Cloud Computing in the Healthcare Industry

Cloud Computing in the Healthcare Industry
Data is omnipresent; the healthcare industry is no exception to this fact. As the Cloud takes over technology at an exponential rate, there is a vast amount of potential for Cloud computing in the healthcare industry. Since the Cloud provides on-demand computing, it has rapidly become the go-to tool, especially when healthcare institutes and hospitals require deploying, accessing, and handling network information at the drop of a hat.

As healthcare regulations push the healthcare industry towards the need to look for better storage, collaboration, and data sharing techniques, there is an imminent need to prevent any data loss. Since electronic medical records (EMR) are prone to data loss, the Cloud has proved to be a reliable, secure medium of data storage; this can thoroughly be ensured by defining security parameters with Cloud providers at the time of undertaking the services.

Moving to the Cloud for a Better Tomorrow
The Cloud provides endless benefits to the healthcare industry. While many hospitals and institutes have already moved to the Cloud, many others are in the process of implementing their facilities to become available. The Cloud facilitates better collaboration while giving access to secure storage and providing remote access to stored data. Further, there is a remote conferencing facility available, which can update a patient’s health condition in a matter of minutes, all leading to considerable time-saving.

To perform all tasks related to storage, data manipulation, transition, and collaboration internally, a healthcare unit would need to invest heavily in infrastructure and the resources for maintenance. However, this process would mean massive costs and dubiousness around the full utilization of resources. However, with the use of the Cloud, all these thoughts can be put at bay, since everything is performed at a fraction of the total cost, leading to enhanced efficiency.

As soon as providers move into the Cloud, traffic gets channelized to the Internet, instead of the data center. This way, there is a lesser load on internal servers, as the availability and bandwidth free up drastically.

Use of Hybrid Cloud Services
Not many healthcare providers are too keen on making the use of public or private Clouds. Instead, such institutions make use of hybrid Cloud services. Hybrid cloud services make use of on-premise private data-center and third-party public Cloud service, which helps create a hybrid setup between different servers. This way, providers have an option to choose the apps and resources they would like to utilize within their local data center as well as in the Cloud.

Top Three Benefits of Cloud Computing
We have established the importance of the Cloud in the healthcare industry. While security is a significant concern with the storage of the data in the Cloud, nevertheless, it is not a deciding factor at the same time. Here are the top three benefits which highlight the usage of the Cloud services in the healthcare industry, and why it has become such an integral part of the technological universe:

Improved Patient Care Services: Patients can benefit immensely, as different health services are moved from a physical environment to a digital environment. Through the Cloud, doctors and patients can initiate virtual sessions, which enable the use of enhanced patient care services. Users can share, view, and store their records in the Cloud, while doctors can archive and access them remotely as well. By placing documents in the Cloud, different healthcare centers can access patient data with the press of a button, without having to bother about endless paperwork and delayed treatment issues.

Freeing up Essential Resources: The Cloud has been aligned to provide exceptional support in operational, administrative, and HR functions. It can ensure an outstanding quality of services when it comes to scheduling, referrals, sourcing files, inventory management, as well as perform many other types of behind the back actions. All this can be achieved at relatively low costs, which makes the process all the more efficient and lucrative for the end users. In other words, through the use of the Cloud, health institutes can expect better resource allocation, at a fraction of the cost.

Paving the Path from Administration to Analytics: Since a significant part of analytics is about data storage and manipulation, the Cloud comes in handy when tasks are more analytical, as compared to administrative. In fact, Amazon’s Web Services (AWS) are currently in the process of analyzing genomic data, which can help medical practitioners take a deeper dive into the causes of breast cancer and ovarian cancer.

Given its collaborative nature, the Cloud is becoming the future of tomorrow, and the healthcare industry shall be no exception to this fact, not now, not ever.

