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How Artificial Intelligence Can Help Tackle Emotionally Charged Situations

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How Artificial Intelligence Can Help Tackle Emotionally Charged Situations

An increasing number of jobs are incorporating artificial intelligence. But artificial intelligence applications tacking emotional problems is something rarely heard of.

Researches reveal that people who face workplace harassment don’t always report it to concerned authorities– fear of judgment and discomfort in recalling emotionally disturbing situations being among the many reasons. However, scientists believe that people shying away from human assistance might be able to open up in front of a tech.

Let’s look at some examples of artificial intelligence applications assisting humans in emotional situations.

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Spot:

Back in February 2018, Shaw and his partners who are software engineers by profession introduced Spot. This web-based chatbot is powered by artificial intelligence and helps people share and report painful incidents. The app incorporates expertise of psychologists as well as police personnel and has a robust interview technique ensuring recorded narratives are highly accurate. Moreover, the person interacting has the option to remain anonymous.

Spot is trained to gather information from the person’s initial description of the incident and respond to cues by posing specific questions, but never asking leading questions that might intimidate the user. Finally, a detailed and time-stamped PDF report is generated, which the user can choose to share or keep personal.

Advantages of artificially intelligent tools dealing with human emotions:

  • Apps are accessible 24/7
  • No prior appointment with HR is needed
  • Eliminate inconsistencies associated with human interactions
  • Help overcome a common human weakness– emotional memory recall
  • Machines can act more logically in situations where humans fail to think clearly.

Navigating through human emotions:

Ixy is another AI based app that aims to minimize anxiety in human chats by facilitating interactions over texts. It samples texts to help users understand how he/she is perceived by others.

Israeli enterprise Beyond Verbal has launched ‘’emotional analytics’’ software, which is a patented technology that measures the emotional factor of voice based on its modulation. This tech is employed in call centers so worker interactions can be fine-tuned as per customer needs. Other important uses of this software include examining employee morale and improving the working of AI virtual assistants by helping understand elements between lines of human conversations.

Yoram Levanon, the inventor of Beyond Verbal tech and its chief science officer, visualizes the development of applications more advanced than the ones described above, such as virtual assistants that can examine vocal biomarkers and predict a person’s emotional and physical state. He firmly believes that modern AI apps need to work in tandem with human emotions; that’s the key for AI to be complementary to human work and not its replacement.

To help, not replace humans:

Recently, there’s been a lot of unease and insecurities associated with AI. There’s fear that AI might eat into our human side and corrode human emotions. But the opposite of this is actually possible and these apps prove that point. Done correctly, machines can help us become better humans.

In today’s tech-driven society, the first step in unlocking the powers of modern equipment is enrolling for comprehensive Artificial Intelligence Certification Courses. From factory machinery, to chatbots, and now even human emotions, everything is forming inseparable bonds with AI. Join the leading artificial intelligence training institute in Gurgaon DexLab Analytics and begin a successful journey in this field. For course details, look up the brochure on DexLab’s website.

 

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The ABC Basics of Apache Spark

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The ABC Basics of Apache Spark

Amazon, Yahoo and eBay has embraced Apache Spark. It’s a technology worth taking a note of. A bulk of organizations prefers running Spark on clusters along with thousands of nodes. Till date, the biggest known cluster consists of more than 8000 nodes.

Introducing Apache Spark

Spark is basically an Apache project tagged as ‘lightning fast cluster computing’. It features a robust open-source community and is the most popular Apache project right now.

Spark is equipped with a faster and better data processing platform. It runs programs faster in memory as well as on disk as compared to Hadoop. Furthermore, Spark lets users write code as quickly as possible – after all, you’ve more than 80 high-level operators for coding!

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Key elements of Spark are:

  • It offers APIs in Java, Scala and Python in support with other languages
  • Seamlessly integrates with Hadoop ecosystem and other data sources
  • It runs on clusters controlled by Apache Mesos and Hadoop YARN

Spark core

Ideal for wide-scale parallel and distributed data processing, Spark Core is responsible for:

  • Communicating with storage systems
  • Memory management and fault recovery
  • Arranging, assigning and monitoring jobs present in a cluster

The nuanced concept of RDD (Resilient Distributed Dataset) was first initiated by Spark. An RDD is an unyielding, fault-tolerant versatile collection of objects that are easily operational in parallel. It can include any kind of object, and supports mainly two kinds of operations:

  • Transformations
  • Actions

Spark SQL

A major Spark component, SparkSQL queries data either through SQL or through Hive Query Language. It first came into operations as an Apache Hive port to run on top of Spark, replacing MapReduce, but now it’s being integrated with Spark Stack. Along with providing support to numerous data sources, it also fabricates several SQL queries with code transformations, which makes it a very strong and widely-recognized tool.

