Article | February 26, 2020
More businesses than ever are using cloud services to store data and run their essential business applications.If your business is using Google Drive, Dropbox, or other cloud platforms like HubSpot, then you may assume that all of your data is secure.However, as secure as cloud-based platforms should be, they are also where all of your sensitive data is being stored. While in house data environments will never be as secure as cloud storage, there are still vulnerabilities.From user lapses to data transference, there are key areas where you should be prioritizing improvements to boost the security inherent in the cloud.Here are the four best ways that you can protect your cloud servers and the data that you store within them.
Article | February 26, 2020
Zoom Video Communications is a remote conferencing services company. Many organizations use their product for its video conferencing, online meetings, chat, and mobile collaboration to stay in contact with remote colleagues, customers, partners, etc. Zoom’s value and use has skyrocketed over the last several weeks, mostly due to the COVID-19 pandemic. Between December 2019 and March 2020, they have gone from 10 million users per day to 200 million. By clicking “Join,” we are trusting that Zoom will provide the necessary security to protect our personal information and the content of our Zoom sessions.
Article | February 26, 2020
Nowadays, most businesses across the globe are leveraging cloud technology and its advanced natives, such as machine learning and artificial intelligence (AI), to pave their path towards success. As per Analytics Insight, Machine Learning would record revenue of US$80.3 billion by the year 2023, with a CAGR of 33.6% from 2020. This article, explains the effects of integrating machine learning in cloud computing and how it helps make future-proof decisions, create innovative solutions, and drive organizational success.
Machine Learning (ML) is one of the subgroups of artificial intelligence. It helps infer effective business decisions to attain digital presence in the market by providing valuable insights from disparate organizational data.
“AI works effectively because extremely high volumes of high-quality data drive it. So, identifying and collecting data, and making that data meaningful, is an integral step in the machine learning process.”
Jaime Punishill, CMO at Lionbridge said during an interview with Media 7.
Importance of Merging Machine Learning in the Cloud and Some of the Enterprise Case Studies
Machine Learning utilizes the resources of the cloud to optimize the industrial sectors. On the other hand, cloud computing provides scalable and cost-effective resources to leverage a considerable amount of data for processing to run ML-enabled systems efficiently. As a result, combining machine learning with cloud helps to optimize the capacity of both.
Let us quickly run through some of the stimulating effects of merging machine learning in cloud computing.
Detect and Protect Business Ecosystem
According to the IDG Survey, almost 80% of IT security leaders believe their organizations are susceptible to cyberattacks despite augmented investments made on IT security to accommodate the advanced distributed IT framework. Today, most business leaders are aware of security threats because of various cloud-native and web-based modern applications. In addition, by merging machine learning in cloud computing, companies are developing intelligent security applications to protect organizational data from vulnerable malpractices.
How an Advanced Approach by McAfee Endpoint Security 10.5 Helped a Leading Insurance Company Safeguard its Sensitive Data?
For an insurance company, securing their customers’ sensitive personal data without compromising customers’ experience is imperative. And there lies the primary challenge of any IT security measure, where the traditional silos approach doesn’t work anymore. In this case, you need robust and automated solutions powered by advanced technologies, such as machine learning. Eventually, to solve this, McAfee deployed McAfee Endpoint Security version 10.5, and as a result, the company’s IT help desk started receiving 80% lesser tickets. In addition, the insurance company is now enabled with cloud-based Real Protect machine learning behavioral analysis technology that helps enhance overall security capabilities and stand against the rising risk of security breaches.
Cloud-Based Cognitive Technology
Integration of machine learning in cloud computing makes cloud data the source of ML algorithms for cloud-enabled businesses. ML algorithms utilize the cloud data and modify the cloud archetype to cognitive computing. Cognitive computing technology is one of the emerging trends. Businesses are inclined to deploy cloud-based cognitive technology because it helps to grow their revenue, enhance operational efficiencies, and cater to real-time use cases in a cost-efficient manner.
How Does IBM’s Watson Help Banking and Financial Sectors?
IBM’s Watson is one of the ideal examples of cognitive computing. Assisting the banking and financial sector, IBM’s Watson is a question-answering system, which receives unstructured data in the form of questions and provides humanized answers. Furthermore, Watson can differentiate its limits and route it to respective resources whenever human intervention is required. For example, IBM Watson has helped the Royal Bank of Scotland develop an intelligent assistant proficient in handling 5000 queries in a single day.