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Interop ITX 2018

Interop ITX
Event Details: Interop ITX combines a trusted Conference program with a vendor-neutral Business Hall and lots of networking events. An event for the IT community, its an opportunity to learn about technologies and solutions outside your current ecosystems. Featuring 130+ sessions via a mix of hands-on, panel, and speaker-led sessions, build a custom educational experience suited for you! Find topics at all levels of IT competency, from business intelligence to infrastructure modernization and cloud strategies to security best practices. Plus, dig into hot technologies like AI and IoT. Don’t miss out on this once a year chance to learn, network and drive your career and business forward.

[Know more about the Conference]

About Idexcel: Idexcel is a Professional Services and Technology Solutions provider specializing in Cloud Services, Application Modernization, and Data Analytics. Idexcel is proud that for more than 20 years it has provided services that implement complex technologies that are innovative, agile and successful and have provided our customers with lasting value.

Anand Allolankandy – (Sr. Director Technical Sales & Delivery at Idexcel) will be attending this event. For further queries, please write to anand@idexcel.com

How Big Data Is Changing the Financial Industry

How Big Data is changing the Financial Industry
Big Data is the talk of the town these days; not only has it ushered in the next generation of technology, but it has also modified the way businesses and financial institutions are performing their day to day activities.

Financial institutions are always on the lookout to enhance their day to day operations while keeping their competitiveness intact. Let’s have a quick look at analyzing the top 5 financial trends which are quickly taking over the financial industry and paving the path for modernizations.

Strengthening Financial Models: Data is prevalent in every industry. Financial institutions such as banks, lending institutions, trading firms, etc., produce tons of data on a regular basis. To manage such voluminous data, there is an imminent need to bring into operation a data handling language which is equipped to handle, manipulate and analyze massive volumes of information – this is where the role of Big Data comes into the picture. Financial institutions often work on different business and financial models, especially with respect to approving loans, trading stocks, etc. To make efficient working models past data trends need to be taken into consideration. The better the data relativity, the stronger the model and lesser would the risks involved. All such strategies can be derived from the use of Big Data, which in turn becomes an effective method to drive data-driven models through different financial services.

Enhanced Data Processing and Storage: Technology will never stop growing. Since the aforementioned has become an inseparable part of every organization’s life cycle, the data generated by daily operations gives way to the need of the hour storage and data processing. If one talks about the use of Big Data, the name is a clear giveaway in itself; it encompasses the use of the language, which means storing data in the Cloud or on other shared servers becomes a cinch. Thus distribution and processing come as a byproduct of storage capabilities. Cloud management, data storage, and data processing have become the words to reckon with, as more and more organizations are considering opportunities within the technical world.

Machine Learning Generates Better Returns: Financial Institutions deal with customer data on a day to day basis. Not only is such information critical, but very valuable, since it gives insights into the daily functioning of the bank. Considering the sensitivity of the data, there is a pressing need to evaluate the stored data, and protect it from fraudulent activities, while ensuring the risk is reduced drastically. Machine Learning has become an integral part of modern fraud prevention systems, which help to enhance risk management and prevent fraudsters from entering into protected domains.

Blockchain Technology: When customer data is at the fore, and financial transactions are at risk, Anti-Money Laundering (AML) practices become a topic of deliberation. Many people are beginning to give considerable importance to Blockchain technology within the financial industry forum. Blockchain possesses the ability to decentralize databases, and further link separate transaction information through code. This way, it can secure the transactions and offer an extra layer of security to the organizations dealing with sensitive data.

Customer Segmentation: Banks are always under pressure to convert their business models from business-centric to customer-centric models; this means that there is a lot of pressure to understand customer needs and place them before business needs to maximize the efficacy of banking. To facilitate the shift banks need to perform customer segmentation to be able to provide better financial solutions to their customers. Big Data helps perform such tasks with simplicity, thereby enhancing groups and data analysis.

There is no denying the fact that Big Data has increasingly taken over various industries in a short matter of time. The higher the opportunities being exploited, the better the results being displayed by banks and other financial institutions. The idea is to expand efficiency, provide better solutions, and become more customers centric. All the while decreasing the tangent of fraud and risks within the financial domain.

