Category: Digital Transformation

Understanding the concept and significance of Deep Reinforcement Learning

The field of reinforcement learning has exploded in recent years with the success of supervised deep learning continuing to pile up. People are now using deep neural nets to learn how to use intelligent behavior in complex dynamic environments. Deep reinforcement learning is one of the most exciting fields in artificial intelligence where we combine the power of deep neural networks to comprehend the world with the ability to act on that understanding.    

In deep learning, we take samples of data and supervise the way we compress and code the data representation in a manner that you can reason about. Deep reinforcement learning is when we take this power and apply it to a world where sequential decisions are to be made. 

We use deep reinforcement learning to solve tasks where an agent or an intelligent system has to make a sequence of decisions that directly affect the world around the agent. While trial-and-error is the fundamental process by which reinforcement learning agents learn, they do use neural networks to represent the world.  

Read more: Key Differences Between Machine Learning And Deep Learning Algorithms

Types of learning

All types of machine learning– supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning are supervised by a loss function. Even in unsupervised learning, there is some kind of human intervention required to determine and provide inputs on what is good or bad. Only the cost of human labor required to obtain this supervision is low. Thus, the challenges and the exciting opportunities of reinforcement learning lie in how we get that supervision in the most efficient way possible.  

In supervised learning, you take a bunch of data samples and use them to learn patterns to interpret similar samples in the future. However, in reinforcement learning, you teach an agent through experience. So the essential design step in reinforcement learning is to provide the environment in which the agent has to experience and gain rewards. In other words, a designer has to design not only the algorithm but also the environment where the agent is trying to solve a task. 

The most difficult element in reinforcement learning is the reward – good vs bad. For example, when a baby learns to walk, success is the ability to walk across the room and failure is the inability to do so. Simple! Well, this is reinforcement learning in humans. How we learn from so few examples through trial-and-error is a mystery. It could be the hardware – 230 million years of bipedal movement data that is genetically encoded in us or it could be the ability to learn quickly through the few minutes or hours or years of observing other humans walking. So the idea is if there was no one around to observe, we would never be able to walk. Another possible explanation is the algorithm that our brain uses to learn which has not yet been understood. 

The promise of deep learning is that it converts raw data into meaningful representations whereas the promise of deep reinforcement learning is that it builds an agent that uses this representation to achieve success in the environment.     

Deep Q learning

Q-learning is a simple and powerful algorithm that helps an agent to take action without the need for a policy. Depending on the current state, it finds the best action on a trial-and-error basis. While this works for practical purposes, once the problem size starts increasing, maintaining a Q-value table becomes infeasible considering the amount of memory and time that would be required. This is where neural networks come in. 

From a given input of action and state, a neural network approximates the Q-value function. Basically, you feed the initial state into the neural network to get the Q-value of all possible actions as the output. This neural network is called Deep Q-Network. However, DQN is not without challenges. The input and output undergo frequent changes in reinforcement learning with progress in exploration. The concepts of experience replay and target network help control these changes.

Read more: Top 10 Machine Learning Algorithms in 2020

Deep Reinforcement Learning Frameworks

Here are the three Deep Reinforcement Learning frameworks:

1. Tensorflow reinforcement learning

RL algorithms can be used to solve tasks where automation is required. However actual implementation is easier said than done. You can ease your pain by using TF-Agents, a flexible library for TensorFlow to build reinforcement learning models. TF-Agents makes it easy to use reinforced learning for TensorFlow. TF-Agents enables newbies to learn RL using Colabs, documentation, and examples as well as researchers who want to build new RL algorithms. TF-Agents is built on top of TensorFlow 2.0. It uses TF-Eagers to make development and debugging a lot easier, tf.keras to define networks and tf.function to make things faster. It is modular and extensible helping you to pick only those pieces that you need and extend them as required. It is also compatible with TensorFlow 1.14.

2. Keras reinforcement learning

Keras is a free, open-source, neural network Python library that implements modern deep reinforcement learning algorithms. Using Keras, you can easily assess and dabble with different algorithms as it works with OpenAI Gym out of the box. Keras offers APIs that are easy and consistent, thus reducing the cognitive load. These APIs can handle the building of models, defining of layers or implementation of multiple input and output models. Keras is fast to deploy, easy to learn, and supports multiple backends. 

3. PyTorch Reinforcement learning

PyTorch is an open-source machine learning library for Python based on Torch and is used for applications such as natural language processing. It consists of a low-level API that focuses on array expressions. This framework is mostly used for academic research and deep learning applications that require optimized custom expressions. The PyTorch framework has a high processing speed with complex architecture.  

All these frameworks have gained immense popularity and you can choose the one that suits your requirements.

