A Guide to Agriculture Process Tracking Solution for Agricultural businesses

In today’s time, digitalization is everywhere. Every sector is leveraging the benefits of digitalization, and the agriculture field is not behind when it comes to using the latest technologies and tools for enhanced productivity and efficiency. Today, people looking for an efficient farm management solution can easily find a number of custom-made crop management applications. These applications help users manage tillage, seeding spraying, fertilization, irrigation, harvesting, and various other farming tasks.

An agriculture process management or farm management software can enhance and manage farm operations and production activities in a proficient manner. Various activities that can be managed easily using this software solution include:

• Data storage
• Record management
• Monitoring and analyzing farming activities such as crop rotation, pest control, fertilizer/water saturation, seeding/harvesting, etc.
• Streamlining production and work schedules
  

The best thing about a farm management solution is that it can be customized to meet precise farm requirements. The customized solution allows users to manage and organize all their farm-related information in a centralized location. With the help of an agriculture process tracking solution, farmers can easily organize every aspect of farm management to augment productivity and farm operations. In addition, the solution helps them to keep a real-time check over their crops, workers, farm activities, tools & equipment, etc.

The software facilitates tracking real-time data about the farming activities, thus allowing users to make the best possible decisions based on real-time data. They can easily analyze the cost of production and other important activities and make decisions accordingly. Using farm management software, users can also monitor crop health, track rainfall, allocate resources efficiently, manage all kinds of field activities with ease, and generate real-time reports as and when required.

Features of Agriculture Process Tracking Application

Some of the key features of agriculture process tracking application include:

• Real-Time Farm Monitoring: With the help of a farm management software solution, users can easily monitor every farm-related activity in real-time. Real-time monitoring helps detect any glitch in an early stage and can be rectified as soon as possible without creating any mess.
• Analytics & Reports: The application software comes with the ability to generate a number of reports in real-time. This further helps to eliminate delays in the auditing process and makes tracking the farm’s progress a quick and flawless task.
• Comprehensive Traceability: The feature allows users to create detailed traceability reports related to all the farming activities to improve risk management and reduce any unfortunate incidents.
• Task Scheduling: The task scheduling feature allows users to perform every task on time without missing out or duplicating a particular task. Farmers and other related users can set their schedules as per their key requirements by selecting their template choice from a number of available templates.
• Record-Keeping Abilities: The application allows users to keep a detailed record of all the farm-related activities as they happen systematically. Records of employees, irrigation, harvesting, and other production practices can be stored with ease, and the users can access these records as and when required.
• Farmers Survey Data: The software can also be used for the survey purpose to get the right feedback from the farmers related to any farming activity, process, or tools. Based on the data gathered from the survey, informed decisions can be made to enhance the efficiency and profitability of the farming sector.
• Benefits of Farm Management Software Solution
• Some of the key benefits of using farm management software solution application include:
• Better Planning and Tracking of Farm Activities: The solution allows users to plan and track farm activities more efficiently and better. With the help of this software, users can plan various activities, including when and how to carry out crop activities, the best suitable time for fertilizers, appropriate pest control measures, and others. It also allows users to track various activities in real-time, thus helps them make the best decisions related to farm operations.
• Cost Savings: Agriculture process management software helps users to reduce various input and labor costs to a great extent. By integrating the operational and business data, the software helps in improving the efficiency, thereby helping in enhancing the ROI (Return on Investment).
• Better Risk Manage: There are several types of risks that the agriculture sector has to deal with, including weather conditions, market demand, diseases, etc. With the help of a farm management software solution that consists of various features like weather forecasting, users can easily take the best possible measures to avoid or at least reduce the risk to a great extent. The software helps determine the weather condition, pest/disease occurrence, the health of the soil, and much more and accordingly alerts farmers about any impending risks to take proactive measures on time.

With all the features mentioned above and benefits, customized farm management software is ideal for people associated with the agriculture sector. It helps in generating the best strategies and methods to keep an agricultural farm productive and profitable.

Conclusion:

To conclude, it would be correct to say that with the help of smart innovations like agriculture process tracking solutions, the farming business can be made more efficient, convenient, predictable, and profitable.

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A Guide to Agriculture Process Tracking Solutions

In today’s time, digitalization is everywhere. Every sector is leveraging the benefits of digitalization, and the agriculture field is not behind when it comes to using the latest technologies and tools for enhanced productivity and efficiency. Today, people looking for an efficient farm management solution can easily find a number of custom-made crop management applications. These applications help users manage tillage, seeding spraying, fertilization, irrigation, harvesting, and various other farming tasks.

An agriculture process management or farm management software can enhance and manage farm operations and production activities in a proficient manner. Various activities that can be managed easily using this software solution include:

• Data storage
• Record management
• Monitoring and analyzing farming activities such as crop rotation, pest control, fertilizer/water saturation, seeding/harvesting, etc.
• Streamlining production and work schedules
  

The best thing about a farm management solution is that it can be customized to meet precise farm requirements. The customized solution allows users to manage and organize all their farm-related information in a centralized location. With the help of an agriculture process tracking solution, farmers can easily organize every aspect of farm management to augment productivity and farm operations. In addition, the solution helps them to keep a real-time check over their crops, workers, farm activities, tools & equipment, etc.

