4 Top Reasons for Organizations to Move to Python 3

Python is one of the most widely used programming languages on the planet. Over the years programmers have fallen in love with Python for its increased productivity and capabilities. However, a constant argument persists on whether Python 3 is better than Python 2 and would it be a wiser decision to completely shift to Python 3.

Although Python 3 has been in existence for over 7 years, programmers are quite skeptical about using it. Most programmers tend to cling to Python 2, completely ignoring the capabilities of the new version.

This blog will walk you through some major drawbacks of Python 2 and will pinpoint 4 top reasons on why to choose Python 3 over Python 2.

Why Is Python 3 On The Rise?

Before we dive deeper into the major advantages of Python 3, let’s take a look at its origin. 

Released in 2008, Python 3 was introduced to overcome the flaws of Python 2. The major reason behind developing Python 3 was to clean up the codebase and remove redundancy. Although Python 3 is the newer version of Python, it is not necessarily backward compatible with code written in the 2.x version. 

Significant features of Python 3 makes it simpler, easier and incredibly efficient to use. Here’s listing the major differences between Python version 3.0 and 2.0, and the reasons why Python 3 can make a better programming partner.

1. Unicode Character Encoding

As mentioned earlier, Python 3 was introduced to address the vulnerabilities and drawbacks of Python 2. Hence, it reduces the complexities of coding and improves speed and performance. 

Where in Python 2 the character encoding is done in ASCII format, Python 3 is Unicode based. In Python 2 the strings are by default stored in the form of ASCII values. Programmers are required to add ‘u’, to store strings specifically in Unicode format. In Python 3 strings are stored in UTF-8 format, enabling a large number of storage features such as character value storage, different language characters, and emojis storage as well. 

Dropped deprecated features which were frequent sources of bugs in Python 2 have also been replaced by superior alternatives and retained solely for backward compatibility. 

For instance: If a file is created by with non-ASCII characters in the name, for the below-mentioned code, if you are using Python 2 your code is sure to throw an error 500. 

However, in Python 3  your error will be detected right away saving you the time on long code creation. Moreover, the error message is much easier to understand. Knowing that str object is owner and node is a bytes object, it is easy to recognize that the error is due to listdir returning a list of bytes objects. 

Adding listdir(‘.’) would make the bug disappear as this would appear as a Unicode string in Python 3.

The difference in the behavior is due to the difference in how each version handles the string type. Whatever is lumped together in Python 2 is split in Python 3.

2. Improved Library Standards

When it comes to libraries, there is a huge difference between the two versions. Many libraries developed in Python 2 are not forward compatible. Hence, the new 3.x version is developed focusing on providing good compatibility. Moreover, most of the actively maintained libraries are strictly created for use with Python 3. Hence, it’s suggested to keep codes compatible with Python 3 to help keep your test running on both versions. 

3. Improved Integer Division

Reducing a programmer’s confusion and frustration, Python 3 is created with a syntax that’s more intuitive. Python 3.x version has an elegantly designed structure that allows performing an action with fewer lines of code. Python 2, on the other hand, requires the exact input to perform a particular result or generate the expected results.

For instance: If you try a simple calculation like 5/2 (5 divided by 2), Python 2, after rounding up would give you the result as 2. To derive the exact result, that is 2.5, the input should be 5.0/2.0.

Whereas, Python 3 would right away, give you the answer 2.5 for the input 5/2, without converting the numbers to float data type.

4. End of Python 2 Support

Yes! Python 2 is expected to stop all support and maintenance by January 2020. Now, this is another major reason why you need to shift to Python 3 at the earliest. By end of support, we mean that all-new packages will be built on Python 3 and hence, it will be difficult to add any new features to the existing Python 2 projects. Major plugins are also being ported to Python 3 and thus, the upcoming updates of these plugins will be available only for the 3.x version. 

Moving forward, it will be difficult to find any Python 2 support services or developers. Also, the Python 2 hosting options will grow more scarce and costly. The Python 3.x version and the releases ahead are believed to have different syntax from that of the current version and thus, getting upgrades for existing features would be difficult to find.

Why Should You Stop Using Python 2?

Although programmers have widely accepted and loved working with the 2.x version of Python, there is quite a huge list of flaws and drawbacks experienced with it. Here’s listing a few of them. 

1. Firstly, as mentioned in this post, the Python 2 text model is not Unicode capable. It doesn’t handle non-ASCII files correctly. This is one of the major drawbacks of the version. Python 2 handles Unicode module names quite inconsistently, which is a source cause of multiple programming errors. That is why 3.x version of Python is designed to have a Unicode based string type by default.

2. In addition to not being Unicode capable, there is a large number of Unicode handling bugs in Python 2 standard library that might never be fixed. Fixing these bugs within the constraints of Python 2 is too difficult, and not worth the effort.

3. Python 2 iterator was designed long before the introduction of the iterator protocol. Thus, it has a lot of unnecessary and lengthy listings, which can now be made more memory efficient.

4. Programmers who have been involved with Python 2 for a long time might have noticed that the version interprets numbers in a strange way if they have leading zeros. Also, the version has two different kinds of integers. Python 2 beginners are often surprised to find that the version can’t do basic arithmetic correctly.

5. The print and exec statement is also weirdly different from the normal function calls like eval and execfile. Moreover, you need parentheses to catch multiple exceptions.

6. Although list comprehensions are one of Python’s most popular features, surprising errors arise on the local namespace. Also, if you tend to make a mistake in handling the errors, there might be chances where you’ll lose the original error.