Spark Streaming:

Ideal for real time processing of streaming data – Spark Streaming receives input data streams, which is then divided into batches only to be processed by Spark engine to unleash final stream of results, all in batches.

Look at the picture below:

The Spark Streaming API resembles Spark Core – as a result, it becomes easier for programmers to tackle for batch and streaming data, effortlessly.

MLib

MLib is a versatile machine learning library that comprises of numerous fetching algorithms that are designed to scale out on a cluster for regression, classification, clustering, collaborative filtering and more. In fact, some of these algorithms specialize in streaming data, such as linear regression using ordinary least squares or k-means clustering.

GraphX

An exhaustive library for fudging graphs and performing graph-parallel operations, GraphX is the most potent tool for ETL and other graphic computations.

Want to learn more on Apache Spark? Spark Training Course in Gurgaon fits the bill. No wonder, Spark simplifies the intensive job of processing high levels of real-time or archived data effortlessly integrating associated advanced capabilities, such as machine learning – hence Apache Spark Certification Training can help you process data faster and efficiently.

 
The blog has been sourced from ― www.toptal.com/spark/introduction-to-apache-spark
 

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Data Driven Projects: 3 Questions That You Need to Know

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Data Driven Projects: 3 Questions That You Need to Know

Today, data is an asset. It’s a prized possession for companies – it helps derive crucial insights about customers, thus future business operations. It also boosts sales, predicts product development and optimizes delivery chains.

Nevertheless, several recent reports suggest that even though data floats around in abundance, a bulk of data-driven projects fail. In 2017 alone, Gartner highlighted 60% of big data projects fail – so what leads it? Why the availability of data still can’t ensure success of these projects?

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Right data, do I have it?

It’s best to assume the data which you have is accurate. After all, organizations have been keeping data for years, and now it’s about time they start making sense out of it. The challenge that they come across is that this data might give crucial insights about past operations, but for present scenario, they might not be good enough.

To predict the future outcomes, you need fresh, real-time data. But do you know how to find it? This question leads us to the next sub-head.

Where to find relevant data?

Each and every company does have a database. In fact, many companies have built in data warehouses, which can be transformed into data lakes. With such vast data storehouses, finding data is no more a difficult task, or is it?

Gartner report shared, “Many of these companies have built these data lakes and stored a lot of data in them. But if you ask the companies how successful are you doing predictions on the data lake, you’re going to find lots and lots of struggle they’re having.”

Put simply, too many data storehouses may pose a challenge at times. The approach, ‘one destination for all data in the enterprise’ can be detrimental. Therefore, it’s necessary to look for data outside the data warehouses; third party sources can be helpful or even company’s partner network.

How to combine data together?

Siloed data can be calamitous. Unsurprisingly, data is available in all shapes and is derived from numerous sources – software applications, mobile phones, IoT sensors, social media platforms and lot more – compiling all the data sources and reconciling data to derive meaningful insights can thus be extremely difficult.

However, the problem isn’t about the lack of technology. A wide array of tools and software applications are available in the market that can speed up the process of data integration. The real challenge lies in understanding the crucial role of data integration. After all, funding an AI project is no big deal – but securing a budget to address the problem of data integration efficiently is a real challenge.

In a nutshell, however data sounds all promising, many organizations still don’t know how achieve full potential out of data analytics. They need to strengthen their data foundation, and make sure the data that is collected is accurate and pulled out from a relevant source.

A good data analyst course in Gurgaon can be of help! Several data analytics training institutes offer such in-demand skill training course, DexLab Analytics is one of them. For more information, visit their official site.

The blog has been sourced from ― dataconomy.com/2018/10/three-questions-you-need-to-answer-to-succeed-in-data-driven-projects

 

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Most Popular MS Excel Shortcuts That Would Save Time, Money and Effort

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Most Popular MS Excel Shortcuts That Would Save Time, Money and Effort

Marketers look up to MS Excel every day. The reason can be anything – from creating charts to data analysis to structure a report for the upcoming presentation.