Predictive analytics uses predictive models that are typically machine learning algorithms helping to make accurate predictions of your business outcomes. Playing a crucial role in cloud computing helps to optimize cloud infrastructure, take proactive measures to envisage downtime or infrastructure performance issues. Further, predictive analytics play an intrinsic role in merging the structured and unstructured data from diverse and distributed networks in multifaceted cloud environments.
How WNS Helped a Globally Acclaimed Hotel Chain to Retain its Timeshare Members by Leveraging Predictive Analytics?
For hotels, it is crucial to retain their timeshare members. However, experiencing a high attrition rate among its timeshare members, followed by nonrenewal or membership cancellation, started denting the hotel chain's reputation.
WNS recommended an approach based on the principles of the predictive analytics approach for a better understanding of the behavior of the members. They created complete member profiles based on demographics, duration of membership, and transactional patterns. Implementing statistical analysis to generate probable attrition scores for every member helped to divide members into high, medium, and low attrition groups. Further, identifying the attrition drivers by deploying a logistic regression model was also a part of this exercise. All these drivers helped to predict the members' behaviors in the future.
As a result, the insights empowered the hotel chain to employ marketing campaigns targeted towards the specific audience with special promotional offers to arrest the attrition of their timeshare members.
Internet of Things (IoT)
Internet of Things is described as a network through which multiple devices (read 'Things') are interconnected via the internet. IoT is adapted by technology experts worldwide. Various industrial sectors embrace the utilities of IoT devices. Further, when hosted on a cloud platform and leveraging machine learning in the cloud, IoT provides impactful real-time insights.
How Did Medium One Leverage Machine Learning to Enable Cloud-Connected Industrial Pressure Sensors?
The legacy industrial sensors couldn't generate automated real-time alerts to monitor, detect irregularity, or forecast key events. Medium One’s environment helped the customer in making data sensible to unlock its hidden insights. Medium one helped develop a machine learning algorithm for real-time predictions and alerts with historic cloud data. It enabled the cloud-connected industrial pressure sensors to correlate events smartly and monitor remotely.
Chatbots and Virtual Assistants
Chatbots and personal assistants are innovative examples of machine learning in cloud computing collaborations that dominate personal and corporate ecosystems. Intelligent cloud-based virtual assistants like Siri, Alexa, Cortana interact with you just like any other human being and perform several operations, as per your command. These chatbots are AI-enabled, operate on machine learning algorithms in the cloud computing framework. Further, they use natural language processing technology, predictive analytics, and sentiment analysis to learn from the inputs and engage in real-time conversations.
How Oracle Intelligent Bots Reduced Call Center Wait Times?
During the release of senior school exam results in the summer, the inquiry center of the University of Adelaide received massive traffic from the existing and prospective students. A significant part of that traffic was to enquire about their grades and admission facilities, respectively. The university appointed Rubicon Red, an Oracle cloud consultancy, to build a chatbot based on artificial intelligence (AI), machine learning, and natural language processing to meet this surged demand. The bot is supposed to handle the first line of inquiry when calls come in, relieving human agents' load. Rubycon Red leveraged Oracle Intelligent Bots to create an intelligent chatbot. The deployment was a success that resulted in 40% reduced traffic and 97% less wait time on the university's inquiry service (call center).
The merger of machine learning in cloud computing enables organizations to leverage their massive organizational data in deriving valuable data-driven insights and accurate predictions by analyzing the trends and patterns of the data. Machine Learning helps businesses to understand their target audience, automate their business processes and develop advanced products per market demand. As a result, it drives the success of a company and helps them to stay competitive.
Frequently Asked Questions
How is Machine Learning Used in Cloud Computing?
Cloud-enabled businesses can leverage the vast data and utilize advanced machine learning technology to determine, compute and predict valuable futuristic insights about their business. As a result, it helps to scale the efficiency of the cloud within a cost-effective reach.
Is Machine Learning Important for Cloud Computing?
Machine Learning leverages cloud data and derives insightful information out of it. It computes the data to provide future forecasts and helps the businesses to take necessary actions on it. As a result, adopting machine learning applications are crucial for cloud businesses.
What are the benefits of Machine Learning on the Cloud?