The software facilitates tracking real-time data about the farming activities, thus allowing users to make the best possible decisions based on real-time data. They can easily analyze the cost of production and other important activities and make decisions accordingly. Using farm management software, users can also monitor crop health, track rainfall, allocate resources efficiently, manage all kinds of field activities with ease, and generate real-time reports as and when required.

Features of Agriculture Process Tracking Application

Some of the key features of agriculture process tracking application include:

• Real-Time Farm Monitoring: With the help of a farm management software solution, users can easily monitor every farm-related activity in real-time. Real-time monitoring helps detect any glitch in an early stage and can be rectified as soon as possible without creating any mess.
• Analytics & Reports: The application software comes with the ability to generate a number of reports in real-time. This further helps to eliminate delays in the auditing process and makes tracking the farm’s progress a quick and flawless task.
• Comprehensive Traceability: The feature allows users to create detailed traceability reports related to all the farming activities to improve risk management and reduce any unfortunate incidents.
• Task Scheduling: The task scheduling feature allows users to perform every task on time without missing out or duplicating a particular task. Farmers and other related users can set their schedules as per their key requirements by selecting their template choice from a number of available templates.
• Record-Keeping Abilities: The application allows users to keep a detailed record of all the farm-related activities as they happen systematically. Records of employees, irrigation, harvesting, and other production practices can be stored with ease, and the users can access these records as and when required.
• Farmers Survey Data: The software can also be used for the survey purpose to get the right feedback from the farmers related to any farming activity, process, or tools. Based on the data gathered from the survey, informed decisions can be made to enhance the efficiency and profitability of the farming sector.
• Benefits of Farm Management Software Solution
• Some of the key benefits of using farm management software solution application include:
• Better Planning and Tracking of Farm Activities: The solution allows users to plan and track farm activities more efficiently and better. With the help of this software, users can plan various activities, including when and how to carry out crop activities, the best suitable time for fertilizers, appropriate pest control measures, and others. It also allows users to track various activities in real-time, thus helps them make the best decisions related to farm operations.
• Cost Savings: Agriculture process management software helps users to reduce various input and labor costs to a great extent. By integrating the operational and business data, the software helps in improving the efficiency, thereby helping in enhancing the ROI (Return on Investment).
• Better Risk Manage: There are several types of risks that the agriculture sector has to deal with, including weather conditions, market demand, diseases, etc. With the help of a farm management software solution that consists of various features like weather forecasting, users can easily take the best possible measures to avoid or at least reduce the risk to a great extent. The software helps determine the weather condition, pest/disease occurrence, the health of the soil, and much more and accordingly alerts farmers about any impending risks to take proactive measures on time.

With all the features mentioned above and benefits, customized farm management software is ideal for people associated with the agriculture sector. It helps in generating the best strategies and methods to keep an agricultural farm productive and profitable.

Conclusion:

To conclude, it would be correct to say that with the help of smart innovations like agriculture process tracking solutions, the farming business can be made more efficient, convenient, predictable, and profitable.

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What Movies Can Teach Us About Prospering in an AI World – Part 2

Figure 1: The movie Big: “I don’t get it”

In the blog “What Movies Can Teach Us About Prospering in an AI World – Part 1”, I laid out the challenge that us humans will face surviving in a world dominated by AI.  I discussed the different learning techniques – such as machine learning, deep learning, reinforcement learning, and transfer learning – that are available to AI to accelerate its learning. If success in the future is purely defined by how fast one can learn, then us humans are truly doomed.

But are we really doomed?  Nah, I think us humans still have a few tricks up our sleeves, but we need to reframe the value that humans will bring to a world of AI.

“Big” is one of my favorite movies, and Tom Hanks is one of my favorite actors (though I still want Harrison Ford to play me in my movie).  There is lots to like about the movie, but the scene that really sticks with me is when the “adults” are brainstorming about creating a toy that looks like a building but transforms into a robot.  Review that scene here…

Josh (Tom Hanks): “I don’t get it.”

Mr. MacMillan (the boss played by Robert Loggia): “What don’t you get, Josh?”

Josh (Tom Hanks): “There’s a million robots that turn into something. This is a building that turns into a robot. What’s fun about playing with a building? That’s not any fun.”

Yes!  Us humans aren’t doomed to the dumpster.  We still have a role to play in a world driven by AI.  But that means we need to channel our inner Tom Hanks (Josh) and be willing to raise our hands when something just doesn’t make sense. 

Let’s say a person is applying for a car loan where today those decisions are more and more being made by an AI model.  The AI loan application model rejects the applicant because of the applicant’s spotty income history, student loan struggles, and sporadic work history.  The AI model assumes that the applicant’s past behaviors is indicative of future outcomes because that is all the AI model has to work with!

Now, a (human) loan officer intercedes on the application process.  The loan officer asks the applicant some questions about their intentions for the loan. The applicant explains that they want to buy a nicer car because they want to become an Uber / Lyft / DoorDash driver to generate a more predictable income stream.  The loan officer decides to give the loan applicant the loan.

Now, this is a scenario that the AI loan model hasn’t seen in the data, and consequently couldn’t easily extrapolate to ask those questions to make a more informed decision.  While the “safe” choice would have been to reject the loan application, the bigger picture is that if organizations can’t evolve to contemplate these edge cases, over time, they risk shrinking their total addressable market (see Figure 2).

