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Benefits of Data Storytelling

Reading Time: 3 minutes

What is Data Storytelling?

Data storytelling is the most effective method for leveraging data to produce new knowledge, new choices, or new actions. It is an interdisciplinary profession that incorporates knowledge and experience from several domains such as communication, analysis, and design. It is used to solve a variety of issues and is employed in a variety of areas.

The majority of marketers have some narrative experience. When we talk about data storytelling, we’re talking about stories in which data is the main focus. The narrative’s purpose is to explain the information and its importance. There are many different types of stories, and most of them can be conveyed with the help of photographs, but only a handful do.

The most important elements of data storytelling:

Data: The cornerstone of any data story is a thorough study of correct, full data. Data analysis employing descriptive, diagnostic, predictive, and prescriptive analysis may help you comprehend the whole picture.

Narrative: A tale, also known as a verbal or written narrative, is used to express insights drawn from data, the context around it, and actions you advocate and hope to inspire in your audience.

Visualizations: Visual representations of your data and narrative may help you tell your message in a clear and memorable way. These might take the form of charts, graphs, diagrams.

The benefits of data storytelling

Data storytelling is comparable to human storytelling, but it includes deeper insights and supporting facts in the form of graphs and charts. Data storytelling simplifies complex information so that your audience can connect with your content and make key decisions more quickly and confidently.

Creating a data story that inspires others to act may be a really effective technique. People and your business may benefit from effective data storytelling. Some of the advantages of effective data storytelling include:

  • Increasing the value of your data and insights;
  • Interpreting difficult information and emphasising vital elements for the audience;
  • Giving your data a personal touch;
  • Adding value to your target audience and industry;
  • Developing your reputation as an industry and issue thought leader.

What makes a great data story?

It must be meaningful
This means that the information (including copy and images) must be appropriate for the audience’s present level of understanding and must assist them in achieving some sort of goal.


Perhaps your audience is internal, such as a presentation to leadership about the need of investing in a certain strategy or method. Or they might be external, such as a campaign to get them to test your solution.

In any case, consider what is important to them. The finest stories are those that appeal directly to people, and the more particular the person, the better.

It must have accurate data
This means that the data should come from a reliable source and/or be gathered in a method that accurately depicts what is required to convey a true tale.


Data made public by government institutions, intergovernmental organizations, university researchers, and established analysts are not only more accessible, but also transparent and verified.


The facts you utilize should assist you in telling the truth. It should be relevant to the audience’s needs and assist them in understanding just what they need to know to make an important decision.

A clear narrative is crucial
When it comes to narrative, we are all accustomed to the standard three-act structure with a beginning, middle, and finish.

For data storytelling, this typically implies that you need to learn about the issue first before diving into the data. You must also finish with a particular call to action—another distinction between a data story and a basic report.


Also, if your audience is not an expert, use clear language to avoid losing them in tricky jargon or complicated acronyms.

It should incorporate deliberate graphics

It implies that your graphics, whether images, graphs, or charts, should help your audience grasp what the data means.

What are your thoughts on data storytelling?

References:

https://www.analyticssteps.com/blogs/introduction-data-storytelling

https://online.hbs.edu/blog/post/data-storytelling

https://powerbi.microsoft.com/en-us/data-storytelling/

https://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-needs/?sh=7874148052ad

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Challenges of self driving cars

Reading Time: 3 minutes

While the arrival of self-driving automobiles has many potential benefits, it also has its own set of challenges. Technology is never flawless, and computers may be hacked. Furthermore, while autonomous cars will make our roads safer, they may also have unexpected societal implications, such as greater unemployment rates.

Some possible issues with self-driving automobiles are inherent in the existing layout and use of our highway infrastructure. Existing road conditions and signs, for example, as well as the transition phase in which some drivers on the road will use autonomous vehicles and others, will use traditional vehicles, might all provide substantial challenges to the adoption of self-driving cars.

Road conditions

Road conditions can be exceedingly variable and vary from location to location. There are smooth and well-marked wide roadways in certain situations. In other areas, the road is severely eroded, with no lane markings. Lanes are not well defined, there are potholes, hilly and tunnel routes with unclear external cues for orientation, and so forth.

Weather conditions

Another stumbling block is the weather. The weather might be bright and clear or wet and stormy. Autonomous vehicles should be able to operate in any weather situations. There is no possibility of failure or downtime.

