Category: Insights

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Cloud

What should an enterprise consider when adopting or rapidly expanding its multi-cloud strategy?

Cloud has been in existence since 2006, when Amazon Web Services (AWS) first announced its cloud services for enterprise customers. Two years later, Google launched App Engine, followed by Alibaba and Microsoft’s Azure services. The most recent addition to the public cloud service providers’ list is OCI (Oracle Cloud Infrastructure).

As per the  Gartner 2021 Magic Quadrant, AWS is the market leader, followed by Microsoft Azure and Google Cloud Platform in the second and third positions, respectively. As cloud technology evolves, so do the customer requirements. Today, cloud adoption is one of the top priorities among C-suite executives. The Covid-19 pandemic further accelerated the need for cloud adoption as digitalization is no longer optional for organizations but a mandate. As the pandemic nears its end, there is a surge in demand for cloud services as most enterprises are increasingly leveraging it. As a result, enterprises don’t spend enough time on the “right” workload assessment. There is a possibility that enterprises might get impacted due to this sudden move to the cloud and may have to eventually exit or switch to another Hyperscaler at a later stage.

As per  Gartner’s report, 81% of the respondents said they currently work with two or more public cloud providers. It means multi-cloud is the future of cloud computing.

  1. Regional Presence – This is one of the most common requirements when selecting the Hyperscaler. Most well-known Hyperscalers have extended their global reach to tap into new markets, meet existing customer demands and adhere to regulatory/compliance requirements. Regional presence has a strong impact as enterprises would prefer being closer to their customers, abide by the compliance requirements defined by their country and offer high performant services with low latency. When planning to onboard another Hyperscaler, enterprises must ensure that it fulfils all the regulatory and compliance requirements and has a presence in the local region. Additionally, enterprises must perform a small proof of concept if switching due to latency-related reasons. Besides, they must also evaluate the connectivity options available through Hyperscaler or their Channel Partners.
  2. Best-of-Breed Services – All major Hyperscalers offer a huge portfolio of services across infrastructure, platform, data services, and AI/ML. Yet, some cloud service providers enjoy market leadership for specific services. Enterprises can go for any Hyperscaler for general infrastructure. However, large enterprises, majorly depending upon Microsoft technologies and tools, prefer Azure, as they get to leverage the Microsoft Licensing Model and ease of integration. Lastly, GCP becomes the vendor of choice among enterprises regarding AI/ML/Data services. When evaluating another Hyperscaler, enterprises must validate new and different services that are available with the new Hyperscaler. Evaluate these services for proper functionality, limitations, resource limit, and availability in the chosen region. For a Hyperscaler, all services may not be available in all the regions. Review the Hyperscaler’s roadmap and ensure that the required services will be available before the switch-over.
  3. Vendor Independence – Vendor/cloud provider lock-in can be extremely detrimental, keeping you captive for non-competitive pricing. It can also impact your agility, productivity, and growth if a cloud provider is failing to live up to the committed SLA terms and you are prevented from switching to another provider. Opting for a multi-cloud strategy early in the cloud journey would help enterprises avoid getting locked into such vendor dependence. There are different models today, like using generic services from one Hyperscaler and specialized services from another and using one Hyperscaler for production workload and another for disaster recovery. Enterprises should ensure that the applications can work across different clouds before finalizing the strategy, especially for stateful applications.
  4. Infrastructure Performance – Every Hyperscaler has built its environment using different virtualization technology called a hypervisor. While AWS uses Xen hypervisor for the old generation and Nitro Hypervisor for the newer generation, Oracle Cloud Infrastructure uses Xen technology, and Google Cloud Platform uses KVM. In addition, their services are hosted on the latest and greatest hardware stack. There is a possibility that some workloads may perform slightly better in one environment than another due to abstraction overhead or the underlying new hardware. Also, some Hyperscalers offer different hardware in different regions, so enterprises need to assess this based on the application they plan to deploy in a region. As a recommendation, enterprises can perform a Proof of Concept (PoC) by running the same application across different Hyperscalers. This may require running the same workload in the new setup for a specific duration and closely monitoring it. Try simulating the same use case, setting up alerts, gradually increasing the use-case traffic, and monitoring the application behavior. Based on the PoC results, host your applications across multi-clouds.
  5. Niche Hyperscaler Credibility – There are options beyond the major Hyperscalers that might fit into enterprise niche needs. It is critical to validate these niche vendor’s credibility during the evaluation phase. Enterprises can make use of third-party services to ensure vendor credibility. Industry analysts like Gartner, IDC, Forrester, etc., regularly publish vendor-oriented reports. Look out for their evaluation of the Hyperscaler in Magic Quadrant, Forrester Wave, etc. The Hyperscaler must have a long-term strategy, plan, and roadmap.
  6. Migration Tools/Services – For an enterprise planning to onboard another Hyperscaler, it becomes equally important to select the right tool to migrate the workloads from on-premises to cloud or from one Hyperscaler to another. For this reason, evaluate if the new Hyperscaler provides any tools or services for workload, database, and data migration to their environment.

