims to reverse engineer a typical human brain into a computer simulation. Blue Brain can think, act, respond, make quick decisions, and keep anything and everything in its memory.

It means that a computer can act as a human brain taking artificial intelligence sky high. The simulations are carried out on IBM’s Blue Gene supercomputer, hence the term Blue Brain.

Why do we need this?

Today, we function based on our brain’s capability to respond to different situations. Some people make intelligent decisions and take actions as they have an inborn quality of intelligence. But this intelligence dies when we die. Imagine if such intelligence can be preserved to help the future generation.

A virtual or an artificial brain that can provide the required solution for the stated problem. Our brain tends to forget trivial things that mean more like birthdays, names of people, etc.

Such a brain can help us by storing this information and aiding whenever necessary. Imagine uploading ourselves onto a computer and living inside it.

How can this be made possible?

The information about the brain needs to be uploaded into the supercomputer to perform like a brain. So, retrieval or studying of this information is paramount. This can be made possible by using small robots called nanobots.

These bots can travel between our spine and brain to collect important data. These data contain necessary information such as the structure of the human brain, its current state, etc.

A human brain takes inputs from the sensor throughout the body, and it interprets these inputs to store in the memory or to respond to the desired output.

The artificial brain does a similar job by taking inputs from a sensory chip and it interprets these inputs by associating the input with the value stored in one of its registers which corresponds to different states of the brain.

The Blue Brain Project – Software Development Kit helps the users to utilize the data from the nanobots to visualize and inspect models and simulations. The SDK is a C++ library that is wrapped around Java and Python.

The Einstein Connection

When people think of genius, the list most assuredly includes Albert Einstein. For years, different scientific researchers have been trying to find the mystery behind his genius brain. Imagine if Einstein’s brain could be recreated with the help of the Blue Brain Project. Many intriguing inventions and discoveries could be made. Such intelligence would shape many generations to come. 

The Blue Brain Project has many merits such as non-volatile memory that can store anything and everything permanently, and the capability to make intelligent decisions without the presence of a person. This research can help in curing a lot of psychological problems.

If such technology comes to people, they would be dependent on these systems. This can open the door wide open for hack threats which can pose a real danger to people. People might be fearful of using such technology and it can culminate into large resistance.

The Author’s Views

Intelligence is a quality that has always been associated with humans. Now artificially many intelligent systems and tools are available that aim to better people’s lives.

If Blue Brain technology reaches humans, everyone’s life will be enriched.

But people might get too dependent on this technology which will culminate in catastrophic problems for the human psyche. However, if used properly this technology can add new layers to human life than being a replacement.

Credits: Published by our strategic partner Kaiburr

As the technology leader (CDO / CIO / CTO / CISO or a VP of Technology / Engineering / DevOps / DevSecOps /Security / Compliance) you are looking to deliver your digital initiatives in a predictable manner and accelerate maturity of your software product teams while ensuring gaps are not introduced in the software supply chain.

To achieve this you need answers to the following questions:

• What is our current level of DevSecOps / DevOps maturity?
• Are we really doing the steps we set out to do across various stages of SDLC? How do we identify the tasks falling through the cracks in the software supply chain?
• What is our current level of risk on security, compliance, and quality?
• How effectively are we using the 15-20 tools procured?

Some examples of common issues in the software supply chain are:

After more than six years of R&D, Kaiburr, a low code /no code digital insight platform, is solving this problem meaningfully and at scale for large enterprises and top innovators. With Kaiburr, digital leaders and software teams get a single pane on their overall stage gates across the entire SDLC at the organization, business unit, portfolio, program, and product (application) level like the following:

Users can drill down on any stage gate to know specific items to be acted upon

• [ALM] Stories missing acceptance criteria or story points in tools like JIRA, Azure Boards, Gitlab
• [Source Code Mgmt.] Commits and Pull Requests missing traceability to requirements in tools like Bitbucket, GitHub
• Acceptance criteria set the bounds for the story and the scope of the work the story entails.[Code Quality] Code quality issues on features in tools like SonarQube
• [SAST] Critical static analysis vulnerabilities on the latest code merged in tools like Veracode, Checkmarx
• [SCA] Vulnerable libraries downloaded for releases in tools like Snyk, Blackduck
• [CI-CD] Build / deployment issues in tools like Jenkins, Tekton, Bamboo, Azure DevOps
• [Unit Test] Unit test coverage gaps in tools like JUnit, NUnit
• [Functional Test] Test failures in tools like Selenium, Cucumber, Katalon
• [Auto Provision] Infrastructure automation issues with tools like Terraform, Pulumi
• [Monitoring] Application monitoring issues with tools like Datadog, Dynatrace

Kaiburr adopts the following process for teams to effectively remediate gaps in the software supply chain

To add cherry on top, Kaiburr has mapped out these stage gate validations to industry standard

frameworks like NIST 800 53, CIS, ISO 27k, SOC2, GDPR, FedRAMP, HIPAA, HITRUST, PCI.

Kaiburr has deeply engineered this framework to solve this complex problem:

With Kaiburr

• Digital leaders can gain near real time visibility on gaps in their SDLC so they can mitigate them early in the cycle
• Developers get spoon fed on priorities so their experience and productivity is improved
• Security, Compliance and Governance leaders can identify and remediate security and compliance issues in a timely manner Digital leaders can produce audit reports on internal controls in a fully automated manner

If you want to get started with your Stage Gate Compliance journey using Kaiburr reach us at marketing@sifycorp.com

Credits: Published by our strategic partner Kaiburr

 

Prashant Kanwat breaks down what a cloud kitchen is and how it is revolutionizing the online market

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Gone are the days of restaurants and dining out being the only option to travel beyond the everyday home food and the kitchen. If you look around these days, seeing food aggregators such as Zomato and Swiggy running round the clock to deliver the freshest food right at the customer’s doorsteps, is a common sight.

