The Importance Of Culture Change In Digital Transformation

More and more businesses are seeking to digitally transform their organizations to meet the ever increasing demands and expectations of the modern, technically sophisticated customer. But few are prepared for the internal disruption this causes throughout the entire enterprise, which is why culture change and change management is so important.

For digital transformation to be successful, businesses need to adopt agile methodologies, processes and working practices. Becoming an agile business requires a cultural change. Similarly, as digital transformation is largely driven by the changing demands and expectations of customers, customer-centric strategies are a must and successfully developing and implementing these new approaches requires a more integrated and fluid organization.

Laying the Foundations of Digital Transformation

Businesses whose digital transformation projects fail are usually guilty of failing to lay solid foundations:

Agile

Customer-Centric

Omni-Channel Experience

Laying these foundation stones requires huge culture change within the organization and managing this process is challenging. Unfortunately, if your organization wants to be successful, it is unavoidable.

Culture Change

Due to the ever changing and ever evolving nature of the modern world, businesses need to cultivate a culture of perpetual revolution.

The days of businesses setting a strategy for the next 5 years, then waiting for the business execute that strategy, are long gone. Setting a 5 year strategy is still important, but the need to be able to rapidly pivot that strategy at will is fundamentally important to the long-term strategic success of the enterprise. Most businesses, especially big and established companies, are difficult to turn and this is why aggressive new market entrants are able to sweep in and digitally disrupt industries, markets and supply chains. Established companies are too slow to respond and by the time they have mounted a meaningful response, the new entrant has established a strong position in the market and is almost impossible to remove. Companies need to be able to pivot quickly to respond to new market entrants and changes in the competitive landscape.

Similarly, as new technologies emerge, customers and other major stakeholder group's demands and expectations change. The innovation roadmap you are following today could be redundant in weeks or months. Agility enables quick pivoting of development roadmaps, rapid transformation of customer-centric strategies and the re-engineering of omni-channel customer experiences.

All these cases cause enterprise wide disruption and with the frequency of these disruptions rising, a culture of change and constant revolution is a must.

For more information about digital transformation, read my whitepaper: Digital Transformation: A Guide for Business Leaders.

By Robin Cronan

Data Recovery: How To Recover From A Hard Drive Failure

Context:

Unfortunately, most home users, and many business users, do not back up their systems. Moreover, many small businesses have older back-up procedures that are often ineffective for recovering files.
Of course, you can run down to your neighborhood electronics store and purchase a replacement drive for your computer, but what about your data on the failed hard drive? How important was it? Did you save it or back it up?

What to do:

If you need to recover data on the hard drive, the first thing to do is avoid trying to reboot or doing anything that involves the drive. Doing so can actually do more damage to your data.

The only irreversible data loss is caused by overwriting bits, physical damage to the drive platters or destruction of the magnetization of the platters, which seldom happens in the real world. In the majority of cases, the malfunction is caused by a damaged circuit board, failure of a mechanical component and crash of internal software system track or firmware.

In the case of actual hard drive failure, only a data recovery professional can get your data back. And the fact that you cannot access your data through your operating system does not necessarily mean that your data is lost.

As a "rule of thumb," if you hear a clicking sound emitting from your hard drive, or if the computer's S.M.A.R.T. function indicates an error during the boot process, something is wrong. You should immediately stop using the hard drive in order to avoid causing further damage and, potentially, rendering the information on the hard drive unrecoverable.

After receiving your failed hard drive, a data recovery specialist's first step will be to try and save an image of the damaged drive onto another drive. This image drive, not the actual damaged drive, is where the data recovery specialist will try to recover the lost data.

The next step in the imaging process is to determine if the hard-drive failure was an actual malfunction, a system corruption or a system track issue.

System corruption and system track issues are normally fixed by using a specialist's data recovery software. System corruption or system track recoveries do not require processing in a clean room environment.

Conclusion:

Unfortunately, damage to a drive's circuit board or failure of the head drives is not uncommon. In each of these failures, a data recovery specialist should work on the system only in a clean room environment. There, the specialist can substitute parts such as drive electronics, internal components, read/write arms, writing/reading heads, spindle motors or spindle bearings from a donor drive in order to gain access to the data on the failed hard drive. In most cases, the data recovery specialist is able to retrieve and return the lost data.

By Loveleen Talwar

The Video About Cloud Technology Everyone Should See

The Cloud....seriously though, what is it?  Kitty Flanagan gets right down to it.

This is absolutely hilarious....plus you will still probably learn something!

Enjoy.....