Eliminating all these persisting errors and flaws of Python 2, the new version 3.x is specifically designed to enhance the quality and efficiency of the programmers. Thus, it is highly recommended to start preparing for a complete shift to Python 3. For developers who would like to check on to the Python 3 upgrade packages, here’s the command you can use:

[Don’t forget to create a test-requirement.txt file when using the command.]

With increased competition and high consumer expectations, programmers are under constant pressure to improve software performance. With the efficiency and ease of use offered by Python 3, programming is sure to achieve greater success than before. Although Python 2.7 will be supported until 2020, the sooner the switch, the better.

If you are looking for a technology partner to help your business transform with the latest digital trends, then get in touch with our custom software development experts today!

Data Mining Vs Predictive Analytics: Learn The Difference & Benefits

With big data becoming the lifeblood of organizations and businesses, data mining and predictive analytics have gained wider recognition. Both are different ways of extracting useful information from the massive stores of data collected every day. Often thought to be synonyms, data mining and predictive analytics are two distinct analytics methodologies with their own unique benefits.

This blog examines the differences between data mining and predictive analytics. 

Difference Between Data Mining and Predictive Analytics

Data mining and predictive analytics differ from each other in several aspects, as mentioned below:

Definition

Data mining is a technical process by which consistent patterns are identified, explored, sorted, and organized. It can be compared to organizing or arranging a large store in such a way that a sales executive can easily find a product in no time. Various reports state that by 2020 the world is poised to witness a data explosion. Therefore, data mining is a strategic practice that is necessary for successful businesses. It helps marketers create new opportunities with the potential for rich dividends for their businesses. 

Predictive analytics is the process by which information is extracted from existing data sets for determining patterns and predicting the forthcoming trends or outcomes. It uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. In other words, the aim of predictive analytics is to forecast what will happen based on what has happened. 

Techniques and Tools

Although there are many techniques in vogue, data mining uses four major techniques to mine data. They are regression, association rule discovery, classification, and clustering. These techniques require the use of appropriate tools that have features like data cleansing, clustering, and filtering. Python and R are the two commonly used programming languages in data mining.

Unlike data analytics, which uses statistics, predictive analytics uses business knowledge to predict future business outcomes or market trends. Predictive analytics uses various software technologies such as Artificial Intelligence and Machine Learning to analyze the available data and forecast the outcomes.

Purpose

Data mining is used to provide two primary advantages: to give businesses the predictive power to estimate the unknown or future values and to provide businesses the descriptive power by finding interesting patterns in the data.

Predictive analytics are used to collect and predict future results and trends. Although it will not tell businesses what will happen in the future, it helps them get to know their individual consumers and understand the trends they follow. This, in turn, helps marketers take necessary, action at the right time, which in turn has a bearing on the future.

Related Reading: Predictive Analytics: The Key to Effective Marketing and Personalization 

Functionality

Data mining can be broken down into three steps. Exploration, wherein the data is prepared by collecting and cleaning the data. Model Building or Pattern Identification by which the same dataset is applied to different models, thus enabling the businesses to make the best choice. Finally, Deployment is a step where the selected data model is applied to predict results. 

Predictive analytics focuses on the online behavior of a customer. It uses various models for training. With the use of sample data, the model could be trained to analyze the latest dataset and gauge its behavior. That knowledge could be further used to predict the behavior of the customer. 

Talent

Data mining is generally executed by engineers with a strong mathematical background, statisticians, and machine learning experts. 

Predictive analytics is largely used by business analysts and other domain experts who are capable of analyzing and interpreting patterns that are discovered by the machines. 

Outcome  

Data mining enables marketers to understand the data. As a result, they are able to understand customer segments, purchase patterns, behavior analytics and so on. 

Predictive analytics helps a business to determine and predict their customers’ next move. It also helps in predicting customer churn rate and the stock required of a certain product. Additionally, predictive analytics enable marketers to offer hyper-personalized deals by estimating how many new subscriptions they would gain as a result of a certain discount, or what kind of products do their customers seek as a complement to the main product they bought from the seller. 

Related Reading: Using Predictive Analytics For Individualization in Retail

Effect of Data Mining and Predictive Analytics on the Future 

The global predictive analytics market is estimated to reach 10.95 billion by 2022. We are now in a period of constant growth, where businesses have already started using data mining and predictive analytics sift through the available data for searching patterns, making predictions and implementing decisions that will impact their business.

Both approaches enable marketers to make informed decisions by increasing productivity, reducing costs, saving resources, detecting frauds, and yielding faster results. To make the best use of data mining and predictive analytics, you need the right guidance and the best expertise. Talk to our experts and find out how Fingent, top custom software development company can help your business scale up with the power of data. Get on your way to a digital-first future with us.  

Can Data Warehousing Enhance the Value of Data Visualization & Reporting?

Organizations rely heavily on data to make crucial business decisions. Hence, it is important for your business to have access to relevant data. That is where a well-designed data warehousing comes to your rescue!

Besides gaining actionable insights, corporate executives, business managers, and other end-users make more informed business decisions based on historical data. 

Today’s Analytics and Business Intelligence solutions provide the ability to:

• Optimize business processes within your organization
• Increase your operational efficiency
• Identify market trends
• Drive new revenues
• Forecast future probabilities and trends

Before understanding how data warehousing can add more value to data visualization and reporting, let’s take a look at what these terms mean.