Figure 2: Ethical AI, Monetizing False Negatives and Growing Total Addressable Market

So, what’s the key to embracing our inner Tom Hanks?  Here are my top 10 reasons (David Letterman anyone?) for the reason why humans will excel in a world of AI, if we learn to embrace the fact that AI will force humans to become more human.

Not sure why 10 is some magically number (Number of fingers on your hand? Number of frames in bowlin’? Bo Derek?), but here is my list of the top 10 human behaviors that can overcome that massive learning advantage that AI models have over us:

1) Thoroughly Align Goals. Invest the time upfront to thoroughly align and vet goals across a holistic and diverse set of stakeholders. Be sure that everyone is clear on what is trying to be achieved and why it’s important to each stakeholder (a key part of my “Thinking Like a Data Scientist” process). Bring together the different stakeholders – internal and external – who either impact or are impacted by the goals. Brainstorm (and prioritize) the metrics and KPIs against which the team will measure progress and success.  Leverage “future visioning” exercises to help all stakeholders to imagine what success looks like…to them.

2) Embrace Diversity of Perspectives.  AI models learn by identifying and codifying patterns, trends, and relationships buried in the data.  Diversity and outliers are not the friends of an AI model because they can skew the analytic results.  And that’s an area where humans can truly excel (if we can learn to overcome our own confirmation biases). Think holistically about the variables and metrics against which you want the AI model to optimize, and not just the financial metrics, but include customer, operational, environmental, societal, and diversity metrics as well.  Diversity may be the human secret weapon.  Diverse perspectives can create friction and friction can lead to synergizing new ideas.  There cannot be innovation without friction.  So be inclusive and welcoming of different perspectives.

3) Brainstorm What Could Go Wrong.  Empower the naysayers. Bring together folks who support as well as folks who do not support the goals. Brainstorm all the possible ways that things can go wrong.  All ideas are worthy of consideration.  Invest the time to understand and quantify the costs of the models being wrong. Don’t allow Groupthink, which is the practice of making decisions as a group in a way that discourages creativity or individual responsibility.  For a history lesson on Groupthink, check out the Bay of Pigs fiasco.

4) Empower to Challenge Conventional Thinking.  Empower everyone to think for themselves and question AI authority (channel your inner Timothy Leary). Embrace the power of “I don’t get it?”.  Empower your team members to raise their hands and stand up to challenge the thinking that’s on the table.  Embrace an operating model of “Disagree and commit” where folks are allowed to disagree while a decision is being made, but that once that decision is made, everybody must commit to full-on execution of that decision.  Be aware of and root out Passive Aggressive behaviors amongst the “Disagree and Commit” crowd.  No Pollyannish mentality here.

5) Collaborative Intelligence. Embrace the power of well-aligned, collaborative, diverse teams to bend, break, and blend traditional thinking and approaches into something new and more powerful. Become the master at collaboration. Uncover everyone’s unique assets and synergize the collaboration across those unique assets.  Yes, each of us is special (Mister Rogers).  Collaborate across different perspectives and experiences to create something greater than the sum of the parts.  Resistance is not futile!

6) Empathize. Be more human by being more understanding.  Seek first to understand before trying to be understood (Thanks Stephen Covey).  Seek to intimately understand your customers. And broaden definition of customers to include all those who you seek to serve, which should include your work colleagues.  Embrace empathy, walk in the shoes of others, stand up for what’s right, and truly care about others.  Design Thinking provides some marvelous tools if one has the right mindset to truly seek to empathize with their customers.

7) Stay Curious.  Innovation is driven by Curiosity.  Embrace your inner 5-year-old.  Try to understand why things work the way that they do.  Don’t be afraid to take apart that radio.  Leverage your natural curiosity and turn curiosity into creativity (envision, create, try, fail, learn, re-create, and try again) and turn creativity into innovation.  Remember, the base of the word “creativity” is “create”, so don’t be afraid to create or build schtuff.  And even if that schtuff doesn’t work, use that as motivation to fuel even more curiosity and create even more schtuff.  Build baby, Build!

8) Embrace Organizational Improv.  Identify and leverage everyone’s unique “assets” to achieve team and organizational agility.  Prepare everyone to lead because at different times in organizational improv, everyone will have to lead.  Execute like the US Women’s Olympic Soccer team or a great jazz quartet where everyone is prepared to take their shot or play their riff in sync with the rest of the team.  Think expanding team swirls not limiting organizational boxes.  Transition from a mindset of compromise to a mindset of abundance where everyone can win, and drive team execution from settling on the “Least Worst” to transforming to “Best Best” decisions.

9) Prepare to Unlearn.  Don’t be held captive by your outdated mental models.  Don’t be that person who falls back on “That’s the way that we’ve always done it.”  Don’t be that guy.  The world and capabilities are continuously changing, so challenging your conventional models may be the only way to stay current and valuable.  Besides, you can’t climb a ladder if you aren’t willing to let go of the rung below you.

10) Find Your Spiritual Foundation.  Ethics must be the foundation for our efforts to become more human.  Forgiveness, generosity, caring, compassion.  Think about the critical difference between “Do no harm” versus “Do good.”  And if you’ve forgotten the difference, re-read the Fable of the Good Samaritan.  Be more righteous and sincerely care about others, build for a better future, answer to a higher power.  The best textbooks for being more human?  The Bible, Torah, Shruti, Koran, or whatever your religious foundation. And finally, when in doubt about the right actions to take, apply the “Mom Test” – that is, what would your mom think if you were to explain your action to your mom (and hopefully your mom isn’t Ma Parker).