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How Cloud Migration will help Boost Security and Compliance

How Cloud Migration will help Boost Security and Compliance
Although the adoption of cloud services is becoming increasingly popular in the past few years, many organizations are still skeptical of migrating to the cloud due to security concerns. This outlook tends to emerge from a lack of exposure to the emerging potentialities of the modern cloud. However, the case has become precisely opposite—firms, no matter how small or large, can benefit immensely from cloud migration when regarding stronger security and compliances.

Cloud providers reassure organizations of seamless and hassle-free cloud migration and ongoing maintenance; they make the security and protection of third party data their priority because their reputation highly depends on the kinds of services they provide. Once this goodwill suffers a blow, their company sustains a considerable loss, which is certainly not favored.

The cloud providers render security with the help of following measures:

Safekeeping the Data
Cloud providers are not just any organizations; they have grown considerably and have become among the wealthiest companies in the world. Security concerns come to them not as a challenge, but rather as an opportunity. These companies have a highly skilled team of professional IT engineers that are capable of tackling any security danger that may occur. Take for instance the most prominent cloud provider—Amazon. Amazon’s security parameters are well above the average reach of hackers. Amazon and other cloud providers take protecting infrastructure and customer data as their top priority. They apply a significant portion of their budget to meet and often go beyond security expectations. Companies such as Amazon go through a series of exercises that ensure the protection of physical infrastructure and systems.

Shared Responsibility Model
A model that is implemented at the organizational level is the Shared Responsibility Model in which a cloud infrastructure provider is responsible for maintaining the physical security of its data center, including building access, network and server hardware, as well as monitoring the hypervisor in charge of the virtual machines. On the other hand, the customer is responsible for securing operating systems, applications, and data running on cloud accounts. This co-operation is established when both sides are happy and comply willingly. The benefit is mutual, thus, this model is generally upheld. With its implementation, the cloud providers render best practices for controlling access and limiting network exposures which result in a secured infrastructure.

Supply of Personalized Tools
Typically, cloud providers supply tools that complement cloud-based security management tools to help the organization defend their virtual environments. Take, for instance, Amazon Web Services (AWS) CloudTrail; it provides visibility into the actions being taken by both legitimate users and bad actors operating in the cloud environment and acts as an active vigilante for the entire operation. Other security tools such as firewalls, file integrity monitoring solutions, and centralized logging also remain functional and works together in conjuncture with cloud tools. Thus, it all adds further layers of security that are purposefully built for strengthening and monitoring the environment.

Besides security measures, cloud computing is also highly compliant with the modern day needs of an organization. They focus on cost-effectiveness and the ease of use while keeping in mind the procurement of untainted security measures.

Reduced Business Expenditure
From its advent, cloud computing engineers have strived to seek the betterment of the existing platform services. The financial aspect in organizations is of great importance to the engineers too. Therefore, a traceable shift can be seen in cloud computing as far as reducing cost is concerned. Cloud computing is much more affordable than a traditional data center as it works on a pay-as-you-go model. The building, maintenance and retrieval of data in conventional terms is costly and messy as opposed to cloud computing. Cloud computing uses real-time extraction that takes seconds to locate the data, while any modifications can be done without any harm to the existing data. The labor-force employed and time consumed in cloud computing is a lot less than traditional data centers which result in a more cost-efficient solution for the business.

Greater flexibility
Cloud computing enables organizations to become more agile and flexible through a variety of benefits. The cloud allows businesses to expand their infrastructure without any evident disturbance elastically. Organizations can instantaneously start using systems and applications on newly acquired cloud space without having to worry about the organizational insecurity. Instead, the human resource can work on their business strategies. Even for the IT professionals, who manage these clouds, their efforts can be oriented to other more strategic initiatives instead of a web of data complexity.