Advantages Of Cloud Storages

Utilizing an outer drive is the most generally utilized approach for having reinforcement stockpiling. The general population who mull over making utilization of distributed computing for this reason regularly think about whether the innovation is justified regardless of the exertion. Clients of the framework guarantee that there is no motivation behind why anybody must abstain from utilizing this framework as it guarantees different extra points of interest when contrasted with the ordinary techniques.

The way that one needs to spend an infinitesimal measure of cash each month for the utilization of cloud information stockpiling is one explanation behind potential clients to be reluctant.

Notwithstanding, the accompanying advantages of the innovation are reason enough to guarantee that this cash spent is well justified, despite all the trouble.

Extensive storage space: The most fundamental preferred standpoint of utilizing the cloud is that one can store any measure of information, which is outlandish while utilizing drives. Additionally, the framework is to a great degree simple to use as the record is made in minutes, instead of the time and exertion spent on going looking for an outside drive.

No Physical presence: Once you have put away your information on the cloud, it turns into the obligation of the supplier to stress over its upkeep. Rather than purchasing and putting away those various outer drives, one just needs to remain associated with the web keeping in mind the end goal to get to the put away information.

Convenience of automatic backup: The clients of distributed computing don't need to try guaranteeing that they have associated the outside drive to their PCs and that they take reinforcements at general interims. The settings on the cloud framework can be changed according to the client's inclination with respect to whether the reinforcement ought to be taken various circumstances in a single day or once consistently. The main clear essential for the framework to be moved down is that the web ought to be associated and everything else is dealt with.

Easy restoration: In regular conditions, recovering and reestablishing a hard drive from moved down information is a long and awkward process which requires the administrations of a PC professional. The cloud clients are saved from any such burden as this reclamation procedure is made straightforward and brisk. On the off chance that at all the clients still have questions about taking care of this all alone, they can simply look for assistance from the suppliers and they will gladly oblige.
For such huge numbers of administrations, the little expense charged by the supplier ought to barely be a killjoy. One can simply be on the watch out for rebates and offers that are offered by cloud suppliers for new customers which chop down the expenses to an absolute minimum.
If you want to know more go to: https://cgimart.us/onedrive-customer-service-phone-number/

By Shail Ahmed

The Dangers Of Chatbot Dependence

The use of live chat for customer services has grown popular over the past several years, often replacing voice support services. Many companies now recognize the benefits it brings, such as

the ability to address customer needs with more clarity

increased time and cost efficiency

better customer satisfaction

However, with the growth of chat customer support came the creation of AI software that could take over the responsibilities of a human support agent-the chatbot.

For large companies that often handle hundreds if not thousands or even millions of customers in a day, a chatbot can save them a lot of time and allocation of resources. They don't have to hire big teams of human customer support agents to handle every single customer that comes to them with an inquiry. Another big plus for businesses is that chatbots don't get tired. They don't need to work in shifts-they can work 24 hours a day, 7 days a week for as long as the company uses them.

But as much help as chatbots can be to a big brand, they can also be a huge detriment.

Artificial intelligence is still flawed, as is with anything man-made. Sometimes the AI becomes too good to the point that it appears they have grown sentient, or they can be entirely unable to help a customer in need, as was the case with Telstra, a telecommunication company based in Australia.

Several news sources such as the Sydney Morning Herald, the Daily Mail, and Yahoo! News have reported that many customers have become irate at the quality of Telstra's customer support chatbot, Codi, which was launched last October. Since then, customers have been posting on social media about their discontent with Codi.

For starters, the chatbot has a lot of trouble processing simple requests, such as when a customer requests that they be handled by a human agent. Codi also had a tendency to repeat itself and is prone to system crashes. There is one memorable anecdote of a man named Paris who requested a human agent and instead was asked if he wanted data roaming. Apparently, Codi mistook his name for the French city.

While this is not the same for every chatbot being used by businesses, Codi is a reminder of the possible trouble that awaits them, no matter how good the algorithm is. These kinds of issues can be a serious factor in a customer's satisfaction (or lack thereof) with a company, no matter how good their products or services are.

While AI has proven itself to be useful and full of potential, it is wiser to proceed with caution and not completely depend on it, especially when it comes to customer support. Yes, hiring human support teams can mean more expenses than a chatbot program, but while robots can automate the entire process and handle simple queries with more efficiency, they still cannot deal with problems that require a more human touch.

There is no better investment return than good sales and a happy, satisfied customer. Using an AI today may be able to give you the first, but what about the latter? This is important to consider when deciding how to handle your chat customer support.