Traffic conditions

Autonomous vehicles would have to enter the road and drive under a variety of traffic scenarios. They would have to share the road with other autonomous vehicles as well as a large number of humans. There are a lot of emotions involved whenever people are involved. The flow of traffic might be greatly monitored and self-regulated. However, there are times when someone may be breaching driving laws. An item may appear in unforeseen circumstances. Even a few centimeters per minute of movement matters in tight traffic. One cannot wait indefinitely for traffic to clear and for some prerequisite to begin moving. If there are more of these automobiles on the road waiting for traffic to move, it might lead to a heavy traffic.

Accident Liability

Accident liability is the most significant feature of self-driving automobiles. In the case of self-driving automobiles, the software will be the primary component that will operate the vehicle and make all critical choices. While the earliest concepts had a human physically stationed behind the steering wheel. Furthermore, owing to the nature of autonomous vehicles, the occupants will be primarily relaxed and may not be paying careful attention to road conditions. In instances where their attention is required, it may be too late to act by the time they need to.

Radar Interference

Lasers and radar are used for navigation in self-driving automobiles. The lasers are installed on the roof, while the sensors are located on the vehicle’s body. Radar operates by detecting radio wave reflections from nearby objects. When a car is on the road, it emits radio frequency waves that are reflected by other automobiles and things in the vicinity. The time required for the reflection is calculated to determine the distance between the automobile and the object. Based on the radar data, appropriate action is subsequently performed. Radar operates by detecting radio wave reflections from nearby objects. When a car is on the road, it emits radio frequency waves that are reflected by other automobiles and things in the vicinity. The time required for the reflection is calculated to determine the distance between the automobile and the object. Based on the radar data, appropriate action is subsequently performed. Will a car be able to discern between its own (reflected) signal and the signal (reflected or transmitted) from another vehicle when this technology is utilized for hundreds of cars on the road? Even if numerous radio frequencies are available for radar, it is doubtful that this frequency range would be insufficient for all cars made.

What are your thoughts on the self-driven cars? 

Are they going to become the reality?

References:

https://www.johndaylegal.com/potential-problems.html

https://www.vox.com/2016/4/21/11447838/self-driving-cars-challenges-obstacles

https://www.livescience.com/50841-future-of-driverless-cars.html

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AI in Emotion Recognition

Reading Time: 2 minutes

Emotion recognition is one of the various facial recognition systems that have evolved through time. Currently, facial emotion recognition software is utilized to allow a specific program to inspect and process human facial expressions. Using complex image dispensation, this program acts like a human brain, allowing it to recognize emotions as well.

AI identifies and examines various facial expressions to use them with extra information. This is beneficial for a number of purposes, including investigations and interviews, and enables authorities to identify a person’s emotions using just technology.

What emotions can be recognised by AI system?

  • Anger
  • Joy
  • Surprise
  • Fear
  • Sadness

How does face recognition works?

Every year, facial expression-detecting technology becomes increasingly advanced. The AI used recognizes and examines facial expressions based on a variety of parameters to determine what emotion the individual is displaying. Factors like:

  1. Using Metrics:

    When a person exhibits a certain feeling or expression (for example, a grin) together with a level of confidence. It may be conceived of as a detector by employing metrics: The score climbs from 0 (no expression) to 100 (expression fully present) when the emotion or facial expression arises and strengthns .
  1. Using Datasets:

    The data consists of grayscale pictures of faces with 48×48 pixels. The faces have been automatically positioned such that they are about centered and take up around the same amount of area in each image. The aim is to categorize each face depending on the emotion expressed in the facial expression (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral).
  1. Using ParralleDots:

ParralleDots has developed an AI-based solution that developers may utilize after training on a specified data set to detect images.

The process of developing an emotion detection model, like any other AI project, begins with project planning and data collecting. More information on the steps of an AI project and the collecting of datasets may be found in our dedicated articles.

Let’s take a break from the data acquired for an emotion identification model. It is a vital (and time-consuming) component of the future algorithm since it must be gathered, analyzed, safeguarded, and annotated. This data is necessary to train the emotion recognition model, which is essentially a procedure that teaches the machine how to interpret the data you provide it.

Finally, is important to understand that there are unsolved issues and hazards associated with emotion detection. The intellectual underpinning of this technology is dubious at best, and privacy concerns have caused a few big cities in the United States to ban its usage by the police. However, there is no need to be disappointed by these flaws. Emotion recognition is still in its early stages, but it will get stronger, more accurate, and more secure. With high-quality data and annotation, cultural understanding, and privacy restrictions in place, emotion detection algorithms might be among the most useful technologies of our time.