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Understand How Artificial Intelligence and Machine Learning Can Enhance Your Business

Understand How Artificial Intelligence and Machine Learning Can Enhance Your Business

Automation is the key to success; every company is expanding on this domain’s expertise, as organizations take on a more global approach. Given the problems of decision making, learning, and the need for adaptability when understanding data, data scientists introduced the concept of Machine Learning within the realm of Artificial Intelligence. These practices have been able to bring about a radical change in modern business efficiency.

Artificial Intelligence is commonly a platform which performs tasks intelligently, without incurring the need for human intervention. On the other hand, Machine Learning is an exclusive part of the Artificial Intelligence world, which encapsulates the know-how and the logic behind making the concept of Artificial Intelligence a real success story. Through the use of Machine Learning, machines can be taught to work more sensibly, thereby allowing them to recognize different patterns and understand new circumstances with ease.

Machine Learning has come to be used extensively, especially when it comes to providing analytical solutions to the world of consumers and technology. Through large systems of data, Machine Learning has been able to drive solutions, which help create a more data-driven approach towards solving problems.

How Artificial Intelligence is Changing Enterprise Applications

Corporate enterprises are showing a growing interest in the field of Artificial Intelligence and Machine Learning. From IBM’s Watson to Google’s DeepMind to AWS’s multiple Artificial Intelligence services, there is a lot of activity happening in the market these days.

Other features of Machine Learning include the likes of Deep Learning, computer vision and natural language processing (NLP). With all these innovations languages in place, computers can enhance their functionalities, including pattern recognition, forecasts, and analytical decision-making.

By incorporating Artificial Intelligence and Machine Learning techniques in day to day functions, large enterprises can automate everyday tasks and enhance their overall efficiency in the long run.

Here are some ways in which Machine Learning techniques are helping enterprises enhance their efficiency:

Improving Fraud Detection: Fraud detection has become the need of the hour, as more and more companies are investing heavily in these new capabilities. With more companies falling prey to fraudulent practices, there is an imminent need to be ahead in the game of fraud detection. With Artificial Intelligence and Machine Learning in place, companies and organizations can extensively direct their resources towards enriching their fraud prevention activities, to help isolate potential fraud activities.

Loss Prediction and Profit Maximization: When it comes to deriving insights from heaps of data, there is nothing better than Machine Learning to prevent loss prediction and maximize profits. The stronger the techniques, the more foolproof the loss prediction methodologies would become in the long run.

Personalized Banking: In this era of digitization, everything is automated. For this reason, banks often seek to deliver customized, top notch, personalized experiences to their customers to keep loyalty intact. By leveraging their data, banks can aim to unearth customer needs and fulfill them with the utmost precision and dedication.

Robotic Financial Advisors: Portfolio management has become the talk of the town these days, especially since robotic financial advisors have stepped into the game. Clients can benefit immensely by this advancement, since the right opportunities are mapped with their portfolio needs and demands. Robotic applications are easy to merge with services such as Alexa and Cortana, allowing banks to provide exceptional service to their customers. Through this integration, financial institutions can hope to acquire new customers and also offer more individualized services to existing customers.

Next-Era Digital Traveling: Through the use of recommendation engines, travelers can experience the new recommendations for their travel aspirations. Organizations can play a role by allowing customers to converse with chatbots, which are created through the use of Artificial Intelligence and Machine Learning. As predicted by Gartner, by the year 2020, 25% of all customer service operations will rely on virtual assistant technology to make their business ends meet.

Detailed Maintenance: Through the help of predictive maintenance, industries like aviation, transportation, and manufacturing are expecting to be able to provide the best customer service in the market. Through the use of predictive models, such industries can accurately forecast prices and predict their losses, thereby, reducing any redundancies in the future.

With digitization paving the path of the future, there is a bright scope for companies and organizations which are investing heavily in these new age technologies of Machine Learning and Artificial Intelligence. Third party consulting services such as Idexcel are ready to help companies looking to take their first step with industry leading consulting and cloud-advisory services.