AI is going to force us humans to focus on nurturing the creativity and innovation skills that distinctly make us humans and differentiate us from the analytical machines. Innovation and creativity are the human ability and the willingness to be curious, ask provocative and challenging questions (like Tom Hanks in the movie “Big”), embrace diverse ideas and perspectives, blend these different ideas and perspectives into a new perspective (frame), and explore, test, fail, learn, test again, fail again, and learn again in applicability of the new blended perspective to real-world challenges.

Yea, us humans got this.

Figure 3: Is Analytics-driven Innovation the Ultimate Oxymoron?

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Understand your data better with Agile Data Governance

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Model-centric to Data-centric AI – am I missing something?

Introduction

Andrew Ng is a key reference point for me in understanding AI.

Andrew Ng is always easy to understand – especially for new and complex ideas.

Hence. it’s a bit challenging for me when I cannot fully understand something from Andrew

Recently, Andrew has been proposing the idea of MLOps from model centrc to data centric.

A lively discussion has been created – for example this conversation on linkedin has 778 plus comments

There is a good youtube discussion also

Why is this still not clear to me?

Yet for me, this idea of Model-centric to Data-centric AI is not fully clear

Let me elaborate

Recently, someone sought my advice on writing a new book on MLOps

I advised against it because MLOps is a crowded space already

So, when I think of MLOps from model centric to data centric – I find it hard to distinguish between MLOps itself

And for AI practitioners, MLOps is not new.

In fact, I would argue that if you are a large bank or similar institution, you could not risk deploying a model without MLOps

The second point is, model centric vs data centric is a dichotomy but in reality there are more than two elements. 

For example, you would need to consider at least data, models and features instead of just model vs data.

Analysis

The original discussion is framed as:

Would love your feedback on this: AI Systems = Code (model/algorithm) + Data. Most academic benchmarks/competitions hold the Data fixed, and let teams work on the Code. Thinking of organizing something where we hold the Code fixed, and ask teams to work on the Data. 

Hoping this will more closely reflect ML application practice, and also spur innovative research on data-centric AI development. What do you think?

I think in the above the operative word is ‘academic’

If so, that brings more clarity

AI is a unique discipline because it brings academic research with practise much more closely than other disciplines.

And the two worlds are quite different.

So, while MLOps is the norm for practitioners, it may not be so obvious to all as different perspectives amalgamate.

Some more comments

1. Raising the significance of good data for a model is always a good idea
2. In larger projects, at least three job types work together (data engineers, data scientists and devops engineers). So, again, there is value in raising the awareness of MLOps
3. Are we trying to say that MLOps should be about ensuring that data is consistent and of high quality throughout the project lifecycle? That could mean a data driven emphasis on MLOps. Raising that awareness is also a good view – as per the comment “Important frontier: MLOps tools to make data-centric AI an
   efficient and systematic process. “

To conclude

I find the framework of Model-centric to Data-centric AI limiting in the sense of holding model fixed and vary the data or vice versa. But nevertheless, it helps to raise awareness of data itself and could be useful when different perspectives of AI interplay

Image source: Andrew Ng

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How new technology trends impact web development in 2021?

With the rise of technologies, our day-to-day has completely transformed. As the transit of time, we are witnessing some major changes in the web development space. 

Technologies like IoT, AI, AR, VR is making human life simpler and perform monotonous tasks quickly. The advanced web development technologies offer top-notch products to people, which help them do tasks with fewer complications.

Introduction

Many products have been launched in the online world in recent years, which are most popular these days. The product reviews with interactive graphics can help your business’s website to stay ahead in the cutthroat competitive world.

With these advanced features and functionalities, you’ll leverage the perks for your website, which gives your business a huge profit. There are numerous updated changes available with the assistance of technology, the changes can work in the favor of web development requirements.

If you plan for website development or run an eCommerce website, you need to read the following latest advancements. Here are many things available which help you generate huge revenue on investment, look:

Artificial Intelligence and Bots

In the upcoming years, bots will be capable of self-learning and fulfill the users’ requirements. This means that bots will perform the work of executive employees, which eventually helps the companies in saving money. Giant B2C companies are already using this technology to answer their customers- chatbots inside social media platforms are one of the examples. Web development companies are now integrating bots on the website, PWA, and mobile applications.

Bot’s rise will make advent changes in website design, user experience. The important top-notch technology helps custom web development companies with virtual help design. The technology isn’t only help with visual experience, but also helpful in producing sound design. The most significant perk of AI-powered bots is that it has problem-solving potential, understand human behavior.

Accelerated Mobile Pages (AMP)

Accelerated Mobile Pages (AMP) is one of the trendiest web development practices. The motive is to reduce the loading time of web pages and enhance page performance. AMP technology is like PWA. Twitter and Google develop these AMP technologies with the help of the open-source plugin. 

AMPs are customized pages that function fast and have a simple yet interactive web design with some basic features, functionalities to offer their services and products. These pages are user-friendly and their content has high readability. 

With the emergence of 5G internet connectivity, companies are usually opting for native apps for enhanced user experience, but AMP plugins help brands save time. Because of this, small-scale startups can make their step into the free market world of technology.