By Donald Smithon

How SDWAN Is Like Smart GPS For Wide Area Networks

Do you remember the days before you had a smartphone and you wanted to get somewhere you had never been? Did you pull out the Thomas Brothers map book, look up the address in the back and then find the page and quadrants to find your street? Then you had to backtrack to your location to figure out how to get there. And traffic? You had to listen to the AM station that had “traffic on the sixes.” Want to go back there?

Well in the old days, the way we routed network traffic was about as antiquated. Let’s compare this natural progression of finding your way from point A to point B using network equipment. The days of physical maps – the “Thomas Brothers” is like a basic router. This “physical map” showed you all paths leaving it up to you to figure out the best way there. Then we were introduced to MapQuest. This let us look up the address where we wanted to go, and it gave us a map and printed directs how to get there from our location. Let’s compare that to fail over routers. These used non-real-time path information to get you there using the shortest path.

The Aha Moment

Today we have a computer in our pocket to help us get where we want to go using the fastest route. Google Maps or Waze use real-time data and information from actual drivers to get you there faster and smarter. Today’s SDWAN Technology does this for the network. Traffic can be prioritized, and the technology know what to get there first.

Traditional network devices cannot detect all network conditions on every hop between A & B. Anomalies like network stability, small packet loss, jitter, latency, etc. cannot be detected in real time. Therefore, services like voice & video (without SDWAN) can have quality issues, even with the best bandwidth.

Today’s network monitoring is not real-time, it is near real time, which leaves significant gaps in visibility/information such as traffic bursts and other anomalies. SDWAN helps companies by combining real-time granular advanced end-to-end network metrics/information with real-time wire speed per-packet and per session routing decisions at each edge.

SDWAN combines navigation smarts with actual routing ability with easy management, automation, and visibility into all locations giving companies an amazing solution that merely saves time, money, and effort. SDWAN is an easy-to-use toolset that can be configured in many ways. SDWAN fixes the problem of the edge management and lack of onsite IT. SDWAN automates many of the once manual tasks of programming, provisioning and routing decisions with degraded internet connections.

THERE'S free Help AVAILABLE TO You FOR Selecting the Right Equipment

SDWAN is a broad technology and can get confusing but fear not. All SDWAN manufacturers are purpose-build for specific client use cases. Some are built to help prioritize and control cloud connectivity and applications that run the business, others are built for network resiliency and reliability, while still others are built to collapse environments using an all-in-one cloud firewall solutions. All SDWAN manufacturers are great; it just all depends on what you are trying to accomplish not only today but three years from now. By approaching SDWAN from a consultative and high-level, goal-oriented approach can bring you an extreme amount of long-term value and savings.

As far as pricing, it is very dependent on size and speeds required; it can run anywhere from $55 per month per location to $2k per month per location depending on your specific needs.  These solutions can also be Managed, Co-Managed or do-it-yourself installations. In some cases, SDWAN can even save you money by offsetting high-cost equipment and networking services.

 Here are just a few of the SDWAN manufacturers that we have access to:

Aryaka, Silverpeak, Cato Networks, Cisco VIPtela, Cisco Meraki MX, Citrix , CloudGenix, Barracuda Networks, BigLeaf Networks, Ecessa, Riverbed, SimpleWAN, TeloIP, Versa Networks, and VeloCloud.

 By scheduling a discovery call with us and one of our engineers, we can determine a solution that fits best for your specific use case from simple failover to global designs.

All it takes is to ask us at the below link....it's as easy as 1, 2, 3.

Free SDWAN Network Design Support

Virtualization For Fast-Growing IT Requirements

The actual need for virtualization basically requires the prior understanding of three things: Why Virtualize? What is Virtualization? And When to Virtualize?

The virtualization technology evolution dates back to the times of main frame computers, where the operators had to utilize huge power resource to run processes. Operating Virtualization addressed this issue by allowing the hardware resource to run multiple operation system images using a single software tool, thus managing the power utilization in running processes.

Server virtualization is the key aspect of virtualization technology, where the main server is virtualized to create a guest system that exactly works as a main system. A software layer called hypervisor makes this happen by emulating underlying hardware. Here the guest operating system uses the software emulation of the underlying hardware, i.e., virtualized hardware and not the true hardware.

The performance of the virtual system is not exactly the same as that of the true system. Even then the virtualization holds significance as the most applications and guest systems may not demand for full utilization of the underlying hardware.

Thus, the dependence on hardware is alleviated, allowing greater flexibility and isolation of the processes from the main system, whenever needed. Here is where the companies working on multiple applications on multiple platforms can have an advantage of minimization of extra resource utilization.

Virtualization, which was initially confined to server systems, has evolved over the years to suit for networks, desktops, data and applications, among others.

Wings of Virtualization:

Virtualization has spread its wings across six key areas of significance in the IT industry:

Network Virtualization: This reduced the complexity across networks by grouping the available resources in a network, connecting them with independent channels formed as a result of the splitting of available bandwidths. These channels can be linked to devices later, depending on the requirement.

Storage Virtualization: Here, various storage devices are grouped into a single large virtualized storage unit, which is controlled from a central console.

Server Virtualization: This involves the masking of servers so as to limit the server users from accessing server's complex information, such as physical address, among others, while also ensuring the resource sharing. The software that is used to virtualize the underlying hardware is 'hypervisor'

Data Virtualization: Here the broader data access is provided to meet the business requirements, while abstracting the very important basic information like storage location, performance, and format.

Desktop Virtualization: Here the main intention is to share the workstation. Instead of server, the workstation load is shared via virtualization, in the name of remote desktop access. As the workstation works in data centre server environment, security and portability are also ensured.

Application Virtualization: Here the application is abstracted from the operating system, and encapsulated. The encapsulated form of the application is used across platforms without having need fo depend on the operating system every time during implementation.

Overall, fast-growing IT and end-user requirements driven by rise in demand for automation gives a needed boost to the global IT virtualization market.

By James Peterson

A Brief Introduction to Artificial Intelligence

We all know that Siri, Google Now, and Cortana are all intelligent digital personal assistants on various platforms (iOS, Android, and Windows Mobile). In short, they help find useful information when you ask for it is using your voice; you can say "Where's the nearest Indian restaurant?", "What's on my schedule today?", "Remind me to call Mom or Dad at eight o'clock," and the assistant will respond by finding information, relaying information from your phone, or sending commands to other apps.

AI is important in these apps, as they collect information on your requests and use that information to better recognize your speech and serve you results that are tailored to your preferences. Microsoft says that Cortana "continually learns about its user" and that it will eventually develop the ability to anticipate users' needs. Virtual personal assistants process a huge amount of data from a variety of sources to learn about users and be more effective in helping them organize and track their information.

Your smartphone, calculator, video games, car, bank & your house all use artificial intelligence daily; sometimes it's obvious what its' doing, like when you ask Siri to get you directions to the nearest gas station. Sometimes it's less obvious, like when you make an abnormal purchase on your credit card and don't get a fraud alert from your bank. AI is everywhere, and it's making a huge difference in our lives every day.

So, we can say that Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans. For example, speech recognition, problem-solving, learning and planning. Today, Artificial Intelligence is a very popular subject that is widely discussed in the technology and business circles. Many experts and industry analysts argue that AI or machine learning is the future - but if we look around, we are convinced that it's not the future - it is the present.

Yes, the technology is in its initial phase and more and more companies are investing resources in machine learning, indicating a robust growth in AI products and apps soon. Artificial intelligence or machine intelligence is the simulation of human intelligence processes by machines, especially computer systems.

What is the use of AI?

Vision systems. The need to interpret, fully understand and make sense of visual input on the computer, i.e. AI is used to try and interpret and understand an image - industrial, military use, satellite photo interpretation.

What is the purpose of AI?

When AI researchers first began to aim for the goal of artificial intelligence, a main interest was human reasoning... The specific functions that are programmed to a computer may be able to account for many of the requirements that allow it to match human intelligence

What is an ASI artificial intelligence?

A superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds.

What is the goal of AI?

Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". General intelligence is among the field's long-term goals.

What are the different types of AI?

We need to overcome the boundaries that define the four different types of artificial intelligence, the barriers that separate machines from us - and us from them.

Type I AI: Reactive machines
Type II AI: Limited memory
Type III AI: Theory of mind
Type IV AI: Self-awareness

Is computer vision part of AI?

Artificial intelligence and computer vision share other topics such as pattern recognition and learning techniques. Consequently, computer vision is sometimes seen as a part of the artificial intelligence field or the computer science field in general.

Is machine learning the same as artificial intelligence?

Increasingly, machine learning (ML) and artificial intelligence (AI) are cropping up as solutions for handling data. The two are often used interchangeably, and although there are some parallels, they're not the same thing.

What are the fields of artificial intelligence?

· List of applications
· Optical character recognition.
· Handwriting recognition.
· Speech recognition.
· Face recognition.
· Artificial creativity.
· Computer vision, Virtual reality and Image processing.
· Diagnosis (AI)
· Game theory and Strategic planning.

How important is Artificial Intelligence?