As we progress through the years, what should be interesting to note are the changes we will get to see in the various industries, as every sector aims to provide exceptional customer service to their customers in multiple ways.

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True Business Efficiency Combines the Power of Cloud Computing and DevOps Practices

True Business Efficiency Combines the Power of Cloud Computing and DevOps

The use of both DevOps and cloud-computing have become a promising combination for companies these days. The blend of practices and implementations provides increased speed and efficiency, as well as higher agility and better end-user experiences. While the Cloud and DevOps are two independent technologies, they become intertwined, resulting in a workflow more efficient than ever before.

The Driving Force of the Cloud and DevOps

Implementation of Cloud computing techniques has rapidly ushered in the use of the DevOps philosophy. Through this trend, enterprises are increasingly making use of agile software to develop and implement administrative IT operations for their customers to enhance their services in the long run.

Through the concept of DevOps, businesses can break down their functional silos and drive discipline between their IT-related processes. Since the process of development is rather elaborate, more companies have to spend a lot of time, effort, and resources to plan and transform their IT infrastructure thoroughly – all to obtain the maximum benefit out of available resources.

The progression of the Cloud and DevOps has been rather sudden, as compared to other technological advancements in the enterprise market. The development of DevOps is aimed at making the approach more Cloud-centric; this means that most public and private Cloud providers are beginning to support the implementation of DevOps within their platforms. These practices will enhance and aid the continuous creation of development tools. Strong integration leaves little or no space for cost inefficiencies, as there is an efficient mode of centralized governance within the processes.

Through effective governance, there is tighter control on developers, since they have a more streamlined process to follow, which is controlled centrally via Cloud services. This way, in a subtle yet efficient manner, companies can bring their differentiated departments under control. Cloud-based DevOps also lessen the need for leveraging resources, thereby providing a usage-based accounting solution. Through such elaborate measures, companies can track their applications and developers related data.

Would it be right to say that the Cloud is running DevOps? The answer is quite merely ‘no.’ Given the interdependence between the two technologies, there is an imminent need for both the technologies to be present to derive maximum functionality. As per RightScale’s 2015 State of the Cloud Report, over 71% of companies have adopted 66% of DevOps within their Cloud Services.

DevOps Leads the way into the Cloud

The Cloud is ruled by DevOps – this fact has been proven time and again. With the powerful combination of the Cloud and DevOps working together, there is a lot of aggregate value for CTOs, who are working to remove technical challenges from the paths of implementation. To gain maximum efficiency out of the partnership between the Cloud and DevOps enterprises need to strive towards the rapid deployment of DevOps practices continually.

Due to the inherent lack of knowledge and deployment techniques, many large enterprises and developers alike fear the implementation, since the concept of utilization comes across as confusing and sometimes, even impossible. If Cloud computing is paving the path towards the future, chances are enterprises and developers will need to take this bull by the horns and make the most out of it. In other words, if the Cloud has to work, one must adequately know how to deploy and implement DevOps for best results. Companies such as Idexcel focus on providing DevOps consulting services which provide reduced development times and operational costs for businesses of all sizes.

Use the Cloud and DevOps to Your Advantage

Budgets need to be focused and modified to utilize the Cloud’s full potential and meet enterprise goals. DevOps is about using the right techniques to mold the Cloud’s functionality to one’s advantage. The better the implementation, the more efficient a process will be created. With the right approach and the proper knowledge, developers and enterprises can go a long way in advancing towards the right direction, especially when it comes to meeting all chalked out goals in the long run.

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CIO Cloud Summit 2018

CIO cloud summit 2018

Event Details: The CIO Cloud Summit brings together top IT executives to have in-depth conversations about industry pain points and best practices. These individuals are guaranteed to be Senior Level IT decision-makers. The summit helps the attendees build a network of IT executive peers capable of providing you with invaluable business advice with which to grow your career. Each summit creates a focused environment that allows executives to focus on the issues that are most important to their organizations and the team ensures that all aspects of the event are run smoothly and according to schedule, so that attendees are free to focus on matters at hand.

