Decoding the True Potential of Visual Data with Image Recognition Services

Image recognition technology has transformed the way visual data is pooled and processed. It offers opportunities similar to the ones portrayed in science fiction movies that make the imagination run wild. Faster detection of objects in real-time with assured accuracy, impressive face recognition mechanics, and improved augmented reality—all are made possible with image recognition, powered by machine learning.

Putting it simply, image annotation for machine learning brings in unique capabilities for a wide range of businesses irrespective of the industry verticals they deal in. Startups to MNCs are leveraging image annotation services to decode the true value of image data. Take a look at some of the amazing use cases of image recognition as elucidated here:

1. Product Discoverability with Visual Search

One of the great applications of image recognition is visual search as it empowers the users to search for similar products via a reference image. Online retailers dealing in verticals such as fashion, home décor, furniture, etc. can implement image-based search features in their applications and software systems. This not only results in enhanced product discovery but allows them to deliver a seamless digital shopping experience. It offers product recommendations based on actual similarity, increases the conversion rate, and decreases shopping cart abandonment.

2. Face Recognition on Social Media

Though face recognition is a sensitive ground, yet it is integrated by platforms such as Facebook, Instagram, Snapchat, etc. to improve user’s experience. Objects and scenes in the photo uploaded are recognized way before the user enters the description. Computer vision can differentiate between facial expressions, natural landscapes, sports, and food, among others. Likewise, it is used to identify inappropriate or objectionable content. Besides, photo recognition is also embraced by other image-centric products including Apple’s photo app cluster and Google Photos. Users can organize their pictures in meaningful series. It is also helpful in translating the visual content for blind users, thus enabling companies to achieve enhanced accessibility standards.

Source Prolead brokers usa

5 Ways Tech Is Making Insurance More Efficient

The insurance industry is a late bloomer in adopting cutting-edge technologies.

However, the rapid growth in the new-age technologies, such as AI, ML, blockchain, big data, cloud computing, IoT, is driving a shift in the insurance industry. The insurers are making strategies to enable the digital transformation of their business.

Nearly 86% of insurers believe that innovation must happen at a rapid pace to retain a competitive edge, as per a recent Accenture report.

Global insurance funding has reached USD 7.12 billion in February 2021. Besides, the global insurance market revenue is likely to reach USD 10.14 billion by 2025.

The investment in new technologies is making the insurance industry more effective and far-reaching. 

Here are the five ways technology is reshaping the insurance industry:

1- More Accurate Underwriting

Cloud computing integrates various data resources, enabling insurance companies to implement intelligent operations in customer marketing, product development, risk pricing, underwriting, and claims.

For underwriting, AI applications help determine and record the authenticity of the information provided by customers. For example, documents, recordings, and images.

Thus, this helps to speed up operations and mitigate the risk of insurance fraud. Using these technologies helps insurance companies to conduct underwriting processes in real-time. It also helps to effectively reject certain high-risk applications and reduce the loss ratio.

For example, car insurance can be transformed by connected devices such as telematics. It helps transfer important data to assess customers’ risk profiles.

It allows insurers to obtain real-time data on their customers’ driving habits, such as abrupt turns or stops made, speed, or location. Thus, these details enable insurance firms to make more informed underwriting decisions and provide policies accordingly.

2- Better Customer Experience

Insurers better understand their consumer needs by activating and collecting the right data from IoT. It enables them to offer customized advice, coverage, and tailored pricing.

These technologies help customers compare products, such as car insurance rates, review, and find plans that match personal requirements.

For example, usage-based insurance policies incorporate customer data to charge customers as per their specific needs and behaviors. Thus, it makes the consumer in charge of their own fees.

Such clever and personalization data usage benefits customers as well as insurers.

 On the one hand, it improves user satisfaction by providing tailored products. On the other hand, it provides more accurate risk assessment and stable margins to companies.

Another benefit of adopting digital strategies by insurers is to enable customers to fill and submit claims digitally.

Around 61% of customers prefer to monitor their application status with digital tools. 

Besides, insurance companies are adopting API and RPA, such as chatbots, mobile technologies, and voice recognition algorithms. It improves customer interactions and boosts data-harvesting capabilities. 

3- Using Technology to Assess Damage Faster

An insurance policy is a hedge against a variety of issues. It may include a big car accident, loss of property, or a fire in a luxury house. In such cases, insurance companies first investigate the truthfulness of the claim and then credit the claim amount.

This is a time taking process as it involves reviewing the claim, investigating, making subsequent adjustments, and remitting payment or cancelling the claim.

Therefore, deploying AI and ML software can make the claims process simple, faster, and more effective. Machine learning algorithms can calculate damage using satellite images and drones, eliminating the human factor and significantly reducing time and cost.

As per a study by McKinsey, by 2030 AI will overtake all aspects of the insurance industry.

With the rise of intelligent machines, bio-sensors, and deep-learning algorithms in ordinary objects, the insurance sector is making a shift from pay for damage to prevent damage.

4- Identifying and Mitigating Fraud

Fraud is a great calamity for the insurance industry.

According to Coalition Against Insurance Fraud, US insurers lose at least USD 80 billion annually. 

However, with fraud detection software, companies can identify and mitigate fraudulent activities. 

Cloud technologies provide real-time information to insurance companies. This information supports the insurer to deal with duplicate claims, fake diagnoses, inflated claims, overpayments, or any internal employee scams. 

For example, a client tries to recover from the same property fire by forged documents with a changed date. In this case, the technology will compare the claim data with the database and identify the fraud. 

5- Improved Cybersecurity

Insurance companies have access to highly sensitive customer information, making them prone to cyber-attacks. 

Close to two-thirds of insurers across all regions who participated in a study by Deloitte are looking to increase spending on cybersecurity.

Insurers are considering implementing “zero trusts” principles. It means imposing verification requirements on anyone seeking access to data or systems.

The focus is to invest in endpoint protection technologies to exert greater control over end-user devices. 

Predictive analytics software is useful for detecting malware and suspicious network behavior. The ML models are built on a large sequence of user’s activities within a network. These activities are labeled as acceptable or normal to gain a sense of regular activity. 

Final Thoughts

The insurance industry has a strong demand for investment in technology and innovative processes. AI, IoT, Blockchain, API, wearables, and Telematics are emerging technology trends to boost operational efficiency and stay ahead of the competition.

These technologies allow insurance companies to offer personalized solutions for customers, prevent risk, and improve fraud detection. They also help companies to track customer behavior and open the path to new business models.

Source Prolead brokers usa

IoT in Telecommunications: Challenges, Opportunities, Benefits & The Future

The rise of IoT has offered telecom operators another chance to spring into action. If the telecom industry makes the most of IoT, availing the USP that edge computing and 5G offers them, they can have huge potential profits of more than $600 billion by 2022, as per Accenture.