    For example, every Hyperscaler has a set of tools for workload migration, database migration, data migration, data transformation, etc. AWS provides Application Migration Services for workload migration, AWS Database Migration Service for database migration, AWS DataSync for data migration from on-premise to AWS. Similarly, Google Cloud Platform has tools to make the data and workload migration very seamless – Migrate for Compute Engine for workload migration from On-Premise to GCP, AWS/Azure to GCP (Hyperscaler to another Hyperscaler), Migrate for Anthos for workload transformation from GCE to GKE, AWS EC2/Azure VM to GKE (one Hyperscaler to another Hyperscaler) or Storage Transfer Service for Cloud, etc. Likewise, Azure has Azure Migrate for workload migration, Azure Database Migration Service for databases, etc.

  7. Pricing, FinOps, and Cost Optimization – Service consumption charges are always a top priority for a CFO. Enterprises are constantly exploring different options to reduce their operating expenses. They expect Hyperscalers to recommend options to reduce cost, display granular usage and report service-wise breakdown. Tools/platforms like CloudCheckr, CoreStack (FinOps), Flexera CMP, etc., offer recommendations and insights for cost optimization. These products/tools use an advanced ML-based approach to the past (historical) data to recommend the next course of action. Cost optimization plays a vital role in deciding the multi-cloud strategy.
  8. Support Model, KPI, SLAs – Few enterprises may also want to add another Hyperscaler since the available Hyperscaler cannot meet the required SLA or they don’t offer well-defined KPIs. These are a few key measurable parameters for an enterprise to discuss with their Hyperscaler before deciding. It helps in evaluating the cloud partners, measure the project progress and its impact on their business. Evaluate the benefits of each support model available through the Hyperscaler. Go for the one that best suits the enterprise’s requirements. Check different SLAs, KPIs, monthly/quarterly reports, etc.
  9. SME & Skills Availability – For going multi-cloud, an enterprise will require guidance at every stage, like identifying the right workloads, right Hyperscaler(s), right monitoring and management tools, right skills, etc. For these reasons, an enterprise must have or engage an expert or a system integrator (SI) who can advise, help the team and guide them through the multi-cloud journey. In addition, define a path for the internal teams to learn new skills and get certified

As the public cloud offerings and services expand, enterprises have multiple options available at their disposal. They can decide and pick up the most suitable Hyperscaler for their workloads. Workload mobility across clouds will be a general pattern based on service cost, application latency, and/or need for additional resources. Though it may not be ideal for critical production-grade workloads/applications with regulatory and compliance requirements, it is most suitable for other workloads like product testing, scalability testing, code development, etc., which caters to around 30%-40% of the workloads. Such workloads can make use of this capability to achieve cost optimization.

Earlier, due to a limited number of cloud service providers, enterprises had to worry about service outages, vendor lock-in, delays in problem resolution, vendor insolvency, etc. But with the blooming Hyperscaler eco-system, enterprises are flooded with choices. This leads to challenges in effectively managing, monitoring, securing, and optimizing costs in a multi-cloud environment. However, enterprises can use multi-cloud management solutions from vendors like IBM (Cloud Pak), Micro Focus (Hybrid Cloud Management X), Flexera (Cloud Management Platform), Scalr, ServiceNow (ITOM Cloud Management), etc. to ensure seamless operations.