Not only has this driven a trend among consumers, but it has also left the food entrepreneurs, the small restaurant owners, and people in the food industry, gaping at the growing trend of the online ordering business. The number of users of the online food delivery system is expected to grow up to 2.9 million users by 2026.

A cloud kitchen or a ‘ghost kitchen’ is called so, due to the physical visibility it lacks, to the public. Unlike restaurants that offer dine-in, cloud kitchens are devoid of all the setup. In fact, cloud kitchens require minimum equipment, such as space and kitchen equipment, compared to the lavish decor that restaurants use. 

Types of cloud kitchen model

1. Independent cloud kitchen: As the name suggests, behind the cloud kitchen is a single brand that is dependent on an online ordering system for their orders. With a small team of chefs, definitive operative hours and a brand name, independent cloud kitchens have a business model that is self-reliant and is hosted on different food aggregators to acquire customers.
2. Hybrid cloud kitchen: Being a hybrid of takeaway and cloud kitchen, a hybrid cloud kitchen can be visualized as an extension of the regular cloud kitchen.
3. Food aggregator owned cloud kitchen: With the aim of generating revenue and growing popularity of cloud kitchens, there are several food aggregators that lease out or purchase a convenient kitchen space to a growing food brand or one that is new in the market.
4. Multi brand cloud kitchen: This cloud business model is a combination of varied brands under the same kitchen.
5. Outsourced cloud kitchen: TAs the newest entry to the cloud kitchen game, this cloud kitchen business model is solely dependent on outsourcing of the food and the delivery services. A restaurant or any other business can outsource a part or the entirety of the menu such that the prepared product is received at the restaurant. The restaurant then packs the item and hands it over to the delivery personnel. The operational cost for the in-house team is reduced as everything from preparation to delivery is handled by the outsourced group.

How to set up a cloud kitchen in India

1. Choosing the right rental space
2. Licenses and trademark registration

   Some of the licenses to procure before starting out with a cloud kitchen business model include, 

   • GST (Goods and Services Tax) (Goods and Services Tax) registration
   • Trade license
   • Fire and safety license 
   • FSSAI (Food Safety and Standards Authority of India) license 
   • Trademark registration
3. Deciding the cuisine 
4. Kitchen space, equipment, and raw ingredients
5. Online Order Management System 
6. Staff requirements 
7. Marketing

Costs associated with a cloud kitchen

The costs of setting up a cloud kitchen model in India vary depending on the city chosen, the demographics, the type of cuisine offered and so on. Here is a rough outline of the costs that might come up and a rough estimate of how much they amount to.

The resources one would have to be spending in a cloud kitchen business model include,

1. Rent: This depends on the location and the land prices. A space of 600-800 sq feet is considered sufficient for a cloud kitchen model and may range from ₹25,000-50,000 
2. Licenses: The basic and necessary licenses cost around ₹15,000-20,000
3. Staff: Having a basic set of staff can cost around ₹50,000-85,000
4. Kitchen and equipment: This are solely dependent on requirement and can range from ₹5 lakh from scratch to around 8 lakhs. Basic kitchens can also be outsourced. 
5. Online ordering system: Many ordering systems allow customization based on features required, and these can range from ₹4,000/year to around ₹6000
6. Customer acquisition and social media presence: Based on paid and organic marketing, this may cost around ₹40,000-80,000 per month
7. Branding and packaging: As packaging is the crucial thing with cloud kitchen startups, branding across social media, food aggregators and effective packaging can cost around ₹50,000-70,000

Choosing the right Technology

With the right technology, you can streamline your operations and make your cloud kitchen run more smoothly. Here are a few things to keep in mind when choosing technology for your business:

• Order management system: An order management system (OMS) is a software that helps you track and manage orders. It can be used to track customer information, inventory levels, and delivery status. A good OMS will be user-friendly and scalable so that it can grow with your business. 
• Kitchen display system: A kitchen display system (KDS) is a software that helps you manage food preparation and cooking. It can be used to track recipes, ingredient lists, and cook times. A good KDS will be user-friendly and customizable so that it can be adapted to your specific needs.
• Customer relationship management system: A customer relationship management (CRM) system is a software that helps you manage your relationships with customers. It can be used to track customer information, contact history, and order history. A good CRM will be user-friendly and scalable so that it can grow with your business.
• Accounting software: Accounting software is a software that helps you manage your finances. It can be used to track income, expenses, and invoices. A good accounting software will be user-friendly and customizable so that it can be adapted to your specific needs. 

By investing in the right technology, you can make your cloud kitchen more efficient and organized. This will help you save time and money in the long run.

In India, the average annual cost of setting up a restaurant is almost 3x more than the set-up of a cloud kitchen, steering good entrepreneurs and food aggregators alike to jumpstart on this side of the competition. 

Regardless, it is always recommended to not follow the herd and go with the requirements your business needs to succeed. Assessing market trends, costs needed, estimating the funding required, security, profitability eventually are topics to consider before getting started on a cloud kitchen model.