What do you think about such innovation?

References:

https://recfaces.com/articles/emotion-recognition

https://tudip.com/blog-post/what-is-emotion-recognition/

https://link.springer.com/article/10.1007/s10919-020-00340-4

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The Biggest Cloud Security Challenges

Reading Time: 4 minutes

What is Cloud security?

Cloud security is a branch of cyber security that focuses on safeguarding cloud computing platforms. This involves maintaining data privacy and security across internet infrastructure, apps, and platforms. The efforts of cloud providers and the clients that utilize them, whether an individual, small to medium corporation, or enterprise, are required to secure these systems.

Cloud providers use always-on internet connections to host services on their servers. Because their firm relies on consumer confidence, they deploy cloud security solutions to keep client data private and secure. However, cloud security is also partially in the hands of the customer. Understanding these aspects is critical for a successful cloud security solution.

Why Cloud security is imortant?

Business and personal data resided locally in the 1990s, and security was also local. Data would be stored on your personal PC’s internal storage and on business servers if you worked for a firm.

The introduction of cloud technology has compelled everyone to rethink cyber security. Your data and apps may be bouncing between local and distant servers — but they’re always online. If you use Google Docs on your smartphone or Salesforce software to manage your clients, the data might be stored anywhere. As a result, safeguarding it becomes more complicated than before it was only a matter of preventing unauthorized individuals from accessing your network.

Cloud security necessitates certain changes to prior IT processes, however it has grown increasingly important for two reasons:

  • Convenience over security. Cloud computing is rapidly becoming a key technique for both business and personal use. Because of innovation, new technology is being introduced faster than industry security regulations can catch up, putting additional responsibility on users and providers to address accessibility concerns.
  • Centralization and multi-tenant storage. Every component, from fundamental infrastructure to minor data such as emails and documents, may now be discovered and accessed remotely via 24/7 web-based connections. All of this data collection on the computers of a few large service providers can be quite harmful. Threat actors may now target enormous multi-organizational data centers and trigger massive data breaches 

What are the biggest Cloud security challenges?

As risks have developed and more sophisticated new assaults have emerged, it is now more vital than ever for enterprises to adopt security-first mindsets. Having said that, here are some of the most pressing difficulties we face this year, as well as how cloud security solutions may assist your firm in overcoming them.

Data Breaches

Failure to handle data properly (through purposeful encryption) exposes your company to significant compliance concerns, not to mention data breach penalties, fines, and substantial breaches of consumer confidence. Regardless of what your Service-Level Agreement (SLA) states, it is your responsibility to secure your customers’ and employees’ data.

IT workers have traditionally had extensive control over network infrastructure and physical hardware (firewalls, etc.) used to protect proprietary data. Some of those security controls are abandoned to a trusted partner in the cloud (in all scenarios, including private cloud, public cloud, and hybrid cloud scenarios), implying that cloud infrastructure might raise security concerns. Choosing the proper vendor with a proven track record of deploying robust security measures is critical to overcome this difficulty.

Compliance With Regulatory Mandates

It’s typical for corporations, particularly small and medium-sized businesses, to believe that just cooperating with a cloud solutions provider provides them with optimum security. However, there is more to it than meets the eye.

The correct cloud security solutions give the technological capability to comply with regulatory demands, but constant supervision and detailed attention to detail are required. The cloud provider provides cloud security under the responsibility model, whereas the end user provides cloud security.

Data loss

It’s natural to be concerned about the security of business-critical data when it’s moved to the cloud. Losing cloud data, whether by inadvertent deletion and human mistake, criminal manipulation including malware installation (i.e. DDoS), or a natural disaster that shuts down a cloud service provider, may be fatal for commercial businesses. A DDoS assault is frequently only a distraction for a more serious danger, such as an effort to steal or erase data.

To address this difficulty, it is critical to have a disaster recovery plan in place, as well as an integrated system to combat hostile assaults.

What types of cloud security solutions are available?

Identity and access management (IAM)

Enterprises may utilize identity and access management (IAM) technologies and services to install policy-driven enforcement methods for all users seeking to access both on-premises and cloud-based services. IAM’s fundamental capability is to generate digital identities for all users, allowing them to be actively monitored and limited as needed throughout all data exchanges.

Data loss prevention (DLP)

DLP (data loss prevention) services provide a set of tools and services designed to safeguard the security of regulated cloud data. DLP systems secure all stored data, whether at rest or in motion, by combining remediation warnings, data encryption, and other preventative measures.