Businesses can experiment with their processes using machine learning on the cloud and then scale up as demand increases. Further, the pay-per-use model makes a cost-effective solution to the company without unplanned expenses.
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Article | February 26, 2020
If you don’t completely understand hybrid cloud, you’re not alone.
Organizations feel the pressure of being competitive by being innovative and adaptive due to changing infrastructure, changing strategies and changing technologies. Many businesses are looking to move their data centers to the cloud for obvious reasons like disaster recovery or increased capacity but importantly to bring in the digital transformation. Cloud has emerged as an enabler of success.
Cloud computing has evolved over the recent years to fulfil the varied needs of organizations, even as each organization realizes that it has different cloud requirements. This gave birth to Hybrid Cloud environment with both public and private cloud services.
Data is by-far the most important resource for organizations these days. This is where all the business lives. As organizations embark on strategic business initiatives to digital transformation, hybrid cloud model provides a wider data-centric view and seamless access to your data to produce insights by optimizing resource without compromising security.
Table of Contents:
- What is Hybrid Cloud?
- Why Hybrid Cloud over other cloud options?
- Gaining data insights in a Hybrid Cloud Environment
- Leveraging data to power innovation in a Hybrid Cloud environment
- In Conclusion
What is Hybrid Cloud?
Forrester research describes hybrid cloud as “One or more public clouds connected to something in my data center. That thing could be a private cloud; that thing could just be traditional data center infrastructure.” To put it simply, a hybrid cloud is a mash-up of on-premises and off-premises IT resources.
To expand on that, hybrid cloud is a cloud-computing environment that connects a mix of public cloud, private cloud and on-premises infrastructure. A key advantage to this model is that it allows workloads and data to travel between private and public clouds as demands and costs change, providing businesses with the flexibility they need.
There is not a single hybrid cloud model that works for every organization and every model should fit the unique needs of each company. By allowing multiple deployment models in a hybrid cloud, organizations are able to benefit from a seamless and secure environment which enables productivity and flexibility.
We believe in a world where you integrate public cloud with your on-premises infrastructure, and use each where it makes sense in conjunction with each other. And when we say integration, we mean true integration – across infrastructure, applications, development platforms, identity, and databases. This is what we call hybrid cloud.
- Microsoft Azure Jumpstart
Why Hybrid Cloud?
It is not usually feasible for businesses to go all in and move completely into the cloud straight away, unless they happen to be cloud-native organizations. That doesn’t mean that enterprises with legacy systems have been unable to make any headway with cloud. To get around this, they can try a mixture of public and private clouds, and combine this with hosted, collocated and on-premise infrastructure where necessary.
Hybrid cloud allows organizations to experience the advantages of both types of cloud. By spreading computational data across both resources, it is possible to optimize the environment whilst keeping everyday functions as streamlined as possible. Enterprises can make their own minds up on which type of data should be stored in a public cloud, whilst keeping any sensitive data in the private cloud. This is granting them the key element that they need: control. Access to the benefits of private and public clouds is perfect for organizations wanting to grow at the speed they need.
Hybrid solutions grant business the key element that they need: control. Control to optimize their IT investment by selecting the best-fit infrastructure for different categories of workload. Control to choose where their most critical data should reside. Control to spread their workloads across multiple platforms to avoid the risk of vendor lock-in from a single platform strategy.
Only 40% are currently able to move workloads across cloud types or providers. That means enterprises striving toward flexible use of hybrid clouds face a looming challenge, because 62% are looking to achieve portability within the next 18 months.
- CIO Quick Pulse Cloud
READ MORE: https://cloud.report/blogs/flexible-building-blocks-for-the-new-cloud-and-data-driven-world/12337
Gaining data insights in a Hybrid Cloud Environment
• Maintain the “Right” Data Placement
If your organization uses hybrid cloud, IDC highlights the “right” data placement as being critical to comply with regulatory mandates, to meet application response time, and to optimize infrastructure costs. In many cases, you must keep certain datasets within their respective countries of origin, or you must be able to move other datasets from one location to another. In either case, all your customers who access data, regardless of location, need the same seamless customer experience. By dispersing data correctly, your organization can provide faster data access for customers around the world.
And with the “right” data placement, hybrid cloud environments can provide data and data insights faster, without violating global regulations or creating content siloes.