Connectivity Decides the Telcos Success Ahead

They excellently use the IoT services backed up by amazing connectivity. The telecommunication industry needs to invest in its strong core – Connectivity – to gain the higher IoT value stack. However, that is to be figured out how exactly the industry is going to advantage from IoT utilization. 

Read on to understand how the telecom industry utilizes IoT to meet its business needs. Also, we’ll see what’s in the future for telecom operators when utilizing IoT efficiently. 

IoT in Telecommunications delivers higher safety at remote sites, better equipment monitoring, and more logical business analytics. 

Existing Challenges Faced when Implemented IoT in Telecommunications

The Internet of Things is at a tipping point – on one side the progressing technology has made manufacturing smart devices easier than ever before (imperative for the business community). On the other side, there are some unavoidable challenges that businesses have to face when implementing IoT in the telecom industry. 

The challenges faced by the industry are related to power supply, progressing architecture, IoT complexities, privacy, and enabling complex sensing circumstances. 

These challenges are still repairable with the help of different wireless technologies, including Wi-Fi, Radio Frequency Identification (RFID), Bluetooth, and Near-field Communication (NFC). Besides, the existing Wi-Fi networks need to be improved to gain a larger coverage. 

Moreover, it is essential to be knowledgeable about the confirmation of the communication pathway of the Internet of Things to further understand how information is exchanged within the IoT. The Internet of Things relies on different protocols and techniques to disperse information. Majorly, telcos need the support of Device-to-Server, Device-to-Device, and Server-to-Server communication systems to share the information within the Internet of Things. 

However, even after settling all these challenges, telcos face other major challenges, which are as below:

• Improved Performance, Availability, Higher Reliability, Complete Privacy, Absolute Scalability, Interoperability, Compatibility, Extensive Security, Investment, Mobility, and Big IoT Data.

1. AVAILABILITY 

Usually, IoT is utilized to facilitate information anywhere at any time. It depends on what the user requests. Availability, therefore, is a critical issue for the IoT, it requires the high availability guarantee of all the physician devices used. Even IoT apps need to be highly available. The solution to this issue is to maintain the programs and hardware devices that are not in use so that they can be used to balance the load when a failure occurs.  

Generally, these devices or hardware are redundant, even though it increases the complexity of the entire process. Hence, to achieve availability, the best solution is to use these redundant devices. 

The redundancy is of two types:

• Active (Doesn’t perform up to the mark)
• Passive (Activated when primary components fail; sleeps at other times)

IoT involves the usage of multiple technologies. Hence, its performance can’t be judged by just using a single device. IoT’s performance is even dependent on some other factors:

✅ Huge Amount of Data

✅ Extreme Reliance on Cloud

✅ Network Traffic 

Telcos need to figure out how to overcome these challenges of bringing IoT in Telecommunications so that they can deliver the required availability to the users. 

2. RELIABILITY 

Another important aspect of IoT in Telecommunications in the future and even now is to offer apt reliability to the users. And, it won’t come just by sending reliable information, but by adapting to the progressing environmental conditions. No matter what aspect of IoT you are dealing with (hardware or software), there should be guaranteed reliability. 

3. PRIVACY & SECURITY 

Another challenge for IoT in Telecommunications is to maintain the utmost security and privacy. The limited storage capacity of memory cards in IoT allows only small amounts of data to be stored. The remaining data is stored on other sites remotely. In the latter, users are not very comfortable with disclosing their information to others. Hence, telcos need to secure this data for maintaining the privacy of the users. 

Privacy, trusted communication, digital forging are rarely addressed issues in the IoT. Since security has become a major concern when it comes to users’ data, IoT’s non-reliance on common security standards and architecture poses a big challenge for telcos to maintain utmost secrecy for the same. 

4. INTEROPERABILITY 

In IoT, different devices are connected. Hence, interoperability is a necessity, irrespective of the device type. IoT in Telecommunications must deliver services equally to all the devices connected. This challenge can be overcome by adhering to standardized protocols. Ambiguous interpretations of the same protocol make it tough to achieve interoperability. However, if these ambiguities are avoided, interoperability can be achieved in IoT. 

5. BIG IoT DATA 

IoT, indeed, is one of the major sources of gathering huge data. The future of telecom involves connecting billions of devices. That eventually leads to creating extensive big data production. Managing this data (accessing, processing, and storing) needs highly scalable computing platforms, which does not have any impact on the performance of the app. And, that is a big challenge for telcos. 

6. COMPATIBILITY 

The IoT-based smart environment faces another challenge called Compatibility. Proper compatibility is to be maintained between different products that are constantly connected. Most of the devices misbehave and create compatibility concerns, as there is no availability of a universal language for these devices. Hence, different industries need to collaborate to deliver the utmost compatibility. Otherwise, the co compatibility issues will exist. 

Considering these challenges, there is a lot to be done with the implementation of IoT in telecom. However many of these concerns seem to have been sorted for the industry. 

Benefits IoT in Telecommunications 

Why IoT in Telecommunications? The Telecom industry is already leveraging the power of IoT, and the results can be experienced with excellent telecommunication services and products.

Here are some distinct ways IoT is leveraged by telcos:

1. IoT allows telecom to track and trace all data and information provided to consumers. The information is generally regarding products and services offered to the customers. Telcos rely on this information to further identify any issues or glitches in the network and all the hindrances that are not allowing them to deliver the best communication services. 
2. IoT in Telecommunications helps telcos conduct a performance evaluation of products. Once the products are deployed and data is collected using pre-integrated sensors, the performance of the telecommunication is checked. 
3. Telecommunication industries can combat the challenge of maintaining security by installing IoT-powered beacons and cameras to deploy security.
4. Better interfaces can be enjoyed by customers if  IoT-integrated telecom services help in creating a stronger connection with customers’ apps used on tabs and phones. 
5. 5G in mobiles creates a smooth communication between autonomous vehicles via IoT-powered sensor fusions. IoT in Telecommunications is a must for a smoother communication system. 
6. For better machine-to-machine communication, IoT blockchain systems can be highly utilized.
7. IoT sensors allow telcos to monitor the device performance installed at various places, including factories, workstations, and warehouses.
8. IoT in Telecommunications helps companies to build improved predictive analytics models, which assists in generating analytics that achieve expected results.
9. With IoT in Telecommunications, companies can deliver improved location-based services by using the proximity sensors in the devices.  