Single Page Application (SPA)

Nowadays, with high-speed internet connectivity, you won’t be stuck on the web pages, you can click on multiple pages to download from a server. Single page application is the latest web development trend that helps companies in ignoring unwanted communication and showcases enhanced web page performances and offers prominent protection to the website.

With the help of the JavaScript framework, SPAs gain widespread popularity. The JavaScript framework helps browser events and runs the web applications smoothly. Google services like Gmail, Google Drive, or Google Maps and social media platforms like Facebook are some of the examples. The latest web trends make the websites more constructive, enhanced functionalities.

SPAs are designed to engage the users’ retention for a long time and help the website operate quickly. SPAs offer instant feedback to the companies and faster than the regular sites without server-side code at all (API technology).

Voice Search Optimization

The web development of future trends includes voice searches and it’s not only about Siri, Alexa, and Google Home. In the year 2021, more than half of the smart devices include voice recognition searches. And even more–the development can recognize the voices of people and offers a customized AI experience to the users.

Voice search is the technology of the future that was implemented more than a decade ago. In the year 2021, people don’t want to text or type commands. That’s why companies are looking for methods that help them make digital products integrated with voice command features. According to the reports, 55% of all the households’ devices are going to have voice assistant features.

The trend in web development is to launch voice recognition features in websites and applications. Powered with AI, voice searches help the owners and end-users quickly. First, it consumes a lot of time for users to place an order online on eCommerce websites. Secondly, the technology is multitasking. Third, it fetches the users’ loyalty, which helps them cope up with their routine. The most significant point is it helps companies to understand the users’ behavior.

Motion UI

Innovation web design is one of the latest trend brands in the digital world. As the transit of the years, the startups are focusing on fetching the user’s attention through innovative design. However, appealing designs have a high chance to get noticed by users. Visual design is now considered as the marketing strategy. 

Experts are now integrating motion UI into their web apps and websites. Motion user interface design has been the current market demand. Since the year 2018, the motion user interface has been widely accessible across the devices with the help of SaaS libraries technologies. 

Motion UI design makes the websites more interactive. The approach involves CSS transitions included with libraries and animated elements. Developers spend less time on building digital products, which eventually saves the price of web development.

Automation Testing

Most web technologies are developed with a motive to make the entire development process affordable and offer a remarkable user experience. Machine learning and the Artificial intelligence method let us develop difficult projects with fewer experts, while the automated testing tools help us check that the products are ready to launch or not. 

Test automation has several advantages including beta testing coverage, bug detection, and code transparency. The method helps the development team fetch test cases and reduce the development cost and time. The test automation will offer better products in less money. 

We all want to know why automation technology is still relevant in the year 2021? The answer is the digital world is getting more competitive. To survive in this cutthroat competitive world, you need to build a product that performs better than your competitor companies.

JavaScript Frameworks

JavaScript is one of the best programming languages in the world, which is the reason companies are still discussing it. In the year 2021, website trends include many aspects of JavaScript frameworks. 

JavaScript framework is considered as the prominent front-end development ecosystem. In the year 2021, JavaScript includes UX, UI, and testing, and code management. The framework offers components one requires building websites. 

This web trend has a lot of advantages. Immediate feedback and reviews are provided to the users regarding the page performance, code efficiency, and design testing. Mind that the latest version of the framework is comparatively better than the old version. Integrated with HTML templates, component-based design, data management tools that build the system more robustly.

Serverless Applications and Architecture

Serverless technology solves problems like data loss, system overloading times, and expensive development. Powered by providers like AWS, it developed serverless algorithms as cloud-computing models. 

The serverless app models help companies in development cost, robust the application with advanced features and functionalities with a safer internet environment. Cloud computing services offer a chance to build, execute and app features and functionalities without building a product architecture by themselves. 

Chatbots, IoT apps are examples of serverless technology. One task that needs to be excluded from the serverless technology is notification delivery, downloading files back. The top service provider of this technology is Google Cloud and Microsoft Azure.

Blockchain Technology

Recently, a report states that there are over 34 million bitcoin users across the globe. The technology involves software and hardware, legal regulation varies countries wise and a place for trading. Cryptocurrencies aren’t just about bitcoins. It’s about integrating secure, protected features into online e-wallet platforms. Blue-chip bank companies are building strong algorithms for protecting account holders’ data.

Cryptocurrencies aren’t the trendy web development technology. The technology was launched in the year 2004. Three years ago, the crypto trading market was built. Currency trading can’t be ignored by authorities. The utilization of blockchain technology has increased in the last few years and bitcoin and Cryptocurrencies are considered major payment systems.

Internet of Things (IoT)

IoT (Internet of Things) is a technology that interconnects the devices and helps the user to perform tasks without keeping their bodies in action. In the year 2021, some major web development trends are the example of IoT development.

According to the report, there will be over 30 billion IoT devices available in 2025. We expect that in the upcoming years there is going to be a huge requirement for IoT solutions. The basic concept of developing IOT devices is to make human life easy. Automation methods like automatic payment, smart home devices, and an online healthcare environment, allows humans to think about important things. 

The popular industries which are likely to opt for IoT devices are transport, healthcare, and housekeeping. IoT technologies also help web development companies in UX. We might see companies using IOT technologies in voice interface design.