Analytics and Business Intelligence

Business Intelligence is a process that includes the tools and technologies to convert data from operational systems into a meaningful and useful format. This helps organizations analyze and develop meaningful insights to take timely business decisions. The information derived from these tools demonstrate the root cause of your business problems and allow decision-makers to strategize their plans based on the analysis.

Business Intelligence is information not just derived from a single place, but multiple locations and sources. It can be a combination of the external data derived from the market and the financial and operational data of an organization that is meaningfully applied to create the “intelligence”. 

Data Warehouse

Data warehouse is a repository that collects data from various data sources of an organization and arranges it into a structured format. An ideal data warehouse set up will extract, organize, and aggregate data for efficient comparison and analysis. Data warehouse supports organizations in reporting and data analysis by analyzing their current and historical data. This makes it a core component of Business Intelligence.   

Unlike a database, that stores data within, at a fully normalized or third normal form (3NF), a data warehouse keeps the data in a denormalized form. It means that data is converted to 2NF from 3NF and hence, is called Big Data. 

Key benefits of a Data Warehouse

• Combine data from heterogeneous systems
• Optimized for decision support applications
• Storage of historical and current data 

Why We Need Data Warehouse for Business Intelligence?

Before the business intelligence approach came into use, companies used to analyze their business operations using decision support applications connected to their Online Transaction Systems (OLTP). Queries or reports were retrieved directly from these systems. 

However, this approach was not ideal due to: 

• Quality issues 
• Reports and queries were affecting business transaction performance 
• Data resides in heterogeneous sources 
• Non-availability of historical data
• Non-availability of data in the exact form required for reporting

Connecting your organization’s business intelligence tools to a data warehouse can provide you benefits in terms of production, transportation, and sale of products.

Data Visualization vs. Data Analytics – What’s the Difference?

Data Warehousing and Business Intelligence Using AWS 

Today, traditional BI has given way to agile BI where agile software development accelerates business intelligence for faster results and more adaptability. Big Data is growing fast to provide useful insights for making improved business decisions.

There has been a paradigm shift in data storage with warehousing solutions moving increasingly to the cloud. Amazon Redshift, for instance, is one of the most popular cloud services from Amazon Web Services (AWS). Redshift is a fully-managed analytical data warehouse on cloud, that can handle petabyte-scale data, which enables analysts to process queries in seconds. 

Redshift offers several advantages over traditional data warehouses. It provides high scalability using Amazon’s cloud infrastructure to set-up and for maintenance, without the need for upfront payments. You can either add nodes to a Redshift cluster or create additional Redshift clusters to support your scalability needs.

You can use AWS Marketplace ISV Solutions for Data Visualization, Reporting, and Analysis.

Data visualization helps you identify areas that need attention or improvement, clarify factors that influence business such as customer behavior, and making decisions such as finding out a suitable market for your product or predicting your sales volumes, and much more.

TIBCO Jaspersoft, for example, is a solution that delivers embedded BI, production reporting, and self-service reporting for your Amazon data at affordable rates. It features the ability to auto-detect and quickly connect to Amazon RDS and Amazon Redshift. Jaspersoft is available in the AWS Marketplace in both single-tenant and multi-tenant versions. TIBCO Jaspersoft for AWS includes the ability to launch in a high availability cluster (HA) as well as with Amazon RDS as a fault-tolerant repository. Pricing is based on the Amazon EC2 instance, type as well as the chosen single or multi-tenant mode.

Image source: http://bit.ly/2IWWCDn 

Summary

By moving your analytics and business intelligence to a hybrid cloud architecture you will be able to handle huge amounts of data and scale at the rate of expansion required by your business. You will also be able to deliver information and solutions at the speed that your employees and customers demand, and gain insights that will enable your organization to innovate faster than ever.

Business Intelligence and Data Warehousing are two important aspects of the survival of any business. These technologies give accurate, comprehensive, integrated, and up-to-date information on the current enterprise scenario which allows you to take the required steps and make crucial decisions for your company’s growth. To know how your business can benefit from the latest technologies, get in touch with our custom software development experts today!

Can Augmented Reality Improve Conversion Rates For Businesses?

Augmented Reality (AR) is a powerful tool that brands are using to bridge the gap between their products and their digitally empowered customers. With the power of AR, customers can see and experience their products without actually trying them on, helping them make purchase decisions instantly. AR is being widely used by many brands as the most cost-effective way to convert strangers into customers and promoters.

Let’s consider why and how top brands are embracing Augmented Reality for immersive customer experience. 

Top Brands Embracing AR 

When AR technology was pioneered by Ivan Sutherland in 1968, he described his concept of the Ultimate Display as: “The ultimate display would, of course, be a room within which the computer can control the existence of matter. A chair displayed in such a room would be good enough to sit in. Handcuffs displayed in such a room would be confining, and a bullet displayed in such a room would be fatal. With appropriate programming, such a display could literally be the Wonderland into which Alice walked.”

Today we are witnessing the massive affect AR is having on immersive customer experience. Wonderland looks close!

Here are a few examples of top brands that are using AR effectively to achieve this. 

LEGO Wear’s AR Shop

LEGO Wear’s first limited-edition clothing line for adults was featured on Snapchat with a uniquely designed Snapcode. This virtual cloth store had no clothes on display but allowed customers to shop virtually on a Snap-triggered portal. Studies reveal that Snachatters engage with Augmented Reality naturally on a large basis.

Around 70% of them play with an AR lens every day. So with the combination of AR and e-commerce leading to exciting customer experience, the company was able to drive sales rapidly.