IoT brings excellent benefits to telecom operators. But, as the IoT is still in its evolving stage, more issues need to be addressed and sorted out. Seamless communication is built when 

IoT in Telecommunications – The Accurate Utilization 

The telecom operators deliver a collection of services/ products using IoT to bring additional value to their already existing networks. 5G is maturing and the 4G to 5G transformation is still in progress. Amidst this progression, the added value to IoT becomes more valuable for Telecom companies. IoT in Telecommunications builds platforms for organizations to develop their own IoT services and products. 

The First Phase: Enhancing Connectivity 

Currently, telcos are in the best position to transform themselves for the better. Thanks to IoT in Telecommunications that offers reliable and safe connectivity nationwide. The future is about these telcos leaders customizing their technology assets to deliver connectivity platforms backed up by IoT.  Telcos will need highly reliable computing power to uncover the actual capabilities of modern IoT solutions.

The Second Phase: Building an Effective Ecosystem 

With IoT in Telecommunications, the future is about a better ecosystem – as the current telecommunication business model will vanish in the coming five years, ecosystem being the main factor why this change will be needed. However, telecom is amongst those industries that will still drive profits in the future through the ecosystem amalgamated with other valuable factors, including talent, technology, and business vision. 

Again, the focus will be on ‘connectivity’ even while building a better ecosystem for telcos. This ecosystem of partners will be to capture more lucrative opportunities. In short, to be a successful business in the future, telcos will have to first establish themselves as a genuine IoT provider and then partner up with other industry verticals to take advantage of their expertise. 

Once mastering the single verticals, should telcos proceed to co-creating complicated use cases that involve multiple verticals? The innovation and new value streams will be witnessed when these multiple verticals are created. 

Eventually, to become a reliable industry partner that manages multiple verticals, it is required to become a master of their innate strength, i.e. ‘the connectivity.’

The Ultimate Phase: Ruling IoT Readiness 

Becoming the ultimate master of IoT agility is the endmost path. To figure out the exponential value, the telecom industry will have to shift from mastering single verticals to co-partnering with multiple industries. 

• Magnify Core Connectivity – To Win an IoT that is built on Trust

The key to bringing agility in work and mindset has to be developed. Stepping into a zone with faster-than-ever innovation cycles will need new and innovative methods of working. So, how do you fix those challenges and skill gaps to achieve steadiness in the business? Well, you will need to invest in modern technologies and quicker connectivity solutions to win the game. Besides, transfer your core expertise into an IoT business. 

• Build Trust, Serve Single Verticals First 

Focus on gaining the trust of one industry at a time. Combine platform players and device manufacturers to build trust with your partners. Allow your partners to deliver enterprise-grade solutions while you deliver a secure and reliable IoT core network. That’s how you build trust amongst partners. 

• Move to Multiple Verticals

The ultimate step is beyond connectivity. When you have mastered connectivity, become a matchmaker for industry players who want to monetize their data. It could even be a platform where developers and businesses connect. 

The Opportunities Companies can Leverage with IoT in Telecommunications

Telecoms can magnify the service area with multiple IoT-enabled services, like smart retail, smart homes, vehicle tracking, and much more. Telecommunication companies can build IoT platforms for their respective customers, allowing them to connect centralized control and devices to run essential IoT apps. Hence, with IoT in Telecommunications, these IoT-powered platforms need to be built using a modular architecture that features authentic APIs. 

What will this modular architecture featuring reliable APIs deliver?

• Backend Solutions

The backend offered by the telecommunication companies, delivers the ability to store, manage, and process IoT- generated data. Using the back-end solutions, customers can easily integrate them into their already existing apps. 

• Managed Connectivity Services 

Telcos support the IoT infrastructure of their customers by offering managed connectivity services that heavily rely on Narrowband IoT. The data generated by IoT devices is saved and managed on the customer’s side.  

• Data Analytics Services 

To deliver customers the value from the data generated by IoT devices, telcos offer predictive, diagnostics, and prescriptive analytics services to their valued customers. 

• IoT Data Storage Services

Business apps run on the customer side, hence, telcos take care of IoT-generated data by saving, storing, scanning, cleaning, and processing it for their customers. 

The IoT in the Telecommunications industry helps their customers get complete access to SaaS tools for solving their business problems – and the solutions delivered are quite problem-focused in a particular space. 

The Future of IoT in Telecommunications is Green

As new and innovative concepts are amalgamating with existing technologies, IoT still has the chance to grow. It is still evolving. The Green IoT has the potential to change the future environment, which will be more green, healthy, and economical. We can expect green communication and networking, green IoT services, and green design and implementation in the future. 

Although telecom is facing certain challenges at the moment, with advancing IoT, it seems to be vanishing soon, allowing telecom to focus clearly on maintaining and building better ‘connectivity’ to impress the end-users. 

Telecom, be ready for;

1. IoT-Based Smart Environments

• Smart buildings, smart cities, smart health, smart agriculture, smart homes, smart industries, and smart transport. 

The usage of IoT with a smart environment gives amazing opportunities to telecom:

• Real-time information 
• New and innovative business models
• Smart Operations
• Flexible, secure, cost-effective cloud-based apps

2. Green IoT

The increasing awareness of all environmental concerns nationwide has led to the introduction of Green IoT. It included using technologies that help in building a healthy IoT environment. The aim is to help users collect, store, and access information using the storage and facilities available.

Conclusion:

IoT in Telecommunications is a boon for industries operating in this sector. However, it is high time to beat the existing and ongoing telecoms challenges and concerns. IoT in telecommunication can help expand and reach out for more innovative solutions to gain that competitive advantage in the market. 

Source Prolead brokers usa

The Data Science Project Life Cycle Explained

As Covid-19 continues to shape the global economy, analytics and business intelligence (BI) projects can help organisations prepare and implement strategies to navigate the crisis. According to the Covid-19 Impact Survey by Dresner Advisory Services, most respondents believe that data-driven decision-making is crucial to survive and thrive during the pandemic and beyond. This article provides a step-by-step overview of the typical data science project life cycle, including some best practices and expert advice.

Results of a survey by O’Reilly show that enterprises stabilise their adoption patterns for artificial intelligence (AI) across a wide variety of functional areas.

Source: AI adoption in the enterprise 2020

The same survey shows that 53% of enterprises using AI today recognise unexpected outcomes and predictions as the greatest risk when building and deploying machine learning (ML) models.

Being an executive person driving and overseeing data science adoption in your organisation, what can you do to achieve a reliable outcome of your data modelling project while getting the best ROI and mitigating security risks at the same time?