Security information and event management (SIEM)

Security information and event management (SIEM) is a complete security orchestration solution for cloud-based settings that automates threat monitoring, detection, and response. SIEM technology, which uses artificial intelligence (AI)-driven technologies to correlate log data across many platforms and digital assets, enables IT professionals to successfully deploy network security policies while responding fast to any possible threats.

Business continuity and disaster recovery

Data breaches and disruptive disruptions can occur regardless of the precautionary measures that enterprises put in place for their on-premise and cloud-based infrastructures. Enterprises must be able to respond swiftly to newly identified vulnerabilities or large system failures. Disaster recovery solutions are a must-have in cloud security because they offer enterprises the tools, services, and standards needed to fast data recovery and restart regular company operations.

The security risks and challenges associated with cloud computing are not insurmountable. Enterprises may reap the benefits of cloud technology with the correct cloud service provider (CSP), technology, and planning.

The CDNetworks cloud security solution combines web speed with cutting-edge cloud security technologies. With 160 points of presence, our customers’ cloud-based assets are safeguarded with 24/7 end-to-end protection, including DDoS mitigation at the network and application levels, and their websites and cloud applications are expedited on a worldwide scale.

Resources:

https://www.skyhighsecurity.com/en-us/cybersecurity-defined/what-is-cloud-security.html

https://www.ibm.com/topics/cloud-security

https://www.kaspersky.com/resource-center/definitions/what-is-cloud-security

https://www.startus-insights.com/innovators-guide/cybersecurity-trends-innovation/

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AI and Healthcare

Reading Time: 3 minutes

AI and similar technologies are becoming more and more common in business and society, and they are starting to be used in healthcare. These technologies might change many facets of patient care, as well as internal administrative procedures at pharmaceutical organizations.

Numerous studies have already shown that AI is capable of doing important healthcare jobs including illness diagnosis as well as better than humans.

One of AI’s greatest potential advantages is to keep people healthy so they don’t need doctors as frequently, if at all. People are already benefiting from consumer health applications of artificial intelligence (AI) and the Internet of Medical Things (IoMT).

Applications and apps for technology support proactive maintenance of a healthy lifestyle and encourage individuals to adopt better behaviours. It gives customers control over their health and well-being.

Diagnose cancer

AI is already being used to more precisely and early diagnose diseases like cancer. The American Cancer Society claims that a large percentage of mammograms provide misleading findings, telling one in two healthy women they have cancer. Mammogram reviews and translations can now be done 30 times quicker and with 99% accuracy thanks to AI, which eliminates the need for pointless biopsies.


AI is also being used in conjunction with the growth of consumer wearables and other medical devices to monitor early-stage heart disease, allowing doctors and other caregivers to more effectively monitor and identify potentially fatal events at earlier, more curable stages.


Decision making process

AI can assist clinicians in taking a more comprehensive approach to disease management, better coordinate care plans, and help patients to better manage and comply with their long-term treatment programmes, in addition to helping providers identify chronically ill individuals who may be at risk of an adverse episode.


For more than 30 years, medical robots have been in use. They vary from basic laboratory robots to extremely sophisticated surgical robots that may work with a human surgeon or carry out procedures on their own. They are used in hospitals and labs for repetitive jobs, rehabilitation, physical therapy, and assistance for those with long-term problems in addition to surgery.

Training process


AI makes it possible for trainees to experience realistic simulations in a way that is not possible with straightforward computer-driven algorithms. A trainee’s answer to a question, choice, or piece of advise can be challenging in a manner that a person cannot because of the development of natural speech and an AI computer’s capacity to draw instantaneously from a massive library of situations. The training software may take into account the trainee’s prior replies, allowing it to modify the tasks to fit their learning requirements continuously.

Additionally, training can be done anywhere thanks to the power of AI integrated in smartphones, making it feasible to do brief catch-up sessions following challenging cases in a clinic or while travelling.

In conclusion, AI have the potential to revolutionize end-of-life care by allowing patients to stay independent for long periods of time, decreasing the need for hospitalization and care facilities. AI mixed with developments in humanoid design are allowing robots to go even farther and have ‘conversations’ and other social interactions with people to keep ageing minds sharp.

Resources:

https://www.wired.co.uk/article/cancer-risk-ai-mammograms

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/

https://www.insiderintelligence.com/insights/artificial-intelligence-healthcare/

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