Watch how AR simplify equipment maintainance

This video is made using InVideo.io

Patron Tequila’ s Immersive Distillery Experience 

Patron’s AR app is designed for its tech-savvy customer persona. Understanding the mindset of today’s socially connected generation to know more about the products they consume, the Patron AR app allows its customers to take a virtual tour of their distillery in Mexico giving them a glimpse of the history and origins of the distillery.

Speaking about the success story, the app-enabled more than a million consumers to have a memory of visiting Hacienda without even physically going to the place. Also, thousands of people have interacted with a virtual Patrón bartender, who is actually not a real person at all, but a robust data processing tool to simulate a real-world interaction.

Nike Snaps Up Customers

The Air Jordan Brand in collaboration with Snapchat and Darkstore, out together a virtual experience that brought in record sales last year. Using a special 3-D AR world lens of Michael Jordan circa 1988, taking off from the free-throw line in the slam dunk contest, Nike was able to target Snapchat users around the Staples Center where an NBA All-Star Game was taking place.

Users could walk around the lens and see Jordan changing into the All-Star uniform and the new AJ III Tinkers. In the following event, Snapchat brought out a QR code that users could use to buy the shoes from the Snap Store. The shoes would be delivered within 2 hours.  The shoes sold out in 23 minutes! 

MAC Cosmetic’s AR-enabled Video Tutorial

In June of this year, MAC cosmetics launched AR-enabled shoppable video tutorials on YouTube in partnership with the beauty influencer, Roxette Arisa. As the tutorial is playing, a ‘try on’ button appears below the video, allowing viewers to try on different shades of lipstick as they continue to watch the tutorial.

Once they have made a choice, they can order the lipstick without leaving the app. MAC sees a huge potential in this as beauty-related content generated more than 169 billion views on YouTube last year.

Speedo’s Customers Try It On Virtually

Early this September, leading swim gear company Speedo launched a mobile app that lets their customers try out their goggles before they buy it. This app is compatible with both Android and iOS phones. Commenting on this feature, Pentland Brand’s head of innovation Ben Hardman said, “This technology will undoubtedly enhance our customers’ shopping experiences by allowing them to interact with the product before they make a purchase. In this instance, it helps them address a well-documented human pain point: leaky goggles.” 

How Augmented Reality Can Simplify Equipment Maintenance

AR Is the Future – Are You Ready?

AR technology is working wonders for the sales and productivity of many brands. It is improving its business operations while giving their customers a more immersive and fun experience. Brands are able to market their products in fresh and interesting ways and are seeing great returns.

Fingent works with brands to achieve this for their business. By using AR technology like Microsoft HoloLens and more, we are making AR possible for our clients.

Reach out to us and know how Augmented Reality technology can be used to improve your customers’ experience and scale your business. 

Can Empowering AI and IoT Bring In Competitive Advantage To Industries?

It takes more than forward-thinking employees to gather customer purchasing trends and improve the customer experience. International companies depend on Artificial Intelligence (AI) and the Internet of Things (IoT) to drive data and forecast the next big wave of trends.

Studies predict Asia and North America to lead in the innovation of AI and IoT. Also, embedded AI in support of IoT smart objects will reach $4.6B globally by 2024.

Major vendors of IoT platforms such as IBM, Amazon, and Microsoft have started offering integrated AI capabilities like ML-based analytics. Scalable digital platforms are designed daily to understand the way customers think while using predictive maintenance in real estate, eCommerce, healthcare, and other industries.

It’s time for us to share the leading examples of how businesses use AI and IoT, and how these technologies benefit them.

AI and IoT: Leading Use Cases

Smart Cities: Making Life Easier

What happens when AI and IoT run a city? It turns into a smart city. Smart city technology can solve an energy crisis, help manage traffic, or improve the healthcare experience. 

One example of a smart city is the use of Advanced Transportation Controller technology linked to a 5G network in Los Angeles. There are road-surface sensors throughout the city, and cameras that monitor traffic, sending information to traffic management systems. Municipal employees can now analyze the data of traffic congestion and issues with traffic lights in high traffic areas. Overall, this improves the quality of living in Los Angeles and helps a business run smoothly without delays.

Convenience in Property Management

One of Fingent’s clients WRI Property Management, a US-based single-family rental provider with 10,000+ leased properties and 20,000+ managed houses experienced many challenges. Here are a few of the issues WRI Property Management faced:

• Tenant eviction
• Rent collection/accounting
• Scheduling property inspections
• Leasing properties
• Screening tenants

What happened next? Fingent introduced an advanced software platform, Honey Badger. The AI and IoT technology-supported WRI managers to conveniently communicate with multiple parties, renovating properties, view lives auction feed, track the construction of new properties, etc.

5G Network Vehicle Safety and Security

Machine Learning technology is improving the autonomous vehicle experience. How does it work? An automobile can stop when a driver is in dangerous tragic weather or unexpected situation. 

The 5G network can cause the brakes of a car to operate by tracking vehicle sensors of other drivers near prevent or relieve car crashes. 

The network can also send drivers a traffic update to use detours and avoid certain roads that are under construction or is unsafe.

AI and IoT Business Benefits 

1. Guaranteed Security and Safety

A company’s highest priority is protecting data in the workplace. As Artificial Intelligence scans security footage, IoT can close gates or doors if an intruder attempts to enter the premises of a head office. 

Organizations are now using machine-to-machine communication to determine potential security threats with an automated response to hackers or intruders. 

An example of AI and IoT in banking security is the detection of fraudulent activity in ATMs to communicate updates to law enforcement to protect customers.