[Know more about the Conference]

About Idexcel: Idexcel is a Professional Services and Technology Solutions provider specializing in Cloud Services, Application Modernization, and Data Analytics. Idexcel is proud that for more than 20 years it has provided services that implement complex technologies that are innovative, agile and successful and have provided our customers with lasting value.

Anand Allolankandy – (Sr. Director Technical Sales & Delivery at Idexcel) will be attending this event. For further queries, please write to anand@idexcel.com

How Your Small Business can Benefit from Machine Learning

How Your Small Business can Benefit from Machine Learning

The practice of Machine Learning (ML) is no longer an exotic concept for businesses. No matter if you have a small business or a Fortune 500 enterprise; the chances are that you can benefit from the nuances of AWS Machine Learning. While prominent organizations have different ways of using Machine Learning than their smaller siblings, there is a multitude of ways in which even small businesses can benefit from Machine Learning techniques.

What Exactly is Machine Learning?

Machine Learning is a type of artificial intelligence which uses programs, algorithms, and data to drive learning and automation. Under normal circumstances, it’s something you most likely already encounter on a day to day basis. For example, if you are using software like Amazon’s Alexa, Microsoft’s Cortana, Google’s Assistant, or Apple’s Siri, then you have already had a taste of the power of Machine Learning.

On the other hand, Machine Learning and Artificial Intelligence can be used in businesses as well; it isn’t just for asking about the latest weather conditions. For instance, many websites are making use of chatbots to assist customers. Businesses, irrespective of their size, are using the likes of Machine Learning to help customers while driving efficiency and monitoring social media accounts.

How can AWS Machine Learning Help Business?

Amazon has established themselves as a leader in customer service and operations. Such execution can be found with the Amazon Web Services (AWS) Machine Learning tool. These data learning devices are aimed at catering to data scientists, researchers, developers and even small businesses who are enthusiastic to use Machine Learning to their advantage.

The extent of Machine Learning advantages is not limited to just the essentials. Solutions such as Amazon Comprehend and AWS DeepLens are some of the top-notch services being provided by Amazon these days. Through these services, developers can inherit the ability to use neural networks to gain insight with regards to computer vision projects.

Developers can also train chatbots, which can cater to a customer’s specific incoming request. Machine Learning and Artificial Intelligence can even be utilized to organize a website’s content, as various defined logical algorithms come into play. A small business can also coordinate their website’s inventory using artificial intelligence.

If you are running a small business, and feel as though you don’t wish to dapple in artificial intelligence alone, then you can count on the consulting services of companies such as Idexcel. Experienced teams are always available to help businesses of any scales accomplish their goals and increase their cloud repertoire.

How does AWS Machine work with Small Businesses?

Small businesses often need to use predictive models to enhance their revenue and sales models. One of the ways to improve these models is through the use of machine learning. Entrepreneurs, who are running small businesses, often don’t have sufficient time or the resources to sift through massive data and derive intelligent decisions out of it; this is where machines learning techniques come to the rescue.

Such business owners can benefit immensely from the use of AWS Cloud-based services and AWS Machine Learning. The vast amount of data which is collected can be sorted, sifted, and analyzed to deliver helpful business-related insight efficiently.

Through the use of machine learning, small businesses can save on operating costs, while at the same time make sound decisions, and earn better profits than before. However, it is import to know that small businesses cater to customers at different stages. For this reason, it’s imperative to understand how customer behavior can change from time to time. Through predictive analytics and machine learning, such tactics can become a breeze.

No matter what the type of business you have, machine learning can come to your aid at any given point in time. From data collection to data storage and insights you can have it all; it not only helps enhance your business’s image through the use of chatbots but also helps you manage your inventory efficiently. Such is the power Machine Learning gives to its users.

For every small business owner out there, there is a unique benefit that you will get with the use of AWS Machine Learning techniques. It all depends on how you use the services to meet your company’s needs and wants at the end of the day.