Mobile-First Development

The number of smartphone users is increasing rapidly. 54% of the internet traffic is consumed with mobile phones and tablets. So we shouldn’t be surprised to know that website design should be according to the mobiles’ screen sizes.  

Mobile-first development is an idea in which you think about your web solutions according to the mobile screen and other hardware features. In simple words, you need to develop a web product that makes it easier for users to interact with your business through mobile phones.

It is advised now to deliver content with fewer complications, elements, and reduce page loading times. Include call-to-action, use a simple yet attractive design, and a simple and bright color scheme. This is significant to understand that since 2018, search engine Google has made some drastic changes in the algorithm pattern for mobile phone customized websites.

Responsive Websites (RWD)

The concept was launched 5 years ago when mobile industries were making enormous steps in the market. In the year 2019, most web searches are made by mobile users, which means that companies need to integrate advanced features to make their websites convenient for mobile phones and desktops.

Companies have a few major options: mobile-first development, invest in responsive websites. Responsive websites are developed using HTML codes with CSS templates which let the website render properly according to the devices’ screen size. The motive is to develop mobile-friendly web solutions. This technology is gaining immense popularity.

Push Notifications

Push notifications are the notification which users receive when they are not using the applications. Companies are using push notifications to fetch users’ engagement, improving conversion rate. Giant platforms like Facebook, Google Mail already integrate push notification technology into their web apps. 

In the upcoming years, we might witness small and medium-scale companies using these technologies. There are chances that this technology might replace email marketing and some other type of communication that companies do to connect with customers.

GDPR and Cyber Security

With automation operations like online payments, booking flights, paying taxes, and even using smart home devices, there is always a risk of a data breach. The cyber securities industry is likely to hit $300 billion by the end of the year 2024. At least we are expecting that web development companies should focus on developing solutions, which save our data from phishing attacks. 

The expansion of data privacy policies such as GDPR (General Data Protection Regulation) is the up-to-date web development market trend. The authorities have the power to put a fine on Internet vendors if they collect the users’ data, and if they sell or use their data without their permission.

Summing up

The above technologies are going to make a tremendous impact on the web development process. You need to build websites that conveniently communicate with the users and fulfill their requirements without complexities.  

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The Machine Learning Process in 7 Steps

In this article, I describe the various steps involved in managing a machine learning process from beginning to end. Depending on which company you work for, you may or may not be involved in all the steps. In larger companies, you typically focus on one or two specialized aspects of a project. In small companies, you may be involved in all the steps. Here the focus is on large projects, such as developing a taxonomy, as opposed to ad-hoc or one-time analyses. I also mention all the people involved, besides machine learning professionals.

Steps involved in machine learning projects

In chronological order, here are the main steps. Sometimes it is necessary to recognize errors in the process and move back and start again at an earlier step. This is by no mean a linear process, but more like trial and error experimentation. 

1. Defining the problem and the metrics (also called features) that we want to track. Assessing the data available (internal and third party sources) or the databases that need to be created, as well as database architecture for optimum storing and processing. Discuss cloud architectures to choose from, data volume (potential future scaling issues), and data flows. Do we need real-time data? How much can safely be outsourced? Do we need to hire some staff? Discuss costs, ROI, vendors, and timeframe. Decision makers and business analysts are heavily involved, and data scientists and engineers may participate in the discussion.

2. Defining goals and types of analyses to be performed. Can we monetize the data? Are we going to use the data for segmentation, customer profiling and better targeting, to optimize some processes such as pricing or supply chain, for fraud detection, taxonomy creation, to increase sales, for competitive or marketing intelligence, or to improve user experience for instance via a recommendation engine or better search capacities? What are the most relevant goals? Who will be the main users?

3. Collecting the data. Assessing who has access to the data (and which parts of the data, such as summary tables versus life databases), and in what capacity. Here privacy and security issues are also discussed. The IT team, legal team and data engineers are typically involved. Dashboard design is also discussed, with the purpose of designing good dashboards for end-users such as decision makers, product or marketing team, or customers. 

4. Exploratory data analysis. Here data scientists are more heavily involved, though this step should be automated as much as possible. You need to detect missing data and how to handle it (using imputation methods), identify outliers and what they mean, summarize and visualize the data, find erroneously coded data and duplicates, find correlations, perform preliminary analyses, find best predicting features and optimum binning techniques (see section 4 in this article). This could lead to the discovery of data flaws, and may force you to revisit and start again from a previous step, to fix any significant issue.

5. The true machine learning / modeling step. At this point, we assume that the data collected is stable enough, and can be used for its original purpose.  Predictive models are being tested, neural networks or other algorithms / models are being trained with goodness-of-fit tests and cross-validation. The data is available for various analyses, such as post-mortem, fraud detection, or proof of concept. Algorithms are prototyped, automated and eventually implemented in production mode. Output data is stored in auxiliary tables for further use, such as email alerts or to populate dashboards. External data sources may be added and integrated. As this point, major data issues have been fixed.

6. Creation of end-user platform. Typically, it comes as dashboards featuring visualizations and summary data that can be exported in standardized formats, even spreadsheets. This provides the insights that can be acted upon by decision makers. The platform can be used for A/B testing. It can also come as a system of email alerts sent to decision makers, customers, or anyone who need to be informed.