The unexpected workplace accidents can be prevented by using sensors that monitor safety hazards as employees work. Employees at some organizations now wear wearable devices that alert the management of undetected dangers such as carbon monoxide released into the air on a work site. 

2. Convenient Shopping Automated Experience

Online shopping is more convenient than ever as websites personalize real-time suggestions to consumers based on a customer’s shopping history. As a result of this investment, Kinsta predicts that by 2021, Artificial Intelligence in e-commerce will increase sales to $4.5 billion from $2.3 billion in 2017.

3. Enhanced Healthcare Experience

NovitaCare, a Netherlands based healthcare company that treats patients with chronic and multiple disorders, wanted to improve the caregiver experience using an effective online platform. 

With Fingent’s help, NovitaCare now can communicate with non-profit organizations, patients, providers and researchers with an online platform that is HIPPA compliant.  

4. Simplified Management Of Supply Chain

The supply chain industry has experienced challenges in managing unexpected events that happen due to inaccurate forecasting. A solution to the problem is implementing AI and IoT. 

Supply Chain Digital recently stated the following about these technologies:

“Intel highlights that the world of IoT is growing rapidly, from 2 billion objects in 2006 to a projected 200 billion by 2020.” 

“AI is on most companies’ radars, with 78% of organizations implementing it to enhance operational efficiency by at least 10%.”

The use of real-time devices will feed data to executives to help create contingency plans for preventing unexpected challenges in the industry. As a result, the supply chain and a company’s reputation can experience fewer impacts.

A Guide for AI-Enhancing Your Existing Business Application

How Fingent Helps Businesses Achieve Success With AI and IoT?

Fingent has mastered the art of technology infrastructure to help companies resolve AI and IoT processes. As a result, it creates efficiencies in managing smart devices.

Implementing these technologies are small changes that can have a huge impact on your business. The ability to use raw data to understand customer behavior and forecast trends in the market can improve customer loyalty. Also, companies can track employees working in multiple departments and locations across the globe by partnering with Fingent.

Fingent is confident that AI and IoT work in your business context by delivering technologies to enable solutions in the cloud, networks and gateways, heterogeneous device support, systems capabilities, and data analytics. 

To Conclude 

Business Insider predicts that there “will be more than 64 billion IoT devices by 2025, up from about 10 billion in 2018.” 

Gartner observes that in three years (by 2020), more than 80 percent of enterprise IoT projects will incorporate at least one AI component. Artificial Intelligence and the Internet of Things is used to improve the safety of drivers on the road, enhance healthcare experiences, automate and streamline enterprise processes, stop intruders from hacking into IT systems or large organizations, and in numerous other ways. 

The combination of these technologies not only delivers a superior customer experience, but also forecasts what customers want in real-time, improves their experience of living in smart cities, maintains a high safety rating in challenging workplaces, and reinforces physical and cybersecurity. AI-IoT duo also avoids any unplanned downtime, increases operating efficiency, helps develop new products and services, and improves your risk management. 

Are you looking for an AI and IoT partner? Get in touch with Fingent experts today for a streamlined and error-free IoT implementation for your business.

Top 6 Reasons Why Implementing LMS Is Crucial for Your Business

With the rise of technology implementation in various business processes, the LMS market is forecasted to reach USD 22.4 billion by the year 2023. This figure was estimated to be USD 9.2 billion in the year 2018. With the increasing rates as shown in these figures, it is evident that businesses leverage a multitude of benefits from implementing a Learning Management System. 

The major drivers towards the increased adoption of LMS in businesses include digital learning, enterprise mobility, BYOD (Bring Your Own Device) policy, Artificial Intelligence technology implementation, Machine Learning, and so on.

What Is An LMS?

A Learning Management System is a software application that offers online training, educational content, and several crucial strategies for implementing LMS into a system. A quick example of an LMS is the SaaS (Software as a Service), which is a kind of web-based (internet-based) LMS. 

SCORM (Sharable Content Object Reference Model) and LTI (Learning Tools Interoperability) are two strategies via which content is integrated into an LMS, which is included in the LMS application. Let us walk through the key benefits that drive businesses in adopting a Learning Management System.

LMS of “Present” and Ways to Enhance It

Why Do Businesses Require An LMS Implementation

An LMS can help a business, streamline its procedures and improve the overall efficiency of the workforce. To sustain growth in businesses, it is crucial that industries employ an LMS into their processes. Let us walk through the major compelling reasons why it is necessary to implement an LMS for a positive business outcome:

1. Flexibility In Accessing Information

With LMS, employees can access information anytime, anywhere via their desktop, laptop or smartphone. Critical decisions can be made through the instant availability of data. With the advent of modern LMS platforms, centralized information can be easily accessed. The data accessed will be stored digitally, such as user profiles, training progress, and so on. This not only makes the data searchable but also reduces the time spent to retrieve the required information. 

2. Cost-effectiveness

An LMS platform can cut down costs associated with training expenses. Training and onboarding generally involve hiring multiple resources. For instance, a hiring manager has to train every newly joined employee in the company software and other implications. But with an efficient LMS implemented, the training can be customized. 

Additionally, providing online training can significantly reduce time and costs. Implementing a centralized location for training can avoid the need for sending employees to get trained in far off places. Since the data can be reused and accessed whenever required, it eliminates the need for excessive paper documentation.

3. Improved Productivity And Profitability

According to a recent study by ASTD (American Society For Training And Development) that was conducted on 2500 firms, the firms that invested in training had achieved a 24% higher margin than the rest. The study also found out that these firms had a 218% increased income per employee. 