The answer lies in thorough project planning and expert execution at every stage of the data science project life cycle. Whether you use your in-house resources or outsource your project to an external team of data scientists, you should:

• Define a business need or a problem that can be solved by data modelling
• Have an understanding of the scope of work that lies ahead

Here’s our rundown of a data science project life cycle, including the six main steps of the cross-industry standard process for data mining (CRISP-DM) and additional steps from data science solutions that are essential parts of every data science project. This roadmap is based on decades of experience in delivering data modelling and analysis solutions for a range of business domains, including e-commerce, retail, fashion and finance. It will help you avoid critical mistakes from the start and ensure smooth rollout and model deployment down the line.

A typical data science project life cycle step by step

1. Ideation and initial planning

Without a valid idea and a comprehensive plan in place, it is difficult to align your model with your business needs and project goals to judge all of its strengths, its scope and the challenges involved. First, you need to understand what business problems and requirements you have and how they can be addressed with a data science solution.

At this stage, we often recommend that businesses run a feasibility study – exhaustive research that allows you to define your goals for a solution and then build the team best equipped to deliver it. There are usually several other software development life cycle (SDLC) steps that will run in parallel with data modelling, including solution design, software development, testing, DevOps activities and more. The planning stage is to ensure you have all required roles and skills in your team to make the project run smoothly through all of its stages, meet its purpose and achieve its desired progress within the given time limit.

2. Side SDLC activities: design, software development and testing

As you kick off your data analysis and modelling project, several other activities usually run in parallel as parts of the SDLC. These include product design, software development, quality assurance activities and more. Here, team collaboration and alignment are key to project success.

For your model to be deployed as a ready-to-use solution, you need to make sure that your team is aligned through all the software development stages. It’s essential for your data scientists to work closely with other development team members, especially with product designers and DevOps, to ensure your solution has an easy-to-use interface and that all of the features and functionality your data model provides are integrated there in the way that’s most convenient to the user. Your DevOps engineers will also play an important role in deciding how the model will be integrated within your real production environment, as it can be deployed as a microservice, which facilitates scaling, versioning and security.

When the product is subject to quality assurance activities, the model gets tested within the team’s staging environment and by the customer.

3. Business understanding: Identifying your problems and business needs, strategy and roadmap creation

The importance of understanding your business needs, and the availability and nature of data, can’t be underestimated. Every data science project should be ‘business first’, hence defining business problems and objectives from the outset.

And in the initial phase of a data science project, companies should also set the key performance indicators and criteria that will be indicative of project success. After defining your business objectives, you should assess the data you have at your disposal and what industry/market data is available and how usable it is.

1. Situational analysis. Experienced data scientists should be able to assess your current operational performance, then define any challenges, bottlenecks, priorities and opportunities.
2. Defining your ultimate goals. Undertake a rigorous analysis of how your business goals match the modelling approach and understand where the gaps in performance and technology are to define the next steps.
3. Building your data modelling strategy. When defining your strategy, two aspects are essential – your assets available and how well the potential strategy answers your business goals – before building business cases to kick start the process.
4. Creating a roadmap. After you have a strategy in place, you need to design a roadmap that encompasses programs that will help you reach your goals, what the key objectives are within each program and all necessary project milestones.

The most important task within the business understanding stage is to define whether the problem can be solved by the available or state-of-the-art modelling and analysis approaches. The second most important task is to understand the domain, which allows data scientists to define new model features, initiate model transformations and come up with improvement recommendations.

4. Data understanding: data acquisition and exploratory data analysis

The preceding stages were intended to help you define your criteria for data science project success. Having those available, your data science team will be able to prepare your data for analysis and recommend which data to use and how.

The better the data you use, the better your model is. So, an initial analysis of data should provide some guiding insights that will help set the tone for modelling and further analysis. Based on your business needs, your data scientists should understand how much data you need to build and train the model.

How can you tell good data from bad data? Data quality is imperative, but how are you to know if your information really isn’t up to the required standard? Here are some of the ‘red flags to watch out for:

• It has missing variables and cannot be normalised to a unique basis.
• The data has been collected from lots of very different sources. Information from third parties may come under this banner.
• The data is not relevant to the subject of the algorithm. It might be useful, but not in this instance.
• The data contains contradicting values. This could see the same values for opposing classes or a very broad variation inside one class.
• Upon meeting any one of these red flags, there’s a chance that your data will need to be cleaned prior to your implementation of an ML algorithm.

Types of data that can be analysed include financial statements, customer and market demand data, supply chain and manufacturing data, text corpora video and audio, image datasets, as well as time series, logs and signals.

Some types of data are a lot more costly and time-consuming to collect and label properly than others; the process can take even longer than the modelling itself. So, you need to understand how much cost is involved, how much effort is needed and what outcome you can expect, as well as your potential ROI before you make a hefty investment in the project.

5. Data preparation and preprocessing

Once you’ve established your goals, gained a clear understanding of the data needed and acquired the data, you can move on to data preprocessing. The best method for this depends on the nature of the data you have: there are, for example, different time and cost ramifications for text and image data.

It’s a pivotal stage, and your data scientists need to tread carefully when they’re assessing data quality. If there are data values missing and your data scientists use a statistical approach to fill in the gaps, it could ultimately compromise the quality of your modelling results. Your data scientists should be able to evaluate data completeness and accuracy, spot noisy data and ask the right questions to fill any gaps, but it’s essential to engage domain experts, for consultancy.

Data acquisition is usually done through an Extract, Transform and Load (ETL) pipeline.

The ETL (Extract, Transform and Load) pipeline

ETL is a process of data integration that includes three steps that combine information from various sources. The ETL approach is usually applied to create a data warehouse. The information is extracted from a source, transformed into a specific format for further analysis and loaded into a data warehouse.

The main purpose of data preprocessing is to transform information from images, audio, log, and other sources into numerical, normalised, and scaled values. Another aim of data preparation is to cleanse the information. It’s possible that your data is usable; it just serves no outlined purpose. In such a case,70%-80% of total modelling time may be assigned to data cleansing or replacing data samples that are missing or contradictory.

In many situations, you may need additional feature extraction from your data (like calculating the square from the room width and length for the rent price estimation).

Proper preparation from kick-off will ensure that your data science project gets off on the right foot, with the right goals in mind. An initial data assessment can outline how to prepare your data for further modelling.

6. Modelling

We advise that you start from proof of concept (PoC) development, where you can validate initial ideas before your team starts pre-testing on your real-world data. After you’ve validated your ideas with a PoC, you can safely proceed to production model creation.

Define the modelling technique

Even though you may have chosen a tool at the business understanding stage, the modelling stage begins with choosing the specific modelling technique you’ll use. At this stage, you generate a number of models that are set up, built and can be trained. ML models — linear regression, KNN, Ensembles, Random Forest, etc. — and deep learning models – RNN, LSTN and GANs – are part of this step.

Come up with a test design

Before model creation, the testing method or system should be developed to review the quality and validity. Let’s take classification as a data mining task. Error rates can be used as quality measures; thus, you can separate datasets in train, validation sets. And build the model using a train set and make a quality assessment based on the separate test set (a validation set is used for the model/approach selection, not for the final error/accuracy measurement).

Build a model

To develop one or more models, use the modelling tool on the arranged dataset.

1. Parameter settings — modelling tools usually allow the adjustment of a wide range of parameters. Make a parameters rundown with their chosen values together with the parameter settings choice justification.
2. Models — models suggested by the modelling tool and not the models’ report.
3. Model descriptions — outline the resulting models, report the models’ interpretations and detail any issues with meanings.

7. Model evaluation

Model selection during the prototyping phase

To assess the model, leverage your domain knowledge, criteria of data mining success and desired test design. After evaluating the success of the modelling application, work together with business analysts and domain experts to review the data mining results in the business context.

Include business objectives and business success criteria at this point. Usually, data mining projects implement a technique several times, and data mining results are obtained by many different methods.

• Model assessment – sum up task results, evaluate the accuracy of generated models and rank them in relation to each other.
• Revised parameter settings – building upon the evaluation of the model, adjust parameter settings for the next run. Keep modifying parameters until you find the best model(s). Make sure to document modifications and assessments.

Here are some methods used by data scientists to check a model’s accuracy:

• Lift and gain charts — used for problems in campaign targeting to determine target customers for the campaign. They also estimate the response level you can get from a new target base.
• ROC curve — performance measurement between the false positive rate and true positive rate.
• Gini coefficient — measures the inequality among values of a variable.
• Cross-validation — dividing data into two or three parts; the first is used for model training and the second for the approach selection, and then the third, the test set, is used for the final model performance measurement.
• Confusion matrix — a table that compares each class’s number of predictions to its number of instances. It can help to define the model’s accuracy, true positive, false positive, sensitivity and specificity.

The confusion matrix

• Root mean squared error — the average amount of error made. Most used in regression techniques; help to estimate the average amount of wrong predictions.

The assessment method should fit your business objectives. When you turn back to preprocessing to check your approach, you can use different preprocessing techniques, extract some other features and then turn back to the modelling stage. You can also do factor analysis to check how your model reacts to different samples.

8. Deployment: Real-world integration and model monitoring

When the model has passed the validation stage, and you and your stakeholders are 100% happy with the results, only then you can move on to full-scale development – integrating the model within your real production environment. The role of engineers like DevOps, MLOps and DB is very important at this stage.

The model consists of a set of scripts that process data from databases, data lakes and file systems (CSV, XLS, URLs), using APIs, ports, sockets or other sources. You’ll need some technical expertise to find your way around the models.

Alternatively, you could have a custom user interface built, or have the model integrated with your existing systems for convenience and ease of use. This is easily done via microservices and other methods of integration. Once validation and deployment are complete, your data science team and business leaders need to step back and assess the project’s overall success.

9. Data model monitoring and maintenance

A data science project doesn’t end with the deployment stage; the maintenance step comes next. Data changes from day to day, so a monitoring system is needed to track the model’s performance over time.

Once the model’s performance falls down, monitoring systems can indicate whether a failure needs to be handled, or whether a model should be retrained, or even whether a new model should be implemented. The main purpose of maintenance is to ensure a system’s full functionality and optimal performance until the end of its working life.

10. Data model disposition

Data disposition is the last stage in the data science project life cycle, consisting of either data or model reuse/repurpose or data/model destruction. Once the data gets reused or repurposed, your data science project life cycle becomes circular. Data reuse means using the same information several times for the same purpose, while data repurpose means using the same data to serve more than one purpose.

Data or model destruction, on the other hand, means complete information removal. To erase the information, among other things, you can overwrite it or physically destroy the carrier. Data destruction is critical to protect privacy, and failure to delete information may lead to breaches, compliance problems among other issues.

Conclusion

AI will keep shaping the establishment of new business, financial and operating models in 2021 and beyond. The investments of world-leading companies will affect the global economy and its workforce and are likely to define new winners and losers.

The lack of AI-specific skills remains a primary obstacle on the way to adoption in the majority of organisations. In the survey by O’Reilly, around 58% of respondents typically mentioned the shortage of ML modellers and data scientists, among other skill gaps within their organisations.

Source: AI adoption in the enterprise 2020

Having questions on how your data can be used to help you boost your business performance? We will be happy to answer them. Drop us a line.

Originally published at ELEKS Labs blog. 

Source Prolead brokers usa

Best JavaScript Frameworks for 2021

According to Stackoverflow’s 2021 Developer Survey, JavaScript is the most used language for the eighth consecutive year, with 67.7% of people choosing it. The main reason for this popularity is that JavaScript is versatile and can use for front-end and back-end development and testing websites or web applications.

If you google “JavaScript framework,” you will find many JavaScript frameworks, each with its advantages and uses. Because there are so many choices of JavaScript frameworks for front-end, back-end development, or even testing, it can be challenging to choose the right one to suit your needs.

It can be tough to find the proper framework for your needs. In this article on the best JavaScript frameworks for 2021, I’ve used StateOfJS 2019, Stackoverflow’s Developer Survey 2021, and NPM trends to compile a list of the best JavaScript frameworks for front-end, back-end, and testing that can help you with this.

Front-end JavaScript frameworks

JavaScript has been widely using for the front-end development for almost two decades. Famous structures such as React, Vue, and Angular have gained a vast legion of followers, while recently, some new competitors have found success in challenging the big 3. Here are the six best front-end frameworks in 2021 -.

1|  React.js

The first place in our ranking of the best JavaScript frameworks for 2021 in the front-end category is React.js. React.js is an open-source front-end JavaScript library (not a full-fledged framework) created in 2011 by a team of Facebook developers led by Jordan Walke and became open-source in June 2013. The prototype was called “FaxJS” and was the first test in the Facebook News Feed. React can be regarded as one of the biggest disruptors in the web development industry, and it was a real breakthrough in shaping the web applications we see today.

React introduced a component-driven, functional and declarative programming style for creating interactive user interfaces for mainly single-page web applications. React offers ultra-fast rendering using a “virtual DOM” that renders only the parts that have changed, rather than causing the entire page. Another essential feature of React is the use of a simpler JSX syntax instead of JavaScript.

Although learning React is a bit more complicated than the other best front-end JavaScript frameworks on this list, React supports by a vast developer community, rich learning resources, and massive adoption in every corner of the world.

React consistently tops the popularity charts for front-end JavaScript frameworks, whether it’s the Stack Overflow Developer Survey or the State OF JS Survey. React has always won the crown as the favorite front-end JavaScript framework. The world’s biggest companies and brands like Airbnb, Facebook, Instagram, Netflix, Twitter, WhatsApp, and many more built with React. It would not be wrong to assume that React.js is arguably the best JavaScript framework.