7. Maintenance. The models need to be adapted to changing data, changing patterns, or changing definitions of core metrics. Some satellite database tables must be updated, for instance every six months. Maybe a new platform able to store more data is needed, and data migration must be planned. Audits are performed to keep the system sound. New metrics may be introduced, as new sources of data are collected. Old data may be archived. Now we should get a good idea of the long-term yield (ROI) of the project, what works well and what needs to be improved. 

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About the author:  Vincent Granville is a data science pioneer, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent is also self-publisher at DataShaping.com, and founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target). You can access Vincent’s articles and books, here.

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How To Keep Data HIPAA Compliant?

Personal information theft for various crimes has developed a huge concern among customers for their data safety and security. Be it any industry, protecting consumer data should be on the top of the priority list and the healthcare industry is no exception. Hence a law called HIPAA (Health Insurance Portability and Accountability Act) was introduced in 1996 that guided different organizations to protect patient’s personal data. Let us know more about it.

Who Needs to be HIPAA Compliant?

Broadly, it is necessary for everyone who is anyhow associated with ePHI (Electronic Personal Health Information), which includes all organizations in the health sector. Not just hospitals and nursing homes, but agencies and service providers doing business with covered entities also have to be HIPAA compliant.

Any business engaged in supplying goods or providing professional services (attorneys, accountants, and consultants) to covered entities must follow HIPAA rules. Not adhering to the rule can land a business into a serious situation where they would end up paying hefty penalties.

Indeed, being aware of the requirements for compliance is the need of time. So, how to keep data HIPAA compliant?

Ways to Keep Data HIPAA Complaint

Before you make a move, you must be aware of the five critical aspects of a HIPAA compliance program, i.e., Privacy rules, Security rules, Transaction rules, Identifier rules, and Enforcement rules. Thorough knowledge of the compliance will make it easier to address each given solution adequately. Now, let us jump to the solution:

1- Distribute HIPAA policies and procedures to staff. 

Every staff member should be aware of the compliance that they have to follow. Create copies of HIPAA policies and procedures in easy-to-understand English language and assign them to all staff members. Make sure all the staff members read the copies and attest to the HIPAA policies and procedures they received.

Also, document all the staffs’ attestation as a record to prove that the organization has distributed the rules. The documentation should also have the annual reviews of your HIPAA policies and procedures.

2- Train employees through a basic HIPAA compliance program. 

Simply distributing copies of policies and procedures is not enough. The staff members also have to be trained about it so that they can practically implement the rules flawlessly.

After the training program does not forget to keep its documentation which would be required during the audit. Additionally, appoint a staff member as the HIPAA Compliance, Privacy, or Security Officer who will be responsible to implement policies, procedures, and standards under HIPAA rules.

3- Identify all business associates defined under HIPAA rules. 

Your organization might have associates who are receiving, transmitting, maintaining, processing, or accessing ePHI. Identify them all, as they all are defined under HIPAA rules and must have a Business Associate Agreement (Business Associate Contract) in place for each such associate. Even if you are an associate, always ensure you have a copy of the agreement.

Once you have the list of the associates, audit them to make sure they are compliant with HIPAA rules. As proof, create a report of the audit and document it.

4- Develop a management system to handle security incidents/breaches.

Handling security incidents or breaches is the job of the professionals. Have a dedicated management system in place to address such issues. The system must also allow staff members to anonymously report any security incident if needed.

Plus, you should track and manage all investigations of any incidents relating the PHI security. Create a record of all incidents of breaches, be it minor or significant. So that you can demonstrate your investigation to the assigned auditors.

5- Conduct an annual risk assessment.  

Self-audit is a good way to start with. But, later on, go for a fully independent auditing team for HIPAA assessment, comprising certified engineers and compliance experts. The team ensures you go through an unbiased audit. Investing over an auditing service is sensible since risk assessment is a complex process that involves identifying multiple possible risks to an organization and addressing any vulnerabilities relating to network security. 

Additionally, they are knowledgeable enough to educate you about the compliance, are experienced experts, provide support and protection required to secure PHI, and help clear your audits.

6- Conduct regular penetration testing and vulnerability scans.  

Frequently scanning vulnerabilities clarifies the criticality involved and the network and data protection. Make sure your auditing team tests the security on a monthly or quarterly basis. Ask them for a complete report of external, internal, and web application testing to add to your record. They should also provide you with strategies or remedies to overcome the shortcomings. 

7- Strengthen application security.  

Technology is evolving every day and so are the cybercriminals, looking for every possible flaw in the security layers to get into your ePHI system. Secure every element of your web-facing applications, including the design, development, and deployment. Update the applications and security apps regularly.

Assess the application thoroughly for any vulnerabilities and address any design flaws immediately. Never ignore any security gaps that might compromise security and HIPAA compliance. Additionally, get screen servers, privacy screens, and professionally managed technology solutions to build additional security layers. Managing and fixing risks right on time will save both your time and money.

Summary

Adhering to the HIPAA rules is not only legally important but also necessary to ensure that patients can trust you with their personal health information. The rules are designed in a way that ensures that every entity that collects, maintains, or uses confidential patient information keeps it safe.

Nowadays, organizations use a SAS-based MR (Medical Records) solution. But does not exempt them from taking responsibility for maintaining patient data privacy. As a covered entity or provider, it is completely your responsibility to protect the data.