LMS ensures that the employees get a thorough training that can help them perform productively and efficiently. An LMS also ensures that multiple users are trained simultaneously at the same pace. When employees are productive, they become profitable as well.  

An LMS can ensure the quality of the training provided to the employees via its data and analytics tracking. This includes factors such as the time duration of the training provided, how well performed is the training, and so on. The centralized training database of an LMS enhances the quality of training provided as well as significantly improves the productivity and profitability of the business. 

4. Effective Employee Onboarding

Employee orientation or employee onboarding is a tedious process in many companies. When the employee onboarding process has a modern LMS platform implemented, it significantly reduces the employee churn rate. This, in turn, increases the productivity of the employees. 

Third-party content can be easily deployed via an LMS platform. LMS works by enabling businesses to deliver, manage as well as track the hiring and training process of new employees. Being able to create courses, setting tests and assignments, and automating the process of onboarding are some of the major functionalities. 

With an LMS onboarding ecosystem, an effective and efficient training and onboarding process are ensured.

5. Measuring ROI

According to industry analysts, the LMS market is expected to grow from today’s figure of $2.06 billion to over $7 billion by the year 2023. A Learning Management System is designed and deployed to deliver increased ROI to businesses in a multitude of ways. 

The major return is in being able to replace traditional one-to-one training. This is a key cost saver. LMS is utilized as a centralized hub for housing large volumes of training as well as other content. LMS improves business outcomes by reducing travel expenses of employees sent for training externally and slashing down employee productivity losses. 

An LMS calculates resources allocated and identifies existing inefficiencies in training. It also lets employees focus on the core parts of their job. This significantly reduces employee turnover as well. 

6. Knowledge Retention With The LMS Centralized Hub

To drive innovation, it is crucial that the employees within an organization are intellectually capable. Technical know-how enhances the productivity of employees. Intellectual capital is thus one of the key benefits leveraged from an LMS. 

LMS ensures that the information does not remain consolidated in a single location and that it is shared with all the resources. With an LMS, employee performance can be tracked easily. The employees who underperform can be provided with additional personalized training and retained.

To become an LMS expert and to identify the current inefficiencies or areas of improvement in your business, talk to our LMS strategists and software development experts today!

Most Common Mistakes To Avoid While Implementing IoT

There are many pressing concerns about the possibilities of IoT in businesses. The most common is probably the question, “Is my business too small to adopt IoT practices?” However, as per the available statistics, the global IoT market is expected to reach $1.7T by the end of 2019.  It is surely not unnoticed that IoT implementation has helped businesses both big and small to drive growth and innovation.  

Making key errors while implementing IoT can however cause the entire business system to halt. These issues can be those related to device management, data flow across the organization, various partnerships involved, and so on. Security, scalability, cost involved, and the complexity of the system are other key factors.

Let us walk through the most common mistakes made while adopting IoT.

1. Security Concerns Associated With Technology Implementation

More than 80% of the senior executives in industries across the globe suggest that IoT implementation is crucial for positive business outcomes. Since more and more devices are connected to the global network, the highly sensitive data and applications require access restrictions to avoid any malpractices. For instance, the security scope needs to be end-to-end to support connected devices. 

An IoT implemented framework needs to be secure. The security concerns could be any of the following:

• An Insecure Web Interface
• Improper Authorization Techniques
• Privacy Issues
• Cloud Interface Insecurity 
• Insecurity In The Mobile Interface
• Insecurity In-Network Services
• Software Or Firmware Issues
• Lack Of Physical Security
• Lack Of Transport Encryption
• Issues In the Security Configuration

Poorly secured IoT devices and software make the IoT prone to cyber-attacks. End-to-end security is thus crucial for any IoT deployment. For instance, consider an Internet-connected car wash. Such devices use a default password. In this case, when a security concern arises, it also leads to a safety concern. 

The solution here is an external security audit of the implemented IoT device. This builds confidence to perform new IoT implementations as well. 

2. Not Being Aware Of The Critical Data Flow Forecasts

Not being able to forecast data volume can be one of the major mistakes in IoT implementation of devices and applications. According to EMC Research, the rate at which data is growing is exponential. It states that the volume of this data would be equivalent to 6.6 stacks of 128gb i-Pads which are fully-loaded, and will stretch from the Earth to the Moon!

Moreover, many businesses think that the more data they extract, the better it is for their business. Many a time, this misconception can lead to storage swelling of both structured as well as unstructured data. 

The solution is to ensure the proper working of the right IoT big data business strategy with a clear forecast on different factors. The factors could be the amount of network traffic, storage requirements, and so on. 

In case of an already existing functional data system, edge computing can be implemented to ensure intelligent pre-processing of data. 

Internet Of Things – Letting Industries Go Digital

3. Cost Factors Involved In Decision-Making Of IoT Implementation

According to recent statistics, cost savings have turned out to be the major IoT adoption criteria for over 54% of enterprises. Taking into account just the cost factor while deciding to implement IoT might turn out to be another major mistake. Various factors affect the cost of implementing IoT projects. Starting from the number of connections to the device, the type of technology used, to the type and features of the application to be loaded, there are many more. 

Hardware, let us say, is a major factor that affects the cost of IoT implementation. The cost of the IoT application is directly proportional to the number of devices used in the connection. Likewise, Infrastructure is another major factor that influences the cost of IoT projects. The infrastructure used could be wireless, middleware, or cloud-based. 