But, of course, working on Excel can be too time-consuming. Don’t you think it would be great if you could labor less formatting and entering gibberish formulas in the desktop or laptop?! Wouldn’t it be helpful if there were some keyboard shortcuts that could make the task easier and faster?!

Woah! Glad you did ask!

Fortunately, we’ve shortlisted a list of most common and widely used keyboard shortcuts for Microsoft Excel. No wonder, you can do all the navigation manually, but these hacks will save a lot of time as well effort, leaving you to focus only on the important stuffs.

 

To view our whole compilation of MS Excel shortcuts, click here

 

Or, another more profound way to fill in excellent MS Excel skills and expertise is through an advanced Excel course in Gurgaon. Encompassing courses like this helps students arm up with advanced skills and take the world in their stride. So, when are you enrolling?

Till then, pore over the list below. These simple yet effective shortcuts does help chop off time, enhance productivity and maneuver swiftly through workbooks, rows, sheets and columns:

Shortcuts play a significant role in every marketer’s or analyst’s life. Since its launch in 1985, MS Excel shortcuts have been helping users across the globe – they help analyze data quickly and efficiently. Undoubtedly, Excel spreadsheet is one of the top notch computer applications that helps store, manage and analyze data in tabular form for easy evaluation and scrutiny.

To know more about Excel that too in depth, look for an advanced excel VBA institute in Gurgaon. DexLab Analytics can come to your rescue. In the heart of India, specifically in the capital city, Delhi, DexLab Analytics with a core team of industry professionals offers state of the art data analytics training. The institute excels in numerous in-demand skill training, including training on Big Data Hadoop, PySpark, Python, R Programming, Business Analytics, Business Intelligence, SQL, VBA Macros, Ms Dashboards, Data Science, Machine Learning, Deep Learning, Data Visualization using Tableau and Excel. Drop by the site for more details.

 

The blog has been sourced from ― blog.hubspot.com/marketing/excel-shortcuts

 

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How Machine Learning Technology is Enhancing Credit Risk Modeling

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How Machine Learning Technology is Enhancing Credit Risk Modeling

Risk is an intrinsic part of the money lending system. There’s always the chance that customers borrowing money from financial institutions fail to repay their loans. And to determine the exact probability of a customer paying off a loan or defaulting on it, banks and other lenders rely on credit risk modeling.

Next-Gen Credit Assessment Techniques

The credit situation has changed a lot from how it used to be ten years ago. And to keep up, lenders must also evolve by identifying and responding to issues in real-time.  Credit risk strategy has become more complex and multiple factors need to be weighed to arrive at the correct decision that’s both profitable for the enterprise and customer. Sophisticated models that contain more than one dimension, such as additional information about a customer’s finance and behavior patterns, are in demand. These models help get a 360 degree view of the customer’s financial condition.

Moreover, banks want to provide broader financial inclusion with the intention that more customers get credit scores and avail their financial services. But they need to keep a check on their risk levels too. Traditional credit assessment techniques having linear nature, for example logistic regression, are useful, but only till a point.

Neural Networks

Recent developments in neural networks have greatly improved credit risk modeling and seem to provide a solution to the above mentioned problem. One such breakthrough is the NeuroDecision Technology from Equifax that facilitates more inclusive models, so scores and consent can be given to a bigger and varied group of customers.

Machine Learning (ML) is a fast-moving field and neural networks are used within deep learning, which is an advanced form of ML. It has the potential to make more accurate predictions and go beyond the linear analysis methods of logistic regression.  This is a positive development for both the business and its customers.

Linear Vs. Inclusive

What happens in a logistic regression model is that all customers above a straight line (prime) get approved, whereas everyone falling below that line (subprime) gets rejected. Hence, customers who are working hard towards creating a good credit profile but fall just below prime get declined repeatedly. Despite this problem, traditional linear models are widely used because outcomes can be easily conveyed to customers, which helps to be in sync with consumer credit regulations that demand higher transparency.

On the other hand, neural networks lead to non-linear or curved arcs that include those customers who aren’t yet prime, but are evidently moving in the right direction. This increases the ‘approved customer’ base, which is beneficial for the business because customers are being served better and the enterprise is growing. This model is advantageous from the perspective of customers also as it allows more people to access mainstream financial services.  The only problem is explaining the outcome to customers as neural networks tend to be rather complex.

Data Science Machine Learning Certification

Concluding Note

Many companies are producing robust credit modeling tools employing deep learning techniques. And these game-changing developments highlight the fact that they are just the starting point of a series of interesting developments ahead.

You can be a part of this exciting and booming field too! Just enroll for credit risk modeling certification at DexLab Analytics. Detailed courses chalked out and taught by industry professionals ensure that you get the best credit risk training in Delhi.


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Top 5 Industry Use Cases of Predictive Analytics

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Top 5 Industry Use Cases of Predictive Analytics

Predictive analytics is an effective in-hand tool crafted for data scientists. Thanks to its quick computing and on-point forecasting abilities! Not only data scientists, but also insurance claim analysts, retail managers and healthcare professionals enjoy the perks of predictive analytics modeling – want to know how?

Below, we’ve enumerated a few real-life use cases, existing across industries, threaded with the power of data science and predictive analytics. Ask us, if you have any queries for your next data science project! Our data science courses in Delhi might be of some help.

Customer Retention

Losing customers is awful. For businesses. They have to gain new customers to make up for the loss in revenue. But, it can cost more, winning new customers is usually hailed more costly than retaining older ones.

Predictive analytics is the answer. It can prevent reduction in the customer base. How? By foretelling you the signs of customer dissatisfaction and identifying the customers that are most likely to leave. In this way, you would know how to keep your customers satisfied and content, and control revenue slip offs.

Customer Lifetime Value

Marketing a product is the crux of the matter. Identifying customers willing to spend a large part of their money, consistently for a long period of time is difficult to find. But once cracked, it helps companies optimize their marketing efforts and enhance their customer lifetime value.

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Quality Control

Quality Control is significant. Over time, shoddy quality control measures will affect customer satisfaction ratio, purchasing behavior, thus impacting revenue generation and market share.

Further, low quality control results in more customer support expenses, repairs and warranty challenges and less systematic manufacturing. Predictive analytics help provide insights on potential quality issues, before they turn into crucial company growth hindrances.  

Risk Modeling

Risk can originate from a plethora of source, and it can be any form. Predictive analytics can address critical aspects of risk – it collects a huge number of data points from many organizations and sort through them to determine the potential areas of concern.

What’s more, the trends in the data hint towards unfavorable circumstances that might impact businesses and bottom line in an adverse way. A concoction of these analytics and a sound risk management approach is what companies truly need to quantify the risk challenges and devise a perfect course of action that’s indeed the need of the hour.

Sentiment Analysis

It’s impossible to be everywhere, especially when being online. Similarly, it’s very difficult to oversee everything that’s said about your company.

Nevertheless, if you amalgamate web search and a few crawling tools with customer feedback and posts, you’d be able to develop analytics that’d present you an overview of the organization’s reputation along with its key market demographics and more. Recommendation system helps!

All hail Predictive Analytics! Now, maneuver beyond fuss-free reactive operations and let predictive analytics help you plan for a successful future, evaluating newer areas of business scopes and capabilities.

Interested in data science certification? Look up to the experts at DexLab Analytics.

The blog has been sourced from ― xmpro.com/10-predictive-analytics-use-cases-by-industry

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A Success Story: Evolution of India’s Startup Ecosystem in 2018

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A Success Story: Evolution of India’s Startup Ecosystem in 2018

India’s startup ecosystem is gaining accolades. Steering away from the conventional, India’s young generation is pursuing the virgin path of entrepreneurship by ditching lucrative job offers from MNCs and government undertakings – the entire industry is witnessing an explosion of cutting-edge startups addressing real problems, framing solutions and satisfying mass level.

Interestingly, 2018 has been the year of success for Indian startups or entrepreneurs venturing into the promising unknown. Why? In total, 8 Indian startups, namely Oyo, Zomato, Paytm Mall, Udaan, Swiggy, Freshworks, Policybazaar and Byju’s crossed the $1 billion net worth mark this year and joined the raft of most-revered 18 Indian unicorns.