2 | Vue.js

Vue.js is a lightweight, open-source JavaScript framework used to build creative user interfaces and high-performance single-page web applications with minimal effort.

Vue was first announced in 2014 by Evan Yu, a Google developer who took inspiration from Angular to provide a simple, lightweight, and efficient alternative in the form of Vue.js. Vue takes much of its functionality from React and Angular but has significantly improved those features to provide a better, easier to use, and more secure framework. The best example of this approach is Vue’s provision of bi-directional data binding, as seen in Angular, and ‘Virtual DOM,’ as seen in React.

Similarly, Vue is very flexible, allowing it to function as a complete end-to-end framework, like Angular, as well as a stateful view layer, like React. Thus Vue’s main advantage is its progressive nature, which is simpler, more accessible, and less restrictive to adapt to developers’ needs. In the last two years, Vue has exploded in popularity, displacing the angular and complex dominance of React as the best JavaScript framework. Some of the world’s biggest companies, such as Adobe, Apple, BMW, Louis Vuitton, and Nintendo, have adopted Vue.

3 | Angular

In third place in the Best JavaScript Frameworks of 2021 category is Angular.js, Google’s open-source, script-based framework used to create the client-side of a single-page web application. Angular was created in 2010 by Google engineers Misko Hevery and Adam Abrons as AngularJS (or Angular 1). AngularJS was widely known and at the height of its popularity, but the advent of React exposed its serious flaws, and it has been relegated to oblivion. As a result, AngularJS was rewritten entirely from scratch and Angular 2 (or simply Angular) in 2016.

AngularJS (Angular 1) took inspiration from React. They made significant changes, the most important of which was moving from an M-V-W (Model-View-Whatever) architecture to a component-oriented architecture like React to a component-based architecture like React. Today Angular is an example of the most secure JavaScript frameworks for building enterprise applications; over a million websites use Angular, including Google, Forbes, IBM, and Microsoft.

4|  Emberjs

Fourth place in the front-end category of the Best JavaScript Frameworks 2021 list goes to Ember.js. It is an open-source JavaScript framework. Unlike the other frameworks we have reviewed, Ember uses the Model-View-ViewModel (MVVW) architectural.

Ember was originally a SproutCore 2.0 framework that was renamed Ember.js by Yehuda Katz, a veteran developer who is considered one of the leading creators of jQuery. Ember’s most popular and essential features are the Ember command-line interface, which is itself very powerful. Ember’s most popular and essential features are the Ember command-line interface, a powerful productivity tool.

Ember is one of the older JavaScript frameworks compared to React, Vue, and Svelte, but it still has a large user base at big companies like Microsoft, LinkedIn, Netflix, and Twitch. It has many users in its customer base. Old friends like Backbone and Polymer have disappeared, but Amber has somehow managed to hang on to Fort through a passionate community.

5| Preact.js

At number five in the front-end category of our list of the best JavaScript frameworks for 2021 is Preact.js. Preact.js is a lightweight, fast and powerful alternative to React (it’s not a complete framework). Jason Miller created preact, Senior Developer Programs Engineer at Google, and used several developers as a subset of React. Jason Miller made preact, Senior Developer Programs Engineer at Google, and is used by several developers as a subset of React, with some features stripped out.

Preact.js  base on the same basic principles as React, a component-based approach using Virtual Dom while fully compatible with React

You can also use React packages without compromising on speed, performance, and lean size. Unless you need the full potential of React, most developers will use Preact during development and even switch to Preact in production. There are many large companies use Preact, including Tencent, Uber, and Lyft. 

Conclusion

These are not even remotely all of the frameworks available for Javascript front-end development, but they do make up the bulk of such frameworks currently in use. As Javascript capabilities continue to evolve (through the ECMAscript process), so too will the likely migration of framework capabilities into the core.

Source Prolead brokers usa

Optimize your Website with these Five everyday SEO Tasks

Time is money. What if you lack both? 

Small business owners deal with a lot of work and don’t have the resources to hire someone to do these tasks. Secondly, they are usually on a very tight budget. Therefore, hiring someone only for SEO purposes or spending much time on SEO is practically impossible. Yet it is naive to ignore SEO. No business can be successful without SEO. So how can you deal with the situation effectively? The answer is simple.
You have to take baby steps but on a regular basis. Here we will discuss some small yet highly effective things you can do to keep the SEO of your business up to the mark.

Long-Term SEO Strategy

Before we dig deep into our topic, let’s shed some light on the importance of a long-term SEO strategy. Although it sounds good to have a plan based on daily tasks, nothing can replace the value and impact of a well-placed long-term plan. Whether you use the services of a digital marketing agency or create a plan on your own. What matters is that you have a well-thought-out plan for your business. Through a long-term SEO strategy, you set goals, define keywords, optimize content, and build links. This helps you keep track of your website and measure the extent of success you are achieving.
Now let’s deep dive into the daily tasks that can improve the SEO of your website.

Keep Your Website Content Fresh

Content is king. We all have heard this time and again. Yet we cannot deny the value and impact of content on the performance of your website. Content is important to rank, will simply be an understatement. That’s why you must regularly add new content to your website or keep the existing content updated. Writing and publishing new content on a regular basis should be part of your strategy. It is not necessary to publish new content every day. You can do it according to your goals and objectives. The important point is to have a posting schedule that suits your business. It can be daily, weekly, or monthly. For example, when you publish new content every Friday, your audience will be looking forward to it each Friday. 

Another aspect is to update comments and reviews. When you reply to comments the content of your website stays fresh. Moreover, if you don’t reply to comments, your visitors will feel ignored. This will impact negatively your website.

Create a Proper Internal Linking structure

No one likes to get confused or get sidetracked. A good internal linking structure plays the role of the red carpet for Google and your visitors. At the start, internal linking may not seem important but as your website grows it is imperative to have a well-defined internal linking structure. 

One of the most important factors to look out for is orphaned content. Orphaned content is the content that doesn’t get any links from other posts or pages from the same website. These links can really impact the rankings of your website. Having pages or content that don’t link to any other useful information can actually affect you negatively rather than having a positive effect on your website.

Improve your Technical SEO

We understand that technical SEO requires expertise and time. You need a professional SEO for this purpose. Having said that, there are few technical tasks that need to be handled on an everyday basis and don’t require an expert.

The first one is to optimize your images. Having high-quality images on your website is a necessity, but you don’t want these images to hinder the performance of your website. The goal is to keep the size of your images as small as possible without compromising on quality. 