4. Lack Of Proper Plans For Device Updates And Replacements

A proper IoT device management is critical to ensure core compatibility of the IoT platform. Device reliability is the most important requirements to ensure an enterprise-ready platform. Device management operations include network, power states, device geolocation, and so on. 

Large volumes of data collection, transfer, storage, and utilization can result in malfunctioning of connected devices in the IoT ecosystem. Implementing an IoT platform enhances the integrity of connected devices. 

The solution to the pressing concern of planning can be solved through regular monitoring, diagnostics, software updates, and maintenance. Performing frequent OTA (Over-The-Air) updates helps the IoT platform in monitoring and maintaining the device software, fixing bugs, managing firmware, and in customizing the connected devices. This ensures in-depth device protection. 

Related Reading: Check out more about IoT – Where and Why should you invest!

In addition to the above-mentioned common mistakes, the following are a few other factors that can lead to IoT errors:

•  Lack of setting a realistic timeline for IoT implementation – Achieving a realistic idea on the timeline of IoT implementation is necessary for a positive outcome. 
• No Tolerance For Possible Failures – Implementing IoT without having a clear picture of your IoT project can be a big mistake. Leave room for scaling up new ideas. 
• Relying Only On Existing Charts – IoT implementation requires dedicated decision-makers instead of relying only on existing organizational charts and decisions.
• Lack Of Technical Expertise – When every part of the IoT project is either reinvented or being contracted out, you are unsure of the third-party development and deployment teams. Technical expertise is the key to a successful IoT project.

Are you looking for an efficient technology partner to help you adopt IoT the best possible way? Get in touch with our custom software development experts today for a streamlined and error-free IoT implementation for your business.

What Is Robotic Process Automation?

Robotic Process Automation is the process of applying automation to perform tedious business tasks of the workforce, such as data manipulation, response triggering, transaction processing, and other redundant tasks. According to a recent study by Snaplogic, 90% of the workforce are burdened with redundant tasks. This not only reduces their productivity but also consumes significant amounts of time with which they could perform higher-value tasks.

The Role Of RPA: Features That Enhance Business Process

Once your enterprise has decided to implement RPA, it is time for you to choose the right robotic process automation solution. 

Traditional RPA software bots are known to handle only a specific task at a given time. When it comes to addressing high volumes, there is a necessity to clone these bots and run them simultaneously. RPA providers usually charge users for each concurrent process. This can become a costly affair for enterprises, especially during volume spikes. Thus, undue extra costs are a key factor to consider while choosing an RPA solution for your business. 

RPA works as a virtual assistant and can handle complex processes starting from performing complicated calculations, data capturing to maintaining records. 

In addition to prioritized work queues, user-friendly features, data analytics, and non-disruptive nature, the following are crucial features that enhance business processes:

• Non-disruptive nature:  An enterprise can easily implement RPA into their workflows without having to disrupt or change the existing structure or risks.
• Data analytics: Gathering critical data from multiple sources, analyzing and storing the data, and creating reports have brought digital transformation to businesses with RPA. This enables accurate forecasts of sales data along with other Key Performance Indicators (KPIs). 
• Prioritization of Internal Work Queues: Every RPA software consists of internal work queues. These work queues are used to extract data derived from various transactions for analysis. The extracted data is then stored on a cloud server and made available for access by the bots.
• User-friendliness: Employees can operate on the robots without any extra RPA knowledge. They only need to learn how the systems work. 
• Scalability: With RPA, it is possible to upscale and downscale various robotic operations. 

Related Reading: Learn more about how Robotic Process Automation is revolutionizing industries.

Types Of Robotic Process Automation Tools

RPA enhances robotic performance in different ways. The three major categories include Working Robots that are commonly used for Data Collection and Project Planning. Monitoring Robots detect faults and breakdowns, whereas Screen Scraping Robots provide data migration tasks for enterprises.

Robotic Process Automation tools come in varying sizes and shapes. Analyzing your business objectives is the most critical factor before deciding to choose a specific RPA tool for your business. A few of the major RPA tools are as follows:

• Attended Or Robotic Desktop Automation Tools

This type of automation always starts with the user via the user’s desktop. The user first launches the RPA code to perform required operations rather than waiting for the workforce to perform. 

• Unattended Automation Tools

This type of automation completes business processes in the background and is used mainly to perform back-end tasks.

• Hybrid Automation Tools

This type of automation combines both attended and unattended automation tools to perform start to end operations.

Related Reading: You might like to read more about ways to empower RPA for enhanced business growth.

How To Choose The Right RPA For Your Business

A clear set of objectives form the primary goal before opting a specific RPA tool for your business. The following are the key factors you need to consider before selecting an RPA tool for your business:

1. Easy-to-use Interface

Simple user experience is a major criterion for choosing the right RPA tool for your business processes. A simple user interface will ensure all employees work efficiently. 

2. Proper Deployment

An RPA tool that can be quickly deployed with the existing technology stack is what is required. 

3. Cost

Replacing tedious tasks performed by the human workforce is largely replaced by the bots. This process of automation saves costs. Employees can focus on their core tasks and spend time and effort on their skills rather than performing redundant and tedious tasks with the help of RPA tools. Purchasing an RPA software tool involves associated costs, such as cost of individual licenses, cost of the software, and other overheads.

4. Scalability

Implementing an effective RPA tool enhances the business processes and leads to the growth of the enterprise. This growth is accompanied by hiring more resources. Thus an RPA tool can enhance the scalability of a business in the long run.