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The Future of Data Science Lays within Cloud-Based Machine Learning and Artificial Intelligence

The Future of Data Science Lays within Cloud-Based Machine Learning and Artificial Intelligence

From working in solitary cubicles to working with artificial bots, automation has come a long way and has changed how the modern generation works. Today, Artificial Intelligence (AI) and Machine Learning (ML) have become regarded as the future of tomorrow’s workforce and culture. At Idexcel, we have been keeping track of these ongoing trends.

It has been said that “with great power comes great responsibility.” If we tweak this statement a bit to reflect more contemporary times, we can also assuredly say that “with great technology, comes heaps of data.” The more we progress on the path of digitization, the more massive our datasets have become, and the idea that fascinates all data scientists is linked with the emergence of AI and ML technologies – how can they be used efficiently?

Here are top 10 trends that you should look out for as they shape the direction of data analytics in our future:

Augmented Analytics: This technology broadly utilizes the power of machine learning to automate data preparation and presentation. Through the use of augmented analytics, data scientists hope to be able to aid human intelligence, to produce rapid outcomes in different data-driven domains.

Artificial Intelligence and Machine Learning: Many of us might still be living in a bubble when it comes to AI assisted work. However, the fact of the matter is, that this bubble is going to burst. With robotics and artificial intelligence taking over at an increasing pace, there is a very heavy emphasis on getting data up and running to meet organizational goals. AI and ML will be used extensively to simplify work processes through the use of Big Data analytics.

Big Data: As technology has been advancing over the years and more affordable machines have emerged, faster processing powers have been available to businesses. Now, as cloud services are taking over traditional storage methods, there is a lot to look forward to regarding the increased output of processed information. As all these sources of information generate data, there is an imminent need to draw meaningful conclusions from the data – this is where Big Data comes into the picture. As the sources for storage get defined, Big Data provides excellent methods for allowing the manipulation of stored data to draw analysis and get the ball rolling.

Cloud and Edge Computing: One can easily say that technology has reached the Clouds. Companies such as Amazon, Google, and Microsoft are providing Cloud Services to organizations for storing their day to day data. Edge Computing, which is another form of shared computing, has become the next generation’s technology. Through the means of Edge Computing, organizations can overcome connectivity and latency issues, so that the distance data has to travel is reduced significantly. Edge Computing has seen an increasing rate of growth in mobile computing, as well as in the decrease of computer hardware. The rising use of IoT-enabled devices has ushered in an era of new technology.

Predictive Analytics: As more problematic situations emerge, there is a need to develop systems which can solve problems with ease and provide meaningful solutions. Predictive analytics prove to be the solution for such issues. The better the insights, the more structured are the solutions. Such are the capabilities of predictive analytics, as they help organizations gear up to tackle the worst possible issues.

Blockchain Technology: Digital currencies, such as BitCoin, owe their very existence to Blockchain technology. Given the success rate of cryptocurrencies, there is a lot of focus on merging Blockchain technology with the world of data science. The idea is to fuse the two methodologies together, to maximize the results. Since Blockchain technology is a versatile source, it can store any digital data; it has become a well-received option with data scientists.

As more and more organizations are taking analytics and data science seriously, there is an imminent need to progress to the next level of technology. Here at Idexcel, we work with clients every day to provide DataOps Consulting and Services; it has become an inseparable part of the modern organizational structure. As we progress into the future, the thin line between business intelligence and artificial intelligence will be removed; data will become smarter than ever before.

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AWS IoT Analytics Provides Intelligence Data For Businesses

AWS IoT Analytics Provides Intelligence Data For Businesses

One of the significant highlights of the Amazon Web Services (AWS) re:Invent 2017 conference is the company’s IoT Analytics; a fully-managed service that makes the experience of running sophisticated analytics on massive volumes of IoT data flawless. The new AWS system eliminates the worry of cost and complexity typically incurred during the build and deployment of a personal IoT analytics platform. AWS IoT Analytics has rendered an effortless way to run analytics on IoT data, along with gathering ongoing insights to better the experience of decision making for IoT applications and machine learning.