A multi-cloud strategy also demands well-defined governance. Otherwise, it may increase the operating costs due to ignorant individuals or poor control mechanisms. An inefficient governance (control mechanism) may lead to underutilized and zombie resources, consuming money in the cloud. It is recommended to set up a central body responsible for managing the cloud resources and ensuring proper governance. Creating a self-service portal with proper workflow is a good approach to managing the cost and handling mismanagement.

Today, we are already consuming “serverless” services from cloud service providers, but, in the future, we may have a new business model where the enterprises pay for the services and forget worrying about where exactly it’s hosted. In the current product market, acquisition is a common strategy adopted by companies to expand their customer base, add unique services to their portfolio, and/or enhance their capabilities. Tomorrow, the trend may continue among the Hyperscalers too. Who knows what’s next in the technology roadmap?

Categories
Operational Excellence

Combining the power of RPA and AI to keep customer experience unharmed during network outages

Leverage RPA and AI to build and implement a proactive two-way Conversational Framework to reduce OpEx, boost agent productivity and improve NPS

According to recent statistics, 30% of the service providers’ contact center calls are network outage related. Their inability to predict these outages on time and provide prior information to the customers results in contact center call spikes, customer dissatisfaction and a low NPS score. This also increases the OpEx for contact centers and may lead to a reputational loss for service providers.

To overcome these challenges and improve NPS, service providers must create a central Intelligent platform capable of orchestrating seamless conversation between the contact centers and customers. This is established by implementing a “Two-way conversational Framework”. The steps involved are:

  • Step 1: Auto-identification of outage information
    Build a standardized process to identify relevant outages in the network monitoring systems. Integrate them with an outage monitoring dashboard for BOT to auto-extract outages and store them in a central database.
  • Step 2: Schedule notification
    Perform automated validation and intelligent scheduling to send proactive notifications to the impacted customers in a well-organized structure.
  • Step 3: Notify and engage with customers using a Conversational AI BOT
    Send proactive notifications, and if the customer has additional queries, the bot can engage in a conversation using the conversational AI


Conversational AI Bot orchestrates bi-directional communication and provides seamless customer experience during common network outages.

Categories
Operational Excellence

Improving the efficiency of your Field Service Workforce

Leverage machine learning to eliminate blind dispatches and improve the first-time fix rate (FTFR)

Field Technicians are the face of your service organization, and it is imperative to equip them with the right tools and knowledge to handle any field challenges. With efficient management and empowerment of technicians, your organization can deliver fast, effective, and efficient services to customers.

A business should strike a balance between the speed and accuracy of on-site customer requests to increase the productivity of technicians and improve customer satisfaction. But, in reality, technicians are frequently not able to deal with customer problems on time and are forced to make multiple trips to the client location due to process inefficiencies. Thus, instead of servicing new customers or optimizing current customer relationships, technicians invest valuable time and resources in non-revenue-generating activities.

Today, 70% of field technicians visit sites without prior information about the nature of the problem, issue location and solution recommendation. It leads to repeated dispatches, longer resolution time and high customer churn.

Going digital is the cornerstone of success for a modern services organization. Adopt the ‘AI-Powered Field Service Framework’ to optimize field services and increase technician productivity. The framework encompasses three vital components to achieve a higher First Time Fix Rate (FTFR) and reduce Mean Time to Resolve (MTTR):

  • Fault Location Classifier– Predicts the fault location and sends email/SMS notification via mobile app to technicians
  • Recommendation Engine– Suggests guided actions and next best resolution steps to improve technicians’ efficiency
  • Technician Dashboard– Provides a one-stop view of all dispatches and actionable insights to technicians


70% of field technicians visit the sites without prior information about the problem leading to repeated dispatches, longer resolution time and high customer churn.

Categories
Digital Customer Experience Insights

Using AI to understand how your customers feel

Predict Net Promoter Scores and identify whether your customer is potentially a promoter, neutral, or detractor. Take corrective actions timely to improve customer service.

Customers today expect a seamless and hassle-free interaction with their service providers. A dissatisfied and frustrated customer will quickly opt to switch. Thus, for the service provider, it becomes very crucial to understand the customer experience and promptly take corrective measures if it lags. One key metric to understand this is using the Net Promoter Score (NPS). It provides customer loyalty and satisfaction measurement by asking customers how likely they are to recommend your product or service to others on a scale of 0-10.