5. Security

Data analytics, compliance, and financial transactions require a highly secure environment. A great RPA software tool ensures a secure solution for all business processes and updates as well.

6. Architecture

The architecture of the RPA depends on where you plan on employing your RPA tool. The deployment and maintenance of an RPA tool depend on factors such as layered design, component reusability, robust delivery, popular language support system, easy accessibility, and so on.

7. Features

Choosing an RPA suite that consists of solid inbuilt features is critical. Flexibility, scope, availability of wizards and GUIs, other extendable commands and supports are some of the features to consider. 

8. Exception Handling Support

A robust RPA solution can detect errors during automation and automatically resolve without human assistance. In other cases where human intervention is required, an effective RPA tool must be able to send error messages.

9. Extended Support

Different vendors offer different support. A dedicated support team is necessary to ensure strong maintenance and support. 

To make the best decision on choosing the right RPA solution for your business and access the full potential of RPA tools, get in touch with our experts today!

What Are Cobots?

Cobots have been around since the 1990s. Cobots operate in conjunction with humans to perform given tasks. They are built to interact physically with humans in a shared workspace. 

In other words, cobots or collaborative robots can be defined as the hardware version of Augmented Intelligence. Rather than replacing humans with their autonomous counterparts, collaborative robots augment artificial intelligence technologies to physical bots. According to Barclays, cobots can revolutionize production. This is effective, especially for smaller companies, that account for 70% of the manufacturing industries, globally.

Cobots help in improving human capabilities in performing tasks with greater strength, accuracy, and data capabilities. The first collaborative robot was a device used to directly interact physically with a manipulator that was computer-controlled’. It was invented by J Edward Colgate and Michael Peshkin in the year 1996. Later, Kuka Robotics launched its first collaborative robot in the year 2004, named LBR 3. 

How Are Cobots Useful In Various Industries?

Since Cobots are capable of performing tasks alongside humans instead of replacing them, there is a multitude of ways in which Cobots are used in different industries for varying purposes. The major ones are as follows:

1. Hand–guiding: This cobot has an additional hand – a pressure-sensitive device at the end of its arm. With this arm, the human operator can teach the collaborative robot how to hold an object or how to move, or how fast to move, and so on. It also ensures that nothing gets damaged.

2. Speed and Separation Monitoring Cobots: This particular cobot operates in safety zones. Instead of stopping to perform its task when it senses an outsider’s presence in the safety zone, this cobot slows down and then tracks the location of the human. It stops as the human gets too close.

3. Power And Force Limiting Cobots: These collaborative robots are designed to frequently interact with humans. This specific cobot stops or reverses its movement on encountering any abnormality.

4. Safety Monitored Stop Cobots: These cobots are designed to work independently, but stop whenever a human needs to intervene. This cobot senses human presence and stops all movement until the human has left the safety zone. 

Related Read: Check out how Robotic Process Automation Is Revolutionizing Industries

How Collaborative Robots Offer Game-Changing Benefits

Cobots have been brewing in the web-space for quite some time. In general, robots have replaced human labor from the industrial workforce since the industrial revolution. Robots, but operated within safe environments. Cobots, on the other hand, help in putting away some of the major spatial and environmental dangers that robots may cause, whilst working alongside humans!

Cobots can also be easily reprogrammed. Many businesses and industries are skeptical about falling behind their competitors, especially in situations where irrecoverable disasters are likely to take place. Here is when cobots can be an effective solution. Let us walk through the major benefits that collaborative robots offer in various industries:

1. Increased and efficient Human-Robot Interaction

In any given industry utilizing bots for performing tedious tasks, time, cost, and floor space are the three major critical factors to be considered. This is because the operator can work alongside the cobot, without having to leave the workspace. Cobots are known to reduce idle time of human workforce by 85%. 

Consider a traditional assembly line that is set up in a workspace. Here the human workforce sets up the mechanical robots with required parts to perform the rest of the tasks. The entire production will be put to halt for a long time or stopped from its current operations, in case of any required human intervention. While on the other hand, a collaborative robot works along with the human workforce, which increases the efficiency significantly.  

2. Applicable To Small And Mid-Sized Industries

Strategies to optimize costs are a major concern in every industry. This is because larger industries that have a higher production volume prefer a robot to perform tedious tasks. On the other hand, smaller industries prefer manual labor. 

Implementing cobots can be beneficial across a range of industry sizes, as these bots do not require a heavy set up process. 

3. Safety In Handling Dangerous And Tedious Tasks

Preventing human error is critical in every industry. For instance, steadying the movement of tools used in surgeries is complex. The process needs to be highly accurate as well. Cobots ensure a safer working environment by preventing human errors that can hinder operation accuracies.

4. Increased ROI

Cobots can be conveniently relocated whenever required. This makes it easy to eliminate any non-productive activities during working hours. In addition to being highly efficient and flexible, cobots ensure increased ROI due to significantly reduced labor and maintenance costs. This also results in an increased profit margin as well. 

Related Reading: Read on to learn how you can accelerate your business growth with Robotic Process Automation. 

Future Of Cobots

By the year 2020, cobot sales are expected to cross $3.1 billion. The sales of cobots are increasing every year. According to Barclays Equity Research, analysts state that the global sales of cobots have crossed US$120 million in the year 2015. This figure is forecasted to grow to $12 billion by the year 2025!

The figures above illustrate that the sales of cobots are just 5% of the total robot market. This figure is forecasted to grow exponentially as more industries start to explore the multitude of possibilities of these bots. 

To know more about how cobots can be leveraged for your business, drop a call to our strategists right away!