The Complexities of Unstructured Data

Since IoT data is highly unstructured, it became a mission for AWS to simplify data structures so that it would become easier for cognitive computing solutions to analyze the IoT database. This idea is executed through business intelligence tools that are designed to process large unstructured data. IoT data is procured mainly through reasonably noisy processes, which in turn produces extensive and complex data with gaps, corruption, false reading and so on; this data needs to be taken care of before any analysis can occur. Besides, IoT data is often integrated into the context of other data from external sources and must be managed appropriately.

Are you utilizing analytics and the existing information provided by your system to increase problem solving and overcome the obstacles to processing big data? Amazon’s AWS IoT Analytics allows for customers to solve complex problems without complex solutions. Our team here at Idexcel is at the ready and available to work with those who want to ensure they are getting the most out of their AWS setup. Be sure to reach out for our cloud advisory services and accelerate your journey to the cloud.

Analyzing Problems and Providing Solutions

AWS IoT Analytics automates each of these problematic steps that are required to analyze data from IoT devices. IoT Analytics acts as a catalyst that filters, transforms, and enriches information before storing it in a time-series data storage for analysis. The service can then be customized according to the business: which, how much, and when to use appropriate data. AWS IoT Analytics applies mathematical equations to process and then enrich the data with device-specific metadata. Data is then analyzed by running queries using the built-in SQL query engine. IoT Analytics kick starts the process and provides better scope for outputting high accuracy information. IoT Analytics also exhibits the ability to facilitate machine learning through employing pre-built models of common IoT use cases; it can then quickly respond to probable system failure or system incompatibility and suggest replacement of hardware.

AWS IoT Analytics can keenly examine and scale automatically to support up to petabytes of IoT data; it helps analyze data from millions of devices and build fast, responsive IoT applications without managing different hardware or infrastructures. The service complements the driving forces of current IoT infrastructure with differing advancements.

It is worth noting some of the most important benefits of IoT Analytics include:

Quick and Easy Queries on Massive IoT Data – With the help of a built-in IoT Analytics SQL query engine, it becomes effortless to run ad-hoc queries; this service enables the user to use standard SQL queries to extract data directly from the data store to answer potential questions.

Time-Series Analytics – AWS IoT Analytics also supports time-series interpretations to analyze the performance of devices over time in a recurring pattern, and understand their place and manner as they are being employed. Analytics can continuously monitor device data and suggest maintenance actions as needed. The system can also observe sensors to analyze and react to environmental conditions.

Data Storage Optimized for IoT – AWS IoT Analytics stores processed device data and can deliver fast response times on IoT queries. The source data is automatically stored for later processing or to reprocess it for another use case, creating a more intelligent dataset.

Prepare IoT Data for Analysis – AWS IoT Analytics also performs data preparation that makes it easy to prepare and process your data for analysis. Integrated with AWS IoT Core, the service makes it easier to ingest device data directly from connected devices. IoT Analytics filters the data apart from corruption, false readings, and errors, and then the system performs mathematical transformations of message data. Using conditional statements the analytical service filters data, and then collects specific data required for analysis; it also gives the option of using AWS Lambda functions to enrich device data from external sources.

Tools for Machine Learning – AWS IoT Analytics is well suited for machine learning on IoT data as it has the ability hosts Jupyter notebooks. The administrator can directly connect IoT data to the notebook to build, train, and execute models right from the IoT Analytics console. Machine learning algorithms are applied to data all the more readily, which produces a health score for each device in the fleet.

Automated Scaling with Pay-As-You-Go Pricing – AWS IoT Analytics follows a pay-as-you-go service, with which one can analyze an entire fleet of connected devices without managing hardware or infrastructure. As the administrator’s needs change, they can expand or contract computation power. The data store will also automatically scale up or down, which results in the billing of only employed resources.

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