Another task is to look for duplicate content. Google doesn’t appreciate duplicate content of any kind. It is possible that there is some duplicate content on your website that you may be unaware of. You don’t need to look for duplicate content on daily basis, but keep it on our list of tasks.

Keep Track of your Site Maintenance

Anything working needs proper maintenance. The same goes for your website. If not daily, you must analyze your website on a weekly basis. Most of us will agree that it is better to clean daily rather than waiting for the house to become a total mess. The same principle applies to your website. Managing a bunch of pages will be much harder than managing a few ones. Some of the tasks that you need to carry out regularly are:

• Make sure that all the pages are loading without any errors. If not then look for reasons behind and take steps to eradicate them.
• Look out for the cannibalization of your content. When you have too many pages and upload content on regular basis it is possible that you have content that will compete against each other. 
• Find pages that are irrelevant or unnecessary. Analyze all the pages and determine if there are any pages that are not useful anymore.
• Check the speed of your website and make sure that it is above the optimum range. Compress images, reduce redirects, and reduce response time.

Stay Active on Social Media

No one can deny the importance of social media platforms today. A decade ago it may seem like a waste of time and money but not anymore. When you have a website it is almost mandatory that you have some presence on social media. Through social media, you can stay engaged with your audience. Being on social media doesn’t require much time or money. It is easy, fast, and effective. You can post blog posts, share images, news about events, and so on. This also allows you to interact with your audience at a personal level. You can deduce what they prefer, what they want, and what are the issues they face. You can leave comments on their profiles or reply to their questions. You can choose the platform that suits you the best. You have a lot of options like Facebook, Instagram, Snapchat, Twitter, LinkedIn, and Reddit. Whatever platform you choose, the important part is to utilize it to your advantage and use this information to improve the SEO of your website.

Source Prolead brokers usa

DSC Weekly Digest 19 April 2021

Becoming A Data Society Data has always been an integral part of computing, but it has only been in the last decade or so that we have reached a point of data ubiquity. What that means in practice comes down to a fundamental notion about what exactly data is. Data isn’t really a “thing” per se. Rather, you can think of data as being the digitized artifact that a process produces as it changes state. It is not just a signal, but a signal with some attached semantics that describes what a thing has become. Human civilization has produced such signals for a long, long time, but in most cases, only the crudest of these signals were interpretable, usually at a significant cost. Increasingly, however, everything around us has begun emitting state signals that can be encoded and transmitted clear across the planet. If the temperature in my house falls below 65 degrees, the thermostat not only acts by turning up the heat, but it also informs me that the house is too cold. Amazon informs me when a particular book I’ve been wanting to read comes out, but it also now tells me when the cost of that book falls below a certain threshold … and can even purchase the book on my behalf without me even being in the loop. This is having a profound impact upon our society in ways we’re only just beginning to realize. One effect is that it provides a natural deflationary pressure on financial transactions even in the midst of the supply chain disruptions from the pandemic that would ordinarily be strongly inflationary. It is even mitigating those disruptions by providing a better idea about where alternatives can be found in real-time, a process that normally would be prohibitively complex. We’re beginning to discover that data societies are mediated societies, where the mediation is less through human interaction and more through algorithms and gradient modeling (machine learning). The next decade will see refinements of this mediation, as the theoretical work being done today becomes embedded in self-modifying code that is able to reduce the friction of human interactions. This is what data scientists do, ultimately, and while there are advantages to this, there are also deep ethical and technical questions that need to be worked out as we draw the line about how much automated mediation is too much. This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society. Data Science Central is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think. In media res, Kurt Cagle Community Editor, Data Science Central Announcements Build statistical and analytical expertise as well as the management and leadership skills necessary to implement high-level, data-driven decisions in Northwestern’s Online MS in Data Science. Earn your degree entirely online in classes that are led by industry experts who are redefining how data is used to boost efficiency and effectiveness in a wide range of fields. Learn more DSC Featured Articles Unusual Opportunities for AI, Machine Learning, and Data Scientists How the Python Interpreter Works What Does The Future Hold For the Companies Developing Mobile Apps Cloud Computing In Healthcare: Main Problems Companies Encounter What is Unsupervised learning MLOps: Comprehensive Beginner’s Guide Data Mesh: The 4 Principles of the Distributed Architecture Top Python Operator Why many financial services professionals use alternative data every day (and the rest wish they could) The Megatrends Driving Demand for Datacenter Compute Five Steps to Building Stunning Visualizations Using Python 6 Ways AI is Changing The Learning And Development Landscape Limitations Of Power Bi How to Leverage Data Science For Customer Management The Future of Medical Software Development. Model Training: Our Favorite Tools in the Shed What is data curation and how it helps data management? Types of Saas Solutions: Categories and Examples The Pros and Cons of RDF-Star and Sparql-Star DSC Weekly Digest 12 April 2021 TechTarget Articles How social media can help you find jobs that aren’t advertised Insightsoftware acquisition of Izenda targets embedded BI Top 20 cloud computing skills to boost your career in 2021 Will codeless test automation work for you? Reap the rewards of IT/OT convergence in manufacturing New IoT Cybersecurity Improvement Law is a start, not a final solution Who belongs on a high-performance data governance team? Interpreted vs. compiled languages: What’s the difference? IBM acquires MyInvenio to build its automation portfolio Structured vs. unstructured data: The key differences Picture of the Week Ecosystem of Hyperautomation Technologies Copyright 2021 TechTarget   To make sure you keep getting these emails, please add mail@newsletter.datasciencecentral.com to your browser’s address book. Join Data Science Central | Comprehensive Repository of Data Science and ML Resources Videos | Search DSC | Post a Blog | Ask a Question Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge This email, and all related content, is published by Data Science Central, a division of TechTarget, Inc. 275 Grove Street, Newton, Massachusetts, 02466 US You are receiving this email because you are a member of TechTarget. When you access content from this email, your information may be shared with the sponsors or future sponsors of that content and with our Partners, see up-to-date  Partners List  below, as described in our  Privacy Policy . For additional assistance, please contact:  webmaster@techtarget.com copyright 2021 TechTarget, Inc. all rights reserved. Designated trademarks, brands, logos and service marks are the property of their respective owners. Privacy Policy  |  Partners List

Source Prolead brokers usa

When it comes to Kubernetes, data mobility is the game changer

To repeat a commonly used adage, today every company is a technology company. Regardless of whether you are making computer chips or paper pulp, you are using technology, software and IT infrastructure to bring your products to market. Essentially, your company’s digital systems have become a weapon of business to deliver differentiated products and services, making digital transformation an imperative in order to stay competitive.  