While deep reinforcement learning holds immense potential for development in various fields, it is vital to focus on AI safety research as well. This is going to be fundamental in the coming years in order to tackle threats like autonomous weapons and mass surveillance. We should, therefore, ensure that there are no monopolies that can enforce their power with the malignant use of AI. So international laws need to keep up with the rapid progress in technology. 

We have tried to brush across the basics of deep reinforcement learning and the top 3 frameworks that are in use currently. Want to know more about this amazing technology? Reach out to us at Fingent! 

Why your business needs to adopt headless CMS architecture

70% of companies are actively investing in content marketing and almost 60% of marketers rate content marketing as extremely important or very important to their marketing strategy, states HubSpot. Modern customer behavior is driving up the demand for a more flexible, customizable, and scalable CMS that is adept to deliver the experience your customers expect. When compared to traditional CMS, Headless CMS enables organizations to speed up delivery times while iterating quicker. This blog walks you through seven specific business benefits of headless CMS. Let’s begin by understanding what headless CMS is.

What is meant by Headless CMS?

A headless CMS allows us to edit CMS and database without an integrated presentation layer. The integrated presentation layer, which is referred to as a ‘head’, restricts the use of content only to one particular channel such as a website. Once CMS is severed from the head, it could be used across various other platforms such as a mobile, tablet, and smart devices, making it ideal for the current business scenario.

Read more: 5 Convincing Reasons To Adopt The Headless CMS Sanity.io

7 Business Benefits of Adopting a Headless CMS

1. More flexible

Since headless CMS is API driven, it allows you to build your own head or a presentation layer/ frontend. Besides enjoying the ability to pick your programming language, your developers can develop the website without having to conform to any proprietary development constraints. A single piece of content can be reused or combined with various other presentation outputs enabling faster project completions. 

A headless CMS allows secure and easy integration with any of your existing business systems. Additionally, since it does not have a fixed structure to code, your developers are at liberty to code for any type of integration. This gives them the flexibility to integrate with more complex systems. 

For example, Sanity.io is a popular headless CMS that allows you to embed editable data in running text and cache multiple queries on a single request. It also provides real-time collaboration, content versioning, and live previewing.

2. Supports Omnichannel Selling

For marketers to provide a customer-pleasing experience, each channel used by the business would require access to the current product information and availability. It can be quite a challenge to create iconic content that shines across all touchpoints. Instead, a headless CMS provides the capability to orchestrate a seamless experience across all touchpoints while maintaining consistency and relevance. For instance, Sitefinity empowers brands to deliver a personalized experience across channels.

3. Headless CMS is Future-Proof

A headless CMS enables businesses to future-proof their applications by separating the presentation layer from the data and logic layer. You can structure your content to facilitate future-proofing for new projects. Also, you would not be required to make any technical changes when re-branding one or more channels. Sitecore is a leading headless CMS that offers enterprise-class search and content targeting to boost personalization efforts, among other things.

4. Cost-Effective

It is a lot cheaper for your business team to create a new functionality because headless CMS requires little technical involvement. For example, if your marketing department chooses to create a new series of product mini-sites, they do not have to depend on developers to build CMS-based templates. Instead, the marketing team can directly go to the CMS and start creating the mini-sites as and when required, reducing your up-front costs. Kentico CMS, for instance, comes with tailored custom pricing. Websites of popular brands like Sony and Starbucks are powered by Kentico. 

Read more: Top 6 Tech Stacks That Reign Software Development in 2020

5. Offers Better Software Architecture

A headless CMS is architected to decouple CMS platforms and published content. This strengthens security because access to the CMS is restructured internally within the organization. It increases scalability simply by spinning up a new app server and pointing it to the content. It remains available against all odds because even when the CMS application goes offline, web applications will not have an impact. Episerver, the leading WCM platform supports editors to drag-and-drop content to create new digital experiences quickly.

6. Allows you to do more with less

Organizations will no longer need large teams of specialists with particular CMS knowledge, unlike the requirements for a traditional CMS.

7. Lets You Focus on Your Business

Worrying about your CMS can be time-consuming and distracting. A traditional CMS structure can take your attention away from growing your business. Whereas Headless CMS allows you to use your precious time and resources to grow your business.  Being a multi-tenant system, it is fully managed and upgraded for you. 

Read more: Top 5 benefits of outsourcing software development services

A Step Forward 

Apart from these, there are several other reasons why businesses must consider a headless CMS. The important aspect to consider is how you want to manage and store content for products and articles. This can have an impact on websites, application performance, and conversions. Hence, as marketers, it’s time to take a step beyond traditional CMS.

If you’re considering a headless CMS to improve your digital content experience, send us a message immