Scientists believe Artificial Intelligence can free humanity from performing routine tasks in many areas. Healthcare is that area that seems to need these changes the most.
While outside it is already 2020, and the majority of businesses are digitalizing themselves. And moving from on-premises infrastructure to clouds. The healthcare industry remains a pain point for the biggest part of the world.
The research says that 56% of hospitals don’t have a strategy on how to govern data and conduct analytics. Healthcare still lacks the entire structured database, data from which can be easily read, interpreted, and applied to future treatment.
When we’re facing one of the most challenging pandemics of coronavirus, the new approaches to healthcare, data analysis, and predictive analytics are must-have tools to apply.
Data is our Life
Why is data our life? The doctors are the same humans as we, and the possibility of a mistake in prescribing or specifying the dose of medication is not uncommon today. One wrong prescription can become a threat, not only to the complete recovery. But to the life of a patient and all humanity, in general, the same rules apply. Mistakes across the healthcare field increase both insurance and hospital costs.
Data in healthcare is what we all are dependent on. Data analytics in healthcare, in its turn, is crucial for healthcare. As it is a decisive factor when defining the methods of treatment and prescribing medications. It can give us an entire picture of each patient’s condition, precision-driven methods of care.
Graphic source: bhmpc dot com
The real-time medical data analysis gives the possibility:
to keep and process data in real-time to be able to make a clinical decision at the right time.
to decrease costs on unnecessary medicaments, avoid duplications. It also allows searching for less costly alternatives
to minimize the risk of treatment of unaddressed and worsening conditions that need the clinician’s attention. So that problems may be addressed before the patient is readmitted.
to cut patient wait-times through measuring and scheduling the time of each procedure.
to ensure a more personalized patient’s treatment and increase the overall satisfaction.
Of course, hospitals can’t cut their costs or provide services to fewer patients. Instead, they can optimize their use of the assets and try to do more while spending less time and money resources on it. It might sound strange, but with Artificial Intelligence and its technologies of data analysis in health care and predictive analytics, it is more than possible.
How Do AI and Big Data Transform Healthcare?
The integration of Artificial Intelligence models in health care is one of the biggest focuses in the world in recent years. 2020 has just started, but already two leaders on the market announced their plans on budget allocation in AI in the healthcare industry:
Microsoft is going to invest $40 million in Artificial Intelligence technology for the healthcare sector for the next five years.
Bayer agreed on cooperation with Exscientia in drug manufacturing based on AI technologies.
That leaves us with no doubts that AI technologies are fundamentally changing the global health care system, making it possible to radically redesign the medical diagnosis system, develop new approaches to medical treatment. They are generally to improve the quality of healthcare services while reducing costs for medical clinics.
What does real-time data analysis in health care offered by AI enable:
Planning medical care for individuals and population groups, including prognostic management of disease progression.
Identification and involving the most effective practical measures to decrease the number of repeated hospitalizations.
Minimize the risk of blood poisoning and renal failure
Defining new methods of improving the quality of patient care.
Among the benefits of big data in healthcare is the possibility to improve the quality of clinical services, track financial performance. And detect fraud while freeing doctors from routine work and leave the opportunity to do what they have to do – help people to maintain their health in good condition And react to unforeseen health issues in time.
Words of Worth
While static data can just describe the health conditions of the patients and store necessary information about medicine. Data analysis can help answer more critical questions: ‘why is it happening so?’, ‘what can we do with it?’ and ‘how can we avoid this.’
The number of investments in Artificial Intelligence in healthcare, programs, researches. How the world adopts newly coming technologies to the industries, including healthcare. That means that the answers to the question will be found very soon.
In this age of cyber breaches and sensitive data leaks, keeping your personal and commercial information safe has never been more important. What’s more, the security of our data is perhaps more vulnerable than ever as most of it is now stored on our mobile devices.
With that in mind, it’s easy to see the absolute necessity of keeping them as secure as possible. However, the ultimate security would require you to disable all the wireless modules of your device. Additionally, you’d have to set up a 20-character password with letters, numbers, and special characters.
There’s a reasonable balance between data security and convenience on your Android devices.
How to Keep Your Android Device Secure
The goal of this overview is to find a reasonable balance between data security on an Android device and the convenience of everyday use. If you think that you’re a target for a spearfishing cyberattack, you’d probably be better off following the advice from the paragraph above; for the rest of us, the 10 suggestions below should be plenty to allow us to stay safe.
#1. Look at the brand and hardware
Several things we’re going to talk about below, including firmware issues and authentication methods, depend heavily on the implementation in a particular smartphone. For example, cheaper devices may not have special additional cameras and depth sensors for FaceID, which could make it possible to fool them by a photo or simple mask.
If device security is important to you, make sure that you understand the relevant specifications before purchasing. Another obvious recommendation is, of course, to avoid buying from lesser-known brands (think Leagoo, Doogee, or Homtom) or shady sellers — saving a couple of hundred dollars isn’t usually worth the risk.
#2. Check the firmware
Although normally you’d expect to receive proper original firmware when buying a new device, it’s not unheard of for the store to install software of its own on a smartphone before selling it. The store rarely does it with purely malicious intent. Sometimes it could be localized firmware for the target market, sometimes the seller wants to earn extra money with bloatware, and so on.
Anyway, having non-original firmware is a security threat. Not only do you not know what’s hidden there, but you also usually miss important security updates for your device. Therefore, it’s always a good idea to download original firmware from the manufacturer’s website and install it after obtaining a new device. It’s a sensible thing to do with a new smartphone and a must if you buy a used item.
#3. Choose your authentication methods
Any decent Android smartphone these days comes with a range of authentication methods built-in. In most cases, you’d be offered to choose from a password, PIN code, screen pattern, fingerprint, and FaceID.
Let’s assume you’ve read the first section of this overview carefully and got a phone where all authentication methods are implemented correctly. Which should you choose then?
From a security standpoint, a long, unique password is the best authentication method. The problem is, however, that entering it more than 100 times a day (yes, that’s how often we check our phones) isn’t convenient at all. PIN codes and patterns, however, can be guessed relatively easily. In addition to that, it’s quite easy to extract a pattern from a CCTV recording, even if its quality is very low.
With that in mind, fingerprint and/or FaceID are a good balance between security on your device and convenience. Keep in mind, however, that even some of the best implementations of those can be fooled by 3D-printed models or sleeping people. Also, make sure you set up a reasonably complex and unique password as the backup authentication method.
#4. Make sure you encrypt your device
An important step in securing data on your smartphone or tablet is encrypting it. The idea here is that the whole storage of the device gets encrypted every time the phone is locked. The encryption makes it next to impossible to recover the information without unlocking the device.
To turn on the encryption, set up your authentication methods first. Then go to Settings — Encryption and Credentials, and tap Encrypt phone. (The exact names of menu items may vary on different phones, but you get the idea.) The initial encryption process may take up to an hour. And afterward, you probably won’t be able to notice any change in the performance of the device.
#5. Do you need antivirus? It depends
For the experienced Windows users among us, having an antivirus installed on every device sounds like an obvious security measure. However, on mobile devices, it might not be as useful as it is on a PC.
First of all, there’s no way an antivirus suite can work on a mobile device in the same way as it does on a PC, always monitoring everything that’s going on in the system and periodically scanning the storage. This kind of operation would deplete the battery in a few hours.
As a result, a mobile antivirus would normally only scan the apps as they’re installed on the device. This functionality is superficial, however, if you only install applications from the Play Store. Google has a protection system of its own. This system makes sure both the app and the device are not infected by known malware.
In summary, it only makes sense to have a third-party antivirus if you, for any reason, often need to install applications from outside of trusted app stores. In that case, look for the software coming from companies with experience in fighting malware on desktop platforms that have built a reputation and trust over the years.
#6. Get a password manager
Just like on a desktop, a good mobile password manager is your friend. A human can’t possibly remember more than a few secure passwords, which leads either to password re-use on different services or the setting of insecure passwords, both of which put data security in serious jeopardy.
With a password manager installed, you’d only need to remember one master password that unlocks the storage. That way, all the passwords you use elsewhere can be different and secure. Most password managers on the market these days offer a mobile version. You can choose the one you like and keep it handy on your home screen.
#7. Set up always-on VPN with a whitelist
Setting up a secure connection through a VPN server is certainly one of the best information security practices. Simply speaking, any data you send to or receive from the Internet would be routed through an additional server. This is a good way to improve privacy, especially when using public Wi-Fi networks.
This brings us to the always-on VPN option that’s available on Android. Generally speaking, you don’t need VPN at home or when browsing on a mobile network (provided you trust its operator). However, there’s a way to make things work optimally using a whitelist. The latter option is available through most VPN clients and allows you to choose trusted Wi-Fi and mobile networks where a VPN connection isn’t necessary. On all other networks, VPN would turn on automatically.
#8. Turn off USB debugging
You shouldn’t have it on in the first place if you’re not a mobile developer. Simply speaking, USB debugging is a special mode in which your phone allows access to certain parts of its storage when connected via USB to a computer.
When you have this option on, it’s a security risk for your device. To change it, you need to so go to Settings — Developer options, and check that USB debugging is turned off. This won’t affect your ability to connect your phone to a PC to copy files or tether the Internet connection.
#9. Disable location tracking if necessary
Having your location data accessible for various apps and services on your phone — from navigation to ordering takeaway — is often very convenient. In some cases, however, you may want to make sure this data is not being accessed, collected and stored anywhere. This would be a sensible thing to do when the location itself gives up sensitive data about you, like a hospital, or entertainment venue, or even a certain city or country.
In order to block geopositioning as much as possible without actually turning off the phone, follow these two steps.
First, turn off system-level location tracking and make sure no apps have the permissions to access your location data. Head to Settings — Security to do the former and Settings — Apps — App permissions — Location to do the latter. The reason to do both of these steps is to make sure you won’t accidentally allow any app to access your geopositioning data from a dialogue window.
Second, don’t use Wi-Fi, and set up a VPN killswitch. Even with GPS location tracking off, any app could theoretically use your external IP address and/or the names of Wi-Fi networks in the vicinity to figure out your location, often with GPS-like precision. To avoid that, keep your Wi-Fi module off and your VPN client on. As an additional precaution, most VPN clients offer a killswitch option. It means that any traffic that’s not going through a VPN would be automatically blocked.
#10. Use hardware 2FA
Hardware-based two-factor authentication (2FA) is arguably the most failsafe way to protect your accounts in various apps and online services. While traditional 2FA used to rely mostly on one-use passcodes delivered via text messages, SIM swapping has made it extremely insecure.
Another traditional implementation of 2FA is via a mobile app like Google Auth. However, if you lose or break your phone, it can be very complicated to set it up again.
With a hardware key, you can authorize online by connecting the key to your device via NFC, USB-C, or Lightning port. If you lose your phone, you can still use your key for authentication on another one. If you lose the key itself, removing it from your online accounts only takes a few clicks.
Let’s sum things up. It is certainly possible to make your Android device reasonably secure without making everyday use extremely inconvenient. Generally speaking, you can avoid most of the threats by only downloading apps from trusted sources, choosing a secure authentication method, and using a VPN when on public Wi-Fi. Following the rest of the recommendations of this overview will make you an extremely hard target for any malicious actor.
The COVID-19 pandemic is an ongoing tragedy. Cases have grown exponentially, and the death toll continues to rise. In response to the outbreak, governments worldwide have placed tight restrictions on daily life; restrictions that have also transformed patterns of work. Suddenly, millions of workers have traded the office for a digital workplace.
Remote work is nothing new. It has been around for decades, and in recent times up to one in five businesses have made it the norm.
But now, that number is closer to five in five. Many more businesses now have to lean on a suite of cloud platforms in order to keep their employees connected and productive. Microsoft was quick to make premium features of its Teams collaboration platform free for six months to help companies cope. Slack has recorded a net increase of 7,000 customers since the beginning of February; 40% more than it typically has in a whole quarter.
As companies transition wholesale to a digital workspace, the question arises: How can they stay secure? How can they ensure that the third party cloud channels they now rely upon are safe and compliant?
Invisible Employee Interactions
In December of last year, the CEO of a luggage startup Away ended up resigning because she helped foster a toxic working environment on Slack. Lots of factors go into creating such an environment, and 99% of companies manage to use Slack just fine. However, brilliant though the platform is — it does come with some inbuilt challenges that need to be overcome.
In a conventional office setting, managers enjoy automatic oversight of employee behavior.
They can overhear inappropriate remarks, or detect signs of bullying, and swiftly refer incidents to HR. There are a limited amount of private, one-on-one or small group interactions, and so very little goes undetected by the people whose job it is to enforce company policies.
But within collaboration platforms like Slack and Microsoft Teams? In effect, a company’s entire water cooler environment becomes digital, and far less visible.
Instantly, HR and compliance teams are faced with a major visibility issue. They can set company policies, but they have must confront challenges of speed and scale. Some enterprises can produce 40-70,000 Slack messages per day. If they can’t see what staff are saying to one another, it’s difficult to ensure compliance or a safe work environment.
Invisible Customer Interactions
Now that many more companies have moved wholesale to a digital workspace, it isn’t just internal interactions that are the issue.
In an office environment, there are easy ways to monitor and refine how employees are communicating with customers. People overhear or supervise customer calls. Lots of rendezvouses happen in person. Discussions take place at events like conferences or industry meet-ups.
But now? More than ever, companies’ interactions with their customers will be occurring within social media and messaging apps. For most industries, these are the only points of contact they have left.
Under the COVID-19 lockdown, there are no office appointments, no working lunches, no industry events, no shop floor. All that is left are digital cloud channels. Digital — is where all of the customer communication now lives.
And once again: Companies have no visibility here. Many industries face stringent regulations regarding how they can communicate with customers. Pharmaceutical companies have to monitor conversations for mentions of adverse events or off-label usage.
Financial services institutions need to watch for promissory language and capture all complaints. Every industry has its own examples. But if compliance teams can’t even properly monitor the platforms that their employees are using, how can they do their jobs?
Too Many Cyberattacks to Track
Malicious cybercriminals are rubbing their hands at the prospect of millions of people trading important and sensitive information online.
As large droves of office employees move to the digital workplace to continue operations during the COVID-19 pandemic, cybersecurity experts are understandably urging remote workers to strengthen their existing security measures.
In a recent public alert, the Cybersecurity and Infrastructure Security Agency (CISA), the cyber division of the US Department of Homeland Security, urged remote employees to secure “devices being used to remote into work environments with the latest software patches and security configurations.”
Software patches are important; this is solid advice. But this isn’t enough, because staying secure is about far more than repelling hackers.
Some of the worst cyber threats are phishing attacks, malware, and acts of account impersonation. With the velocity of online communications, these threat vectors become nigh impossible to track or react to using most companies’ existing tools.
80% of all data breach incidents reported in 2019 were related to phishing.
Here is a report according to the 2019 Verizon Data Breach Investigations Report. Phishing links can come in across any platform, from WhatsApp to LinkedIn direct messages. When enterprises’ security teams have no way to proactively monitor possible threats, they are flying blind.
Post-Perimeter, AI-Driven Solutions
The challenges of securing the digital workspace in the COVID-19 era all come down to this: we live in a post-perimeter age.
Once, companies established a perimeter, with firewalls and authentication systems, and everything worked mostly fine.
But operations are now distributed across a fragmented digital ecosystem. When your enterprise security is reliant on external, unregulated channels, you have a problem. The intelligence you need to mitigate digital risk and stay secure and compliant, is not as accessible.
The scale and speed of internet communications means that ensuring securing beyond the perimeter is very difficult. The task is beyond human intelligence.
Take “sampling,” for example. Too many digital signals are coming in all the time, and so security teams assess 10% of the overall pool and then apply the results to the other 90%. Taking a sampling works, in a way. But it is far from perfect.
Properly securing the digital workspace can only be done by recruiting AI and machine learning.
Only an AI-driven platform can constantly monitor every relevant digital endpoint.
Only an AI-driven platform can constantly monitor every relevant digital endpoint. Only an AI-driven platform can apply policies to every single message and post, via customizable policies. Modern digital risk protection demands the data aggregation, rapid data processing, and instantaneous execution powers of AI systems.
The Solution? Deploy a Dedicated Digital Risk Platform ASAP
COVID-19 is seeing many enterprises migrate their whole enterprise into cloud channels. But to secure the modern digital workspace, third party cloud channels cannot remain black boxes.
Companies need to have full insight into how their employees are using every digital platform, so they can apply the relevant policies.
Until the COVID-19 pandemic ends and normalcy resumes, remote work is the new reality for many businesses. It’s in their best interest to secure their new digital workplace.
Microsoft released its new Chromium-based browser earlier this year, but what does this new browser mean for the general users and should they give it a try? Yes, if you spend a good amount of time browsing the Internet and if you’re a little concerned about your privacy.
A web browser amongst others is your gateway to the Internet and thus it knows a lot about you and your habits. Every bit of information you exchange on the Internet parses through the web browser. Having a certain degree of control and transparency over your browsing data and its usage must be appreciated.
The new Edge browser provides you with a simple to find 3 level privacy management tab: Basic, Balanced and Strict to choose from, depending on how comfortable you are with marketers being able to target you with ads. Unsurprisingly, it is much more difficult to get to this setting tab on Chrome.
It is much easier for tracking companies to access your information and make money off it if you’re a Chrome user. Even Firefox provides anti-tracking features but Chrome doesn’t.
With the new Chromium-based Edge browser, Microsoft attempts to revive itself in a space it has been losing for almost a decade. With all the new features and advantages over Chrome, it still faces an uphill battle of winning back its long lost users.
Microsoft edge.
We should not forget that Google is an Ad company. It invests substantially to build and maintain a browser which is the most widely used on the planet. But since it relies on your data for revenue, it almost does next to nothing to prevent tracking of your data even by third parties.
Chrome is and has been the undisputed king in the browser space with a solid engineering team behind the wheels and a loyal user base.
But The Chrome Experience
67 percent of all computer-based browsing is done on Chrome as of January 2020. Google leverages this overwhelming lead to its advantage by selling your data while providing you with free personalized apps and services that use this data extensively.
More than 40 percent of people who buy a Windows machine download Chrome using Internet Explorer as their first major activity on their machine. One of the biggest and less talked about the reason why people switch to Chrome is that they have become used to the look and feel of it and reject anything that feels even scantily different. Kudos to the engineering team at Chrome to manage such patronage.
This is probably why the previous feature-rich Edge with Microsoft’s edge HTML rendering engine failed miserably. It could also be attributed to Chrome beating Firefox substantially with the later having only 4.7 percent market share globally.
With the new Edge, Microsoft acknowledges its boo-boo and looks to get back in the browser space albeit some obscurity.
Living on the Edge
Since the new Edge is based on Google’s Chromium engine, you get the Chrome browsing experience annulling a major reason for losing users.
In testing, the rendering time of Edge is found to be faster than Chrome. The RAM consumption of Edge is found to be less than 14 percent of that of Chrome which could be a major respite for a lot of users as Chrome is known for eating up RAM.
With the seemingly right steps taken by Microsoft using its engineering prowess, it has to win back the users it has lost since the inception of Chromium. The new Edge browser has to outdo Chrome consistently in the time to come and create an ardent user base like it has done recently in a very short time with Visual Studio Code.
Microsoft uses Windows which accounts for almost 80% of the operating system market to prompt users to switch to Edge. It has done this previously with aggression at times but has come to no avail.
Interesting times ahead
By moving to Chromium, Microsoft interestingly plays it in the hands of Google, the owner of the Chromium repository, and hence decides the engineering direction the project takes. It might surprise a few but Microsoft is already invested in Chromium with Visual Studio Code and Microsoft Teams which are based on Electron, a Chromium-based framework.
While Microsoft would again use Windows alongside other marketing strategies to push its baby in the hands of users, Google has the stewardship on the engineering decisions and a throng of lawyers to monitor any wrong practice by MSFT.
It would be interesting to see Microsoft market Edge and leverage it for its consumer products while Google tackles this push offering a successful but virtually identical product.
If you consider yourself as having a “super hardcore work ethic, talent for building things, common sense and trustworthiness,” then great news. You just met all of Elon Musk’s criteria for stellar career-building spaceships, proving it doesn’t take a rocket scientist to get a job at SpaceX.
The outspoken entrepreneur recently posted a tweet announcing a career day at the aerospace firm, in which he also promised that “the rest we can train.”
It’s a significant promise and one that ensured SpaceX career day would have candidates lining up out of the door. But the fact is that making the most of employee training is an area where many companies struggle.
A 2019 training industry report stated that 37% of companies surveyed wanted to increase the effectiveness of their training programs, in a year when training budgets had also increased by nearly 42%. But, in the same way that gaining a foot in the door at SpaceX isn’t rocket science, neither is improving the quality of your employee training programs. Feedback loops exist everywhere.
Future-ready business leaders who can learn to leverage these feedback loops in the context of employee development will find it easier to implement more agile, fit-for-purpose, and engaging training programs.
Recognize the challenges
Perhaps one of the most critical challenges facing today’s talent development teams is the move to digital adoption.
A new employee joining your organization faces dozens of systems and tools, some of which may already be familiar to them, while others may be used for the first time. A report from the Cloud Security Alliance survey found that the average enterprise runs 464 applications, of which employees use around 260.
Furthermore, the shift to digital also means organizations are increasingly leveraging automation for routine tasks, using software that connects platforms, robotics, and artificial intelligence. This shift means that employees are increasingly valued for their soft skills. So much so that participants in a Workplace Learning report commissioned by LinkedIn cited soft skill training as the most critical area of focus.
These training demands also have to be balanced against the need for employees to become productive as soon as possible after joining.
While a structured learning program is known to be beneficial in long-term retention, there’s also the challenge of keeping employees engaged through the right balance of on-the-job versus classroom-based learning.
Feedback is a gift
In management contexts, the concept of feedback is often limited to discussions around the annual performance review. But the traditional performance review model is outdated and doesn’t work.
According to a survey by Gallup, only 14% of employees strongly agree that their performance review inspires them to improve. In general, the annual performance meeting is a one-directional feedback process that can be uncomfortable and draining – for managers and employees alike.
However, this unfortunate outcome is the result of the contrived manner in which the annual performance review mandates the delivery of feedback. It doesn’t apply to the concept of feedback itself, which works successfully in many other contexts.
Feedback loops exist everywhere.
For example, when we get cold, our bodies trigger a series of reactions, such as shivering, that tells us to move somewhere warmer. Artificial intelligence algorithms are trained using feedback loops that tell them whether their output was correct, or not. So why don’t we apply the concept more to our employee communications and development?
Introducing feedback loops to employee training models
Using feedback loops in employee training doesn’t need to involve implementing new policies or learning management systems. In fact, doing so risks the same pitfalls as the dreaded performance review.
Instead, you can incorporate feedback loops more holistically and naturally, ensuring that they become woven into the cultural fabric of your organization.
Here are a few pointers.
1. Set objectives
Every training activity should have clear objectives. What knowledge, skills, or understanding should your employees have gained at the end? How will these help them in their new roles and help achieve the goals of the company?
Use these outcomes as a way of gauging the success of the training. If the desired results aren’t there, make adjustments accordingly.
2. The structure is essential – but avoid being prescriptive
Focusing on objectives and outcomes enables you to take a more flexible approach to employee training programs. Just because an organization uses 200 different systems doesn’t mean every employee needs training in all of them. You can use a template or high-level plan to categorize various activities and maintain some structure. For example, compliance-based training is generally essential, but software training can be undertaken on a need-to-have basis.
Tailoring a structured program to an employee’s individual needs allows them to get up and running more quickly. At the same time, the organization saves on training budgets for skills the employee may already have learned elsewhere.
3. Solicit feedback on progress and engagement
Employees should be encouraged to provide feedback as an integral part of their onboarding. Feedback could be formalized, perhaps as a survey at the end of a training exercise. But there are always opportunities for informal, qualitative feedback. During training sessions, whether on the job or elsewhere, take regular breaks to check to understand, offer opportunities for questions, or ask how participants think the training is going.
Feedback doesn’t need to be formalized into performance reviews or employee satisfaction surveys to be meaningful. Creating a culture of open communication means that feedback loops become incorporated into the mindset of employees and managers. In this way, the organization becomes more resilient to feedback and more easily able to make minor adjustments on the fly.
4. Measure return on investment
There is no “one-size-fits-all” approach here. However, some metrics can help to measure the effectiveness of training programs.
Retention is the most universally popular among executives, managers, and talent developers, but performance measurements and qualitative behavior changes can also be effective.
Getting looped in on training
It’s time to consign the dismal annual performance review to the past. Feedback loops offer a way to ensure that your approach to training employees is agile enough to develop, along with the needs of the organization as a whole.
Establish an open feedback culture from day one of onboarding and create the best possible chance for enduring and fruitful employee relationships.
Big data analysis is the next innovative technique that the telecommunications (telecom) sector is deploying. Big data will tame the telecommunications abundance of data, and enable harvesting information “gold” from existing data storage. We know that Big data includes paper as well as innovating new ways of collecting numerous touch-points of data.
Telecoms have always created and analyzed vast quantities of data about their customers — both financial and administrative transactions and operations. Telecom service providers have always been early adopters of data-related technologies. Included in this tech are old-school statistical analysis followed by data mining, knowledge management, and business intelligence applications.
According to a MindCommerce study:
“An average telecom operator generates billions of records per day, and data should be analyzed in real or near real-time to gain maximum benefit.”
For communications service providers (CSPs) to make use of insightful knowledge, much of the data must be processed in near real-time. Traditional systems would take days, weeks, even months to process the data, not to mention the complex variety of structured and unstructured data that would trip up legacy applications.
Big data analysis is used by telecommunications service providers (telecoms) in a variety of intriguing and practical ways that even a few years ago would not have been possible.
CSPs monitor network traffic to identify problems to make decisions that improve service and customer satisfaction. The CSP operational diagnostic information also helps prioritize investment in the networks’ physical and technical assets.
Telecoms analyze the metadata of call records to pinpoint fraudulent activities that protect their customers and criminal investigations.
Telecoms index mountains of documents, images, and manuals within minutes to help call center agents resolve customer issues. Agents can now quickly search for information previously locked away in paper. The paper resolution reduces the call handling time, thereby reducing labor costs. For commerce, these finable documents boost efficiency, which can increase both employee and customer satisfaction — and retention.
CSPs evaluate usage patterns to help businesses create service plans that better suit their customers’ needs. When you take care of your customers, it cuts customers’ costs — and more importantly to telecom companies — it helps them predict and reduce churn.
Telecom companies even use data pouring in from social media networks to optimize the content of, and investment in marketing campaigns on the fly.
Telecom big data sources include the obvious such as phone calls, emails, and multi-media messages. The telecom big data authority also extends to geo-spatial information, transaction metadata, social media usage, log data, file downloads, sensor data, and more.
History of Big Data
Until recently, the variety and velocity of data were vexing. Disparate data being created at rapidly increasing rates presented insurmountable storage, and processing dilemmas.
It may seem like big data is a recent scourge, but data recording and ciphering its value has been going on since 7000 B.C. The earliest modern 20th-century big data problems were US federal government projects.
If only Franklin Roosevelt had known that he was a pioneer in big data. Read more about these and other intriguing big data history facts.
But it would be the 21st century, in 2005, before Roger Mougalas from O’Reilly Media named the problem of managing and processing large sets of data. All of the data produced and gathered cannot be conquered using traditional business intelligence tools.
The term “big data” came long after the knowledge that there was a plethora of information that existed. And each year, each month and each day since, the amount of data have been recognized — innovations have been critical to contain it all.
The Internet of Things (IoT) drives the unfathomable speeds and quantity of data from sensors that are used for calculations. These calculations are lightning-quick, often life-preserving. By their very nature, these calculations must result in instant decisions and actions.