The transition from traditional IT to cloud IT plays a significant role in these digital transformation efforts as it enables efficient use of resources and reduces costs and complexity. But the reality is that for all the potential benefits that cloud adoption brings, for many companies it hasn’t always delivered on its promise. Although the cloud has become the backbone of an organizations applications and systems, many organizations are discovering the shortcomings of a one-size-fits-all mindset toward cloud adoption. 

An enterprise centric approach to cloud 

The cloud has taken hold quickly. Only a decade ago, most enterprises were just beginning to scratch the surface of cloud adoption with sole focus on moving their applications to the public cloud. Many companies became single sourced on their cloud infrastructure and services.  

As requirements have become more complex, organizations are beginning to realize that a one–size–fits–all and a single provider approach is no longer sufficient. Enterprises need the flexibility to leverage resources from the public clouds, private clouds, and edge data centers to make IT more effective, reduce costs, increase agility, navigate regulatory requirements and deliver better service to their customers. The increasing adoption of multicloud and hybrid cloud architectures for IT infrastructure is a recognition of this need. 

However, for an enterprise, the cloud continues to be defined in terms of the provider of the public cloud or the technology adopted by the enterprise for their private or hybrid cloud. What is needed is an enterprise–centric vision for the cloud, as the distributed elastic pool of IT resources across public cloud providers, private clouds, and edge data centers that the enterprise has access to, within which they have the freedom to run applications wherever and whenever they want. 

Kubernetes everywhere and the Enterprise Cloud   

Containerization and Kubernetes are key to realizing this vision of the cloud. Kubernetes allows enterprises to define and build a consistent software–defined IT environment tailored for their specific needs, one that can run across multiple infrastructure silos and providers in private cloud, public cloud, and edge data centers. 

Applications can run anywhere the Kubernetes–based IT environment is instantiated and applications no longer need to be tailored to run on a given infrastructure or cloud.  Given this transformative capability, it’s no surprise that Kubernetes has quickly gained traction in the industry, with adoption increasing from 28 to 48% between 2018 and 2020 in enterprises, according to a survey by VMware.  

IT agility requires data mobility 

To realize the full potential of the cloud and have IT agility, application instances need to be mobile. Kubernetes and containers have made it easy to move application code between clouds and Kubernetes. But it has been much harder to make the underlying data mobile. A new approach is required – one that makes persistent volumes as mobile as application containers so that enterprises can fully benefit from their cloud investment.  

Container native storage solutions enable consistent data and storage management across infrastructure silos and cloud providers. This by itself isn’t sufficient to provide data mobility. Data migration and data replication technologies that are used today to copy or move persistent volumes between clouds and Kubernetes clusters are operationally complex and time-consuming. They require upfront planning and often require significant downtime. 

What’s needed is a new capability that allows persistent volumes to be moved as quickly as application containers – instant data mobility. Since moving all application data within a short period of time is not feasible and since moving data sets between clouds can be expensive, data mobility must allow the application to start working immediately, prioritize hot data over cold data, and implement strategies to minimize the amount of data that’s moved.    

The events of last year accelerated digital transformation and cloud adoption. The shift to Kubernetes and container-native storage simplifies IT. New capabilities in container storage solutions related to data mobility will allow enterprises to optimize their IT investments while maximizing effectiveness. This opens the door for innovation and creates a competitive advantage. It’s a whole new future.  

Source Prolead brokers usa

Risk Management Plan: What Is It and How To Write?

Planning is a path to success in business. Companies create a risk management strategy each time they begin a project because risks are an unavoidable part of any project. This document also aids in the identification of the gaps that may contribute to risk occurrence. Do you have a risk management plan for an upcoming project?

Risk is determined as a possible event that could cause damage. Risk management is a complex of measures, which enable the possibility to achieve successful results with consideration of all potential threats that may appear during the lifecycle of the project.

Before the project is implemented, it is necessary to create a team and develop a risk management plan. This document makes it possible to see the details, make timely adjustments, and revisions at different project implementation stages.

What Is A Risk Management Plan?

A risk management plan is a document describing approaches and principles of project risk management.

The risk management procedure of the project includes:

• Risks identification;
• Risks analysis and prioritization;
• Development of measures for eliminating or minimizing the risk effects;
• Risk monitoring and control.

If you want your piece of writing to look good, try using the services of Writing Judge.

How To Write A Risk Management Plan?

Even if you expect your project to go smoothly, it does not mean that everything will go the right way. It is up to humans and technique to make mistakes and fail. Hence, none of the businesses can avoid risks. Nonetheless, you can mitigate and anticipate them through an established risk management plan. Here are the steps you should take to write it:

Step 1. Risk identification and analysis

It involves the identification and analysis of the risks of your project. While some risks may be regarded as “known,” others might require a study to be discovered. To get well-researched information on a specific issue, you can visit Online Writers Rating to get high-quality, plagiarism-free products crafted by degreed professional writers.

Here are the main categories the risks fall to:

• External;
• Management;
• Organizational;
• Technical;
• Logistics-related.

One should consider all of them to prepare a set of appropriate measures to respond to them.

Step 2. Assessment of the risk, its consequences, and effect

To prioritize project risks, one asks three questions:

• What will happen if this situation takes place?
• How likely is it that this will happen?
• How bad will the resulting impact be on the project?

This helps to see the risks likelihood and their quantitative impact. For deeper research on a specific type of risk, you can contact Best Writers Online, which has specialists to do investigations you do not have time to do.

Step 3. Risk response planning

Risk response planning entails removing a risk, reducing its impact on the project, and preventing its occurrence. Begin with the danger that has the highest priority. Examine it with your team and see whether you can solve it or format it so that it no longer poses a risk to the project.

Step 4. Assign the roles in a team to monitor risks

Once you have determined a range of risks for your project, you will need to allocate each team member a particular risk category. Each of them will be in charge of devising strategies for dealing with the risk as it arises. This way, you will be able to manage all of the threats you will face along the way at the same time.

Step 5. Triggers

Risks do not emerge without triggers, i.e. mechanisms that activate them. This step involves consideration of situations that may launch risks typical to your project.

Why Write A Risks Management Plan?

There are many advantages of writing a risk management plan:

• Improved results. This allows the team to stick to the budget and achieve its objectives. Your projects become vulnerable and exposed to problems if you do not have well-defined risk management plans in place.
• Timely prevention. Having a detailed strategy allows you to take proactive action to address future problems before they arise and reduce the likelihood of their occurrence. Timely prevention is a part of effective time management as you do not have to spend much time to treat the risk when it occurs.
• General assessment of a project. It allows you to assess the progress of your current project and develop best practices for the future.

The risks occurrence is unavoidable in all kinds of projects. If you have a clearly defined risk management plan to guide you in this process, you can solve issues more effectively.

Source Prolead brokers usa