AI is the machines which are designed and programmed in such a manner that they and think and act like a human. Artificial Intelligence becomes the important part of our daily life. Our life is changed by AI because this technology is used in a wide area of day to day services.

For most of us, the most obvious results of the improved powers of AI are neat new gadgets and experiences such as smart speakers, or being able to unlock your iPhone with your face. But AI is also poised to reinvent other areas of life. One is health care. Hospitals in India are testing software that checks images of a person's retina for signs of diabetic retinopathy, a condition frequently diagnosed too late to prevent vision loss. Machine learning is vital to projects in autonomous driving, where it allows a vehicle to make sense of its surroundings. Artificial intelligence is already present in plenty of applications, from search algorithms and tools you use every day to bionic limbs for the disabled.

Sometimes it seems like every other website, app, or productivity tool is citing AI as the secret ingredient in their recipe for success. What's less common is an explanation of what AI is, why it's so cool, and how companies are leveraging it to provide better user experiences. If you don't know much about AI, the absence of an explanation can be confusing. Today, the field of artificial intelligence is more vibrant than ever and some believe that we're on the threshold of discoveries that could change human society irreversibly, for better or worse.

By Kranti Gaurav

What Are The Challenges Of Machine Learning In Big Data Analytics?

Machine Learning is a branch of computer science, a field of Artificial Intelligence. It is a data analysis method that further helps in automating the analytical model building. Alternatively, as the word indicates, it provides the machines (computer systems) with the capability to learn from the data, without external help to make decisions with minimum human interference. With the evolution of new technologies, machine learning has changed a lot over the past few years.

Let us Discuss what Big Data is?

Big data means too much information and analytics means analysis of a large amount of data to filter the information. A human can't do this task efficiently within a time limit. So here is the point where machine learning for big data analytics comes into play. Let us take an example, suppose that you are an owner of the company and need to collect a large amount of information, which is very difficult on its own. Then you start to find a clue that will help you in your business or make decisions faster.

Here you realize that you're dealing with immense information. Your analytics need a little help to make search successful. In machine learning process, more the data you provide to the system, more the system can learn from it, and returning all the information you were searching and hence make your search successful. That is why it works so well with big data analytics. Without big data, it cannot work to its optimum level because of the fact that with less data, the system has few examples to learn from. So we can say that big data has a major role in machine learning.

Instead of various advantages of machine learning in analytics of there are various challenges also.

Let us discuss them one by one:

• Learning from Massive Data: With the advancement of technology, amount of data we process is increasing day by day. In Nov 2017, it was found that Google processes approx. 25PB per day, with time, companies will cross these petabytes of data. The major attribute of data is Volume. So it is a great challenge to process such huge amount of information. To overcome this challenge, Distributed frameworks with parallel computing should be preferred.

• Learning of Different Data Types: There is a large amount of variety in data nowadays. Variety is also a major attribute of big data. Structured, unstructured and semi-structured are three different types of data that further results in the generation of heterogeneous, non-linear and high-dimensional data. Learning from such a great dataset is a challenge and further results in an increase in complexity of data. To overcome this challenge, Data Integration should be used.

•  Learning of Streamed data of high speed: There are various tasks that include completion of work in a certain period of time. Velocity is also one of the major attributes of big data. If the task is not completed in a specified period of time, the results of processing may become less valuable or even worthless too. For this, you can take the example of stock market prediction, earthquake prediction etc. So it is very necessary and challenging task to process the big data in time. To overcome this challenge, online learning approach should be used.

• Learning of Ambiguous and Incomplete Data: Previously, the machine learning algorithms were provided more accurate data relatively. So the results were also accurate at that time. But nowadays, there is an ambiguity in the data because the data is generated from different sources which are uncertain and incomplete too. So, it is a big challenge for machine learning in big data analytics. Example of uncertain data is the data which is generated in wireless networks due to noise, shadowing, fading etc. To overcome this challenge, Distribution based approach should be used.

• Learning of Low-Value Density Data: The main purpose of machine learning for big data analytics is to extract the useful information from a large amount of data for commercial benefits. Value is one of the major attributes of data. To find the significant value from large volumes of data having a low-value density is very challenging. So it is a big challenge for machine learning in big data analytics. To overcome this challenge, Data Mining technologies and knowledge discovery in databases should be used.

The various challenges of Machine Learning in Big Data Analytics are discussed above that should be handled very carefully. There are so many machine learning products, they need to be trained with a large amount of data. It is necessary to make accuracy in machine learning models that they should be trained with structured, relevant and accurate historical information. As there are so many challenges but it is not impossible.

By Gunjan Dogra