Telecom’s Big Data Analytics Trends
Some of the hyped-topics in telecom technology, such as virtual reality (VR) and augmented reality (AR), and even the latest 5G feel like it fizzles to many consumers. But the misunderstanding is mostly because of emerging innovations required to make the information happen. There is a latency between possibilities, and the human mind’s ability to take it all in.
All technology can’t be understood by the layperson, as raw material is presented for the next paradigm about-face. For sure, those technologies will deliver applications, both practical and entertaining, that will exponentially accelerate the creation of data. But for all persons to “get” or understand what is happening in the telecommunications field is like asking the community to understand a booster rocket. All people are not trained engineers, and they won’t have a firm grasp on the technical aspects.
For example, Kevin Westcott, Deloitte’s VP and leader of its US Telecom practice, predicts the popularity of e-sports to skyrocket. We all get that we want the popularity and reputation of e-sports to rise. But, this prediction was before the world-altering COVID-19 pandemic.
When you send millions of individuals into isolation — with little to do but seek out streamed entertainment — something will change in our world and the world of commerce. The forced isolation of the pandemic has brought cancellations of the entire seasons of the world’s hottest sports organizations. E-sports and the big data it generates will likely heat up faster than the predicted trends.
Also, legalized sports betting is on the rise following a 2018 US Supreme Court decision lifting the federal ban on sports betting.
To follow suit — several states have already legalized sports betting. 5G’s low-latency, high-volume communications will enable real-time sports betting. With 5G already being deployed in stadiums and sports bars, betting from your seat or barstool is imminent. (Yes, we are all hoping the stadiums and sports bars will be opened soon.)
Telecoms are at the ready — as they should be — to analyze every wager. New telecom connectivity technologies like 5G fixed wireless and satellite internet will offer the backbones needed for disruptive, big-data birthing applications.
The aggressive growth of smart homes and cars, video on demand, streaming apps, gaming, and other entertainment and educational applications will continue producing even higher volumes of data. The data will then be analyzed to glean the insights for businesses and other operational decisions.
Big Data Analytics Solutions
Big data analytics technologies are evolving right along with the technologies and supporting infrastructure. These are the very structures that created the formidable volumes of data in the first place. Companies must be able to collect data from different sources, analyze them, and distribute the information to disparate databases. Data centers or data warehouses are awaiting the intelligence, and depending on the data for the specific needs of the organization.
Marketing Campaigns
The challenge with big data projects is finding skilled resources with experience to create cutting-edge architectures and supersonic data processing applications. These data processing applications must support data-driven business models and near-real-time hyper-targeted marketing campaigns.
Problem-Solving Teams
Teams for solving big data problems must have a wide variety of engineers, analysts, business experts, and system integrators. These specialized teams are rarely found in-house for most companies, even large telecom service providers.
Outsourcing
Outsourcing and staff augmentation is frequently used for big data ventures. For example, Vates, a leading big data analytics and systems integration company in Latin America, is in the center of some of the biggest telecom big data projects in the world.
Global Telecom Company
Vates was hired by a global telecom company to bring its engineering and agile project management prowess to meld with the development of a system. Engineering teams located across the US, Chile, and Argentina are working on the development.
IBM Streaming Analytics
The combined in-house and outsourced team utilized IBM Streaming Analytics to develop architecture and big data analytics solutions. These processes take and analyze the unstructured and structured metadata from multiple sources in near-real-time.
One of the resulting systems was created using IBM Streams processes. The IBM system processes 35 million CSV files or 100 terabytes of data per month. You can read about these impressive big data analytics use cases for the Telecom company.
The Vates Expertise
In a follow-on project for the same telecom, Vates utilized its expertise. Vates works with data from different formats, that originate from varied locations. The company can create solutions capable of checking network quality (NQI) in near-real-time.
Receiving XML files with measurements as detailed as the antenna position, the company can calculate its deviations. The information enables the system to gauge signal quality at a particular location. With this microscopic big data, a technician can quickly make necessary corrections.
As big data technologies evolve, solutions will likely be developed by blended teams. These blended teams will be using outsourcing and staff augmentation to overcome the challenges of staying on the leading edge.