The above-mentioned tips will help you stick to HIPAA compliance, but at the same, it is essential to understand that it is incredibly challenging to do so by yourself. You must consider getting professional help to avoid lax security.

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Outbreak Analytics at the Forefront of Data Science

• Epidemics remain a major public health issue. 
• Data science steps in when traditional modeling methods fail.
• Outbreak analytics can handle the complex data involved in pandemic assessment.

The last two decades saw more new infectious diseases of global concern than in any similar period of history [1]. Ebola, Influenza A (H1N1), SARS, MERS, Zika virus were all of concern, but it wasn’t until the COVID-19 pandemic hit full force that the world became acutely aware of the inadequacy of the traditional outbreak response system.  Despite officials’ best efforts to contain the Covid-19 outbreak, the traditional methods—surveillance, response, and management—failed to address ample warnings, in part because of inadequate procedures for handling diverse data. 

The successful investigation and containment of infectious disease outbreaks relies on analyzing complex and diverse data sources. Analysis of this data can only be achieved by a multidisciplinary approach, using several different, complementary approaches and tools including outbreak analytics. Clinical researchers and medical professionals around the globe joined forces to find ways to handle complex data, open-source data, and work collaboratively on improvements to the system [2].  

 An Overview of Outbreak Analytics

Outbreak analytics was developed to focuses on the technological and methodological aspects of the outbreak data pipeline from data collection to informing outbreak response. It sits at the crossroads of data science and a variety of public health fields including planning, field epidemiology and methodological development. Outbreak analytics is part of an overall prevention and control plan that includes several other core pillars of outbreak response including case management, surveillance and contact tracing, logistics, and testing [3].

The interdisciplinary field uses data science methods from a variety of perspectives to inform outbreak response, including [4]:

• Bayesian statistics,
• Database design and mobile technology,
• Evidence synthesis approaches,
• Frequentist statistics,
• Geostatistics,
• Graph theory,
• Interactive data visualization,
• Mathematical modelling,
• Maximum-likelihood estimation,
• Genetic analysis.

It can help to answer questions like [4]:

• What are mortality and risk factors?
• Could a rapid test help reduce incidence?
• Which is the optimal vaccination strategy?
• Should international travel be restricted?
• Has the delay between symptom onset and hospitalization been reduced?

Despite many advances in outbreak analysis over the last 18 months, like contact tracing, pandemic modeling and risk assessment, adoption of outbreak analytics has been at a snail’s pace. A unified platform for the analysis of disease outbreaks is still lacking [5].

The Future of Outbreak Analytics

The emergence of outbreak analytics highlights the need for freely available, high-quality, and open-source methods for handling infectious disease outbreaks. While not yet fully recognized as a field deserving of recognition and support [6], it’s likely that the development of outbreak analytics will continue to grow. Many agencies including the World Health Organization and UNICEF have already implemented outbreak analytics into their programs. Earlier this year, a major step towards recognition was made when the Assistant to the President for National Security Affairs (APNSA), in coordination with various other coordinators and agencies, were directed by the Whitehouse to develop a plan for establishing an interagency National Center for Epidemic Forecasting and Outbreak Analytics [7]. Going forward, as globalization leads to increasing pandemic risk, expect to hear a lot more about this emerging field of data science.

References

Image: Author

[1] To Prevent Future Pandemics, The U.S. Should Invest In ‘Real-Time R…

[2] How Data Science Helped Combat the Coronavirus Outbreak

[3] Back to basics: the outbreak response pillars

[4] Outbreak analytics: a developing data science for informing the res…

[5] OutbreakTools: A new platform for disease outbreak analysis using t…

[6] Why development of outbreak analytics tools should be valued, suppo…

[7] National Security Memorandum on United States Global Leadership to …

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New Book: Data Science for Economics and Finance – Methodologies and Applications

This post is to share with you the recent publication of the book: “Data Science for Economics and Finance: Methodologies and Applications“, by Sergio Consoli, Diego Reforgiato Recupero, and Michaela Saisana.

The use of data science and artificial intelligence for economics and finance is providing benefits for scientists, professionals and policy-makers by improving the available data analysis methodologies for economic forecasting and therefore making our societies better prepared for the challenges of tomorrow.

This book is a good example of how combining expertise from the European Commission, universities in the U.S. and Europe, financial and economic institutions, and multilateral organizations, can bring forward a shared vision on the benefits of data science applied to economics and finance; from the research point of view to the evaluation of policies on the other hand. It showcases how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the economic and financial sectors. At the same time, the book is making an appeal for further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies.

The book is entirely published as Gold OA to reach a large audience. Here are the links:

https://www.springer.com/gp/book/9783030668907 

https://link.springer.com/book/10.1007%2F978-3-030-66891-4

This book follows up another previously published Springer volume titled: “Data Science for Healthcare: Methodologies and Applications”, which was co-edited by Sergio Consoli, Diego Reforgiato Recupero, and Milan Petkovic, that tackles the healthcare domain under different data analysis angles. 

Considering the number of recent initiatives that are now pushing towards the use of data analysis within the economic field, we are pursuing with the present book at highlighting successful applications of data science and artificial intelligence into the economic and financial sectors.

We believe the topics dealt by the book to be extremely relevant nowadays within the scientific community, and that the book would be an interesting read for the related audience to let them be acquainted with the latest advancements on these subjects.

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