• Develop a Holistic Picture of All Your Workloads and Data
To gain insight into your hybrid cloud environment, you need to have a complete picture of the workloads and the datasets within that environment. IDC suggests building “data maps,” which include your data, key owners, security requirements, sensitivity levels, data sources, and any other key metrics to meet your organization’s needs.
When your organization has this holistic picture, you can measure data as part of a whole set. You can also manage workloads and identify data trends that might have been siloed in the past. When all your data has been measured together as part of a larger map, you can discover new insights, identify new opportunities, and make data-driven business decisions.
• Understand the Real Costs of Your Service Options
Along with your holistic picture of workloads and data, you need to understand the real costs of your service options. What up-front costs are required? What hidden costs might be exposed later? What’s the TCO over time, and does it match the value of your data?
Cloud solutions are not one-size-fits-all, and your organization can create an environment that’s based on your needs, without added cost. As you create your hybrid cloud strategy, consider your existing costs and evaluate what options you have as you move data into your cloud environment.
• Embrace AI and Machine Learning
Forward-thinking organizations recognize that artificial intelligence (AI) and machine learning are the future of cloud data storage and management. A cloud environment with AI learns from the data that it gathers, makes predictions, and troubleshoots potential problems before they occur. IDC predicts that in the future, AI will drive self-configurable and self-healing infrastructures, enabling systems to dramatically reduce—or even eliminate—human errors.
You can also use AI and machine learning to seamlessly move data between an on-premises infrastructure and a cloud environment. In a hybrid cloud environment, seamless movement, accessibility, and connectivity are crucial. With AI attached to a hybrid cloud, your organization can manage and control data like never before.
READ MORE: https://cloud.report/blogs/how-to-maximise-business-objectives-using-hybrid-cloud-architecture/12360
Leveraging data to power innovation in a Hybrid Cloud environment
1. Control and Secure Your Most Valuable Asset environment
It’s impossible to gain insights from information that you don’t know that you have. In a hybrid cloud environment, you can easily keep your data visible and accessible without losing security. You can establish full data visibility across multiple clouds, and you can implement data protection policies that extend beyond your data center boundaries. With a hybrid cloud environment, you can control and secure your data, wherever it lives.
2. Access and Share New Data Insights
Infrastructure analytics and machine learning work across hybrid cloud environments to help you better understand your data. These data insights include performance, capacity, and availability of each dataset. With this kind of visibility into your organization’s data, you can:
• Decrease troubleshooting time.
• Optimize resources and reduce capital expenditures and operational expenditures.
• Manage costs.
• Implement showback and chargeback reporting.
Data visibility and insight go hand in hand. With integrated data management in a hybrid cloud environment, your organization can eliminate data bottlenecks and share insights to drive new business opportunities and growth.
3. Accelerate Application Delivery and Embrace Digital Transformation
Many enterprises are turning to DevOps as the best way to deliver new software features, services, and applications more quickly and with higher quality. Hybrid cloud environments facilitate DevOps practices by simplifying data services and by connecting your data, in the cloud or on the premises. By bringing developer and operations teams together, DevOps can reduce friction and put your company on a path to continuous integration and seamless delivery.
4. Empower Employees to Make Data-Driven Decisions
In a hybrid cloud environment, your organization can place data where it provides the greatest value, whether it’s a corporate data center, a production facility, a public cloud, or at a cloud service provider. A hybrid cloud also enables you to move data easily as your requirements change. With integrated and accessible data, employees at every level can make data-driven decisions. Working with data in a hybrid cloud environment is all about maximizing the value of the data. That means improving the customer experience, making information more accessible to stakeholders, and identifying opportunities that lead to new markets and new customers.
5. Accelerate Innovation
When your data is mapped and accessible in a hybrid cloud environment, your employees can discover new ways of looking at data to improve performance management, gain new business intelligence, and spot potential customers. Your company can move quickly to advance new ideas from concept to production while reacting faster to market changes. With your data connected through the hybrid cloud, you’re strategically positioned to unleash your data and to grow your business.
Public and private clouds have certain limitations, which is where Hybrid Cloud model shines. Hybrid Cloud approach allows the power to integrate your current IT Infrastructure with new cloud workloads. This leads to seamless access, wider data view and deriving new data insights for faster innovation while adhering to the ever changing tech scenario.