To capture NPS, service providers share the survey forms with their customers. But do customers respond to such surveys? Research shows that only 15-20% of customers respond to the NPS survey after their interactions with customer support. Does it mean the service provider should not take any action for the remaining 80-85%, assuming they would have a good experience? There is a high possibility that a customer not satisfied with the service would have already decided to opt out without taking any effort to respond to the survey.

Most innovative service providers are trying to address this problem with a machine learning (ML) approach.

Fig: Key steps towards building ML Model for CSAT Prediction and Improvement


NPS provides customer loyalty and satisfaction measurement by asking customers how likely they are to recommend your product or service to others

Categories
Operational Excellence

Turn your network issues into customer delight

Leverage automation strategies to streamline the Trouble to Resolve (T2R) process, providing customers with quick resolution and greater satisfaction

TM Forum, a global industry association for service providers and their suppliers in the telecommunications industry, has a business process framework -eTOM’s (Enhanced Telecom Operations Map) Trouble to Resolve (T2R) process. It reveals how to deal with a trouble (problem) reported by the customer, analyze it to identify the root cause of the problem, initiate resolution to meet customer satisfaction, monitor progress and close the trouble ticket.

Most Service Providers follow the eTOM T2R process, however, they encounter key challenges that affect the overall T2R operational efficiency and increase the OPEX.

  • Multiple siloed systems to complete a network event’s lifecycle leads to high manual effort and increased OPEX
  • Difficulty in identifying the right impact of a network event-
    • No proper tools for auto-identification & prioritization of critical events that would cause major business impact
    • Resource wastage: Network Operation Centre (NOC) tends to spend a significant amount of time handling huge volumes of
      alerts
  • Difficulty in meeting business KPIs due to unavailability of fully integrated systems and automated processes

Service Providers in the connectedness industry must develop an effective strategy for integrating the systems and bringing end-to-end automation to the T2R process flow. The majority of service providers have a basic level of automation, however, there is a huge scope for complete lifecycle automation. This Insight showcases an effective approach for implementing end-to-end automation of network event lifecycle from event creation to resolution. The approach is based on the implementation experience of leading service providers at multi-geographic locations.

“According to a report by McKinsey, many service providers have complex fundamental processes with multiple system integrations and are labor-intensive and costly. Leveraging digital technologies to simplify and automate operations makes them more productive and results in a significant cost reduction of up to 33%.”

Categories
Operational Excellence

Steering data migration, powered by RPA

Leverage RPA based Automation Framework to accelerate data migration and improve accuracy

Data migration involves moving data between locations, formats, and applications. This need is on the rise due to ongoing trends such as mergers and acquisitions (M&As), migration of applications to the cloud, and modernization of legacy applications. However, the execution of data migration using traditional methods is not at par with the increasing frequency!

According to Gartner, 50% of the data migration initiatives will exceed their budget & timeline by 2022 because of flawed strategy & execution. Most of the service providers in the connectedness industry adopt the traditional approach for data migration that involves three broad steps: migration planning & preparation, establishing governance, and execution.

Service providers follow the fundamental extract, transform, load (ETL) data migration execution methodology, which is full of challenges. It entails high cost and time due to mock runs and testing for each module. Moreover, it involves manual efforts, which leads to a lot of re-work due to errors and causes fallouts due to data integrity issues. Also, ramping up and down the teams is difficult.

To overcome these challenges, an RPA based automation framework for data migration execution could be an effective approach. The framework encompasses components such as:

  • Smart processor: Identifies data quality & integrity issues in the source data at a very early stage
  • Automation bot: Performs migration/upgrade by extracting & updating data at various layers of the application
  • Fallout management mechanism: Automates the fallout handling, i.e., Fix data quality & integrity issues in CRM, inventory systems, etc.

” According to Gartner, 50% of the data migration initiatives will exceed their budget & timeline by 2022 because of flawed strategy & execution.”