Besides attracting investments from domestic venture capitalists, these startups are bathed in global investments – foreign investors pumped in vast amounts on our homegrown startups to capitalize their activities. Thanks to their generosity, India proudly ranks as the 3rd largest startup ecosystem in the world, next to the United Nations and United Kingdom with its 7, 700 tech startups.

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Nevertheless, our phenomenal startup ecosystem has some grey areas too, which are addressed below:

Startup Initiatives

No doubt, the Indian government is taking conscious efforts to support the startup culture in the country, and for that Prime Minister, Narendra Modi has initiated the Startup India Programme. It is a noble step towards ensuring continuous creation and smooth functioning of fresh startups in India with technology in tow.

Thanks to technology, startups growth seemed to be 50% more dynamic this year!

Fund Generation

As compared to struggling years of 2017 and before, 2018 has been the year of driving investments. India experienced a 108% growth in total funding process, a big jump from $2 billion to $4.2 billion. Though investments at later stages skyrocketed, a decline was witnessed in the early stages during funding companies.

“In terms of overall funding, it is a good story. However, we are seeing a continuous decline in seed stage funding of startup companies. If you fall at the seed stage, innovation is hit. It is the area, which needs protection,” shared NASSCOM president Debjani Ghosh, which remains a matter of concern.

Employment Opportunities

Of course, the new startups push job creation numbers. It enhances the employment opportunities. Of late, NASSCOM reported that the epic growth in startup ecosystem resulted in creation of more than 40000 new direct jobs, while indirect jobs soared manifold. Today, the total strength of Indian startup landscape stands at 1.7 Lakh.

In the wake of powerful female voices and gender-neutral campaigns, our domestic startup ecosystem witnessed how women employees called the shots. The numbers of women employees spiked to 14% from 10% and 11% in the last two years, consecutively.

Global Position

Globally, India ranks as the 3rd biggest startup ecosystem in the world, and Bengaluru is the kernel of tech revolution. A report mentioned India’s significance in recording the highest number of startup set ups after Silicon Valley and London across the globe.

Quite interestingly, 40% of startups are launched in Tier 2 and 3 cities, indicating a steady rise of startup culture outside prime cities like Mumbai, Bengaluru and Delhi NCR.

With technology and startup leading the show, it’s high time you expand your in-demand skills of machine learning and data analytics. How? Opt for a good Machine Learning Course in India. It’s a surefire way to learn the basics and hone already learnt skills. For more information on Machine Learning Using Python, drop by DexLab Analytics!

 
The blog has been sourced from ― www.entrepreneur.com/article/322409
 

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Apache Spark with Machine Learning: A Combination to Digital Success

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Apache Spark with Machine Learning: A Combination to Digital Success

Technology bigwigs, such as Facebook, eBay, Amazon and Yahoo are vouching for Apache Spark for its services. Why? Because, Apache Spark is reckoned to be the fastest engine for processing big data technology. Instead of a disk, Spark runs on RAM – thus is ideal for faster data processing. It offers rich API’s in Python, Scala, Java and R and is more efficient than Big Data Hadoop. The main purpose of Spark is to formulate a unified platform for big data applications so that it can easily be integrated with Hadoop ecosystem later.

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Apache Spark: The Purpose

A raft of processes in machine learning undergoes heavy computation. Tackling these processes through Apache Spark is the best way and of course the easiest too. In a competitive industry, a pressing need always exist for an engine capable enough to process data in real time, perform in-memory processing and execute in batch mode. Apache Spark provides all this and more! Real-time streaming, in-memory processing, interactive processing, batch processing, graph processing, all powered with a fast, simple and effective interface is the USP of Apache Spark.

Practical Applications:

Entertainment

Spark is largely used in the gaming industry with an aim to identify patterns real-time and react to them without losing time. Targeted advertising, player retention and auto-adjustment of complexity in the game are few deployed tasks.

E-commerce

Real-time transaction information can be used to improve recommendation system and set new trends and demands. Unstructured data sources are useful; they include feedback from customers. Machine Learning algorithms process millions of such interactions performed by the users within an e-commerce platform – through Apache Spark.

Finance and Security

Apache Spark is ideal for fraud and intrusion detection. Across the finance and security sector, Spark coupled with Machine Learning algorithms evaluates business spending and offers necessary tools to suggest banks how to control finances – helps in finding problems within the financial industry quick and in an effective way. For example, PayPal relies on ML techniques – deep learning and neural network technologies are used the most.