Categories
Software Intensive Networks

Redefining Virtual Network Function (VNF) Testing

Creating an effective and portable VNF testing framework with end-to-end automation

More service providers in the connectedness industry are rapidly introducing new services online, to enhance customer satisfaction and provide better service. However, service providers face several testing challenges in terms of scope, complexity, and frequency during the delivery process. Almost 40% of the service provider’s time and efforts are consumed in testing activities. Virtual Network Function (VNF) Testing in the connectedness industry has become complex, costly and time- consuming. To address the testing challenges faced by service providers in virtualized environments, existing testing methods and processes must be revised and shifted to a new testing model.

This Insight encompasses the key elements that can help service providers to create an effective and portable VNF testing framework with end-to-end automation. The key benefits include rapid test development and maintenance, faster product launch, improved product quality, and differentiated product delivery.


Accelerate service rollout time by 78% with redesigned VNF testing framework.

Categories
Cloud Insights

Prevent your data lake from turning into a data swamp

Build a light-weight efficient data lake on Cloud

The future of Service Providers will be driven by agile and data-driven decision-making. Service Providers in the connectedness industry generate data from various sources every day. Hence, integrating and storing their massive, heterogeneous, and siloed volumes of data in centralized storage is a key imperative.

The demand for every service provider is a data storage and analytics solution of high quality, which could offer more flexibility and agility than traditional systems. A serverless data lake is a popular way of storing and analyzing data in a single repository. It features huge storage, autonomous maintenance, and architectural flexibility for diverse kinds of data.

Storing data of all types and varieties in central storage may be convenient but it can create additional issues. According to Gartner, “80% of data lakes do not include effective metadata management capabilities, which makes them inefficient.” The data lakes of the service providers are not living up to expectations due to reasons such as the data lake turning into a data swamp, lack of business impact, and complexities in data pipeline replication.

Categories
Product Engineering

Move to technology-driven smart policing

Leverage predictive analytics to reduce crimes and burglaries by 30%

Today, the crime rates in most parts of the world are high, despite taking necessary measures. Reports by FBI reveals, “3.9% increase in the estimated number of violent crimes and a 2.6% decrease in the estimated number of property crimes when compared to 2014.” Due to this, the police forces globally are under tremendous pressure to leverage technologies such as predictive analytics, to draw insights from the vast complex data for fighting the crimes. It not only helps in preventing robberies and burglaries but also aids in better utilization of the limited police resources.

Fig. Predicting crime by applying analytics on data feeds from various sources

As per studies conducted by the University of California, crime in any area follows the same pattern as the earthquake aftershocks. It is difficult to predict an earthquake, but once it happens, the following ones are quite easy to predict. The same is applicable when it comes to crimes in any geographical area. Combinational analysis of the past crime data and other influencing parameters help in predicting the location, time, and category of crime.


With the increasing crime rates, globally the police forces are under tremendous pressure to leverage technologies such as predictive analytics to draw insights from the vast complex data for fighting crimes.

Categories
Software Intensive Networks

Making smarter network investment decisions

Build an open-source network capacity planning framework to accelerate the network decisions by 3X

The network complexity of the service providers in the connectedness industry is ever increasing. The introduction of 5G on top of legacy 2G and 3G networks, coupled with increasing customer expectations, imposes tremendous pressure on the service providers. They often struggle with complex networks, dissimilar data, and inefficient visualization of the inventories, which impacts network capacity planning. Some of the main challenges in conventional network capacity planning are:

  • Increasing inefficiencies in network planning due to rapid network expansions and complex networks
  • Difficulty in visualizing and monitoring the networks and their components due to the rapid expansion of networks
  • Difficulty in consolidating data, as the service provider’s network inventory data is scattered and retrieved from different types of vendor network equipment
  • Increased licensing, hardware, and customization costs for the service providers who use COTS products for network visualization

These challenges impact the service provider’s operations leading to ineffective network capacity planning, delays in new network design and rollout, and inefficient network and resource utilization.

While many service providers depend on COTS products to address these challenges in the unified visualization and capacity planning process, it is recommended to consider an open-source approach that enables efficient and cost-effective capacity planning.

Fig. Build an open-source framework to ease and accelerate network capacity planning decisions


The introduction of 5G on top of legacy 2G and 3G networks, coupled with the increasing customer expectations imposes tremendous pressure on the service providers.