Healthcare

The healthcare industry uses Spark to analyze the patient’s information based on their past health record in order to predict future health complexities. It is also used to reduce the processing time of genomic data sequencing – bonus points!

Machine Learning and Apache Spark

Companies are reaping benefits by equating Apache Spark with ML algorithms. For example, Yahoo uses a combination of these two technologies to pick out new topics which the users would find interesting. Similarly, Netflix also uses Spark+ML for real-time streaming and suggesting better online recommendation to the users, based on their user history.

The Apache Spark library has a separate library dedicated to ML, known as MLib. It consists of algorithms for the functions of regression, collaborative filtering, regression, dimensionality reduction, clustering, etc.

Last Thoughts

No wonder, Apache Spark offers a very innovative, powerful API for ML applications. Widely used for predictive analytics, fraud detection and recommendation engines, Spark swear to make ML practically easier and smoother in operations.

Are you interested in Apache Spark Progamming training in Gurgaon? DexLab Analytics is the place to be! Their incredible Spark Core training and placement assistance is probably the best in town. So, what you waiting for?!

 
The blog has been sourced from ― www.analyticsindiamag.com/how-apache-spark-became-essential-for-machine-learning
 

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Digital Transformation Calls for Wider Security Transformation!

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Digital Transformation Calls for Wider Security Transformation!

Going Digital is the buzzword – conventional businesses are getting transformed, thanks to digital bandwagon! Each day, it’s developing some new ways to engage clients, associate with partners and strike better operational efficiencies. Today’s business houses are using digital power to enhance revenue and reduce cost, and we can’t agree more.

Digital business is generally the implementation of digital technologies to support business models through user behavior evolution and considerable regulation support. For an instance, let’s look at Uber:

  • New Technology – Transportation technology platform
  • Business Model – Driver-partners and riders model
  • User Behavior Norm – Acceptance of non-traditional transportation method
  • Regulation Support – Cities and countries modify regulation to strengthen models

Today, cyber security and technology risk-management are treasure keys to future business growth and prosperity – security industry has evolved a lot over the years in terms of risk mitigation measures. Digital transformation has made way for security transformation, and in this regard, below we’ve whittled down the elements used for security transformation:

Digital Technologies – Smart watches, smart cars, health bands, voice assistants and smart home devices are some of the latest digital technologies clogging the present industry. These devices are to be supported by robust application platforms using AI, Machine Learning and Big Data.

Business Models – Risk management techniques are perfect for determining information risks emanating from business processes. In digital businesses, dynamic processes are common and evolving. Traditional risk models can’t handle them.

Evolving User Behaviors – Consumers are king in the digital world. The users are empowered with tools to make their own choices. On the contrary, traditional security processes used to treat users as weak links.

Regulation Support – To manage risk, security and privacy, regulations around the globe are changing and control standards are being updated or modified. For effective adaptability with the relevant changes, compliance assurance and sustenance need to be modified.

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A Few Fundamental Design Principles for Control Framework for Security Transformation

Business Accelerator – Only security is not just good enough for smooth digital transformation. Security has to take the role of an accelerator since the fundamental premise of going digital is to be fast in the market and enhance customer satisfaction.

Example – Biometric Authentic – it improves user speed and experience.

Technology Changes and Agile Design – The stream of technology is evolving – AI, ML, Blockchain, Virtual Reality, Internet of Things, etc. – every domain of technology is undergoing a robust transformation. Therefore, security controls have to be adaptable and agile in design.

Customer-oriented – Known to all, customers are the most important element in digital business. In the new digitized world, users are the ones who decide. Two-decade ago rule, ‘deny all, permit some’ is now changed into ‘permit all, deny some’ rule – and we are truly excited!

Automate and Digitize – It’s time security goes digital – automation is the key.

In the near future, risk management through security transformation is going to be the utmost priority for all risk managers –if you are interested in Market Risk Analytics, drop by DexLab Analytics. They are the best in town for recognized and reputable Value at Risk Model online training. For more, check out their official website.

 

The blog has been sourced from ― www.forbes.com/sites/forbestechcouncil/2018/09/27/the-digital-transformation-demands-large-scale-security-transformation/#64df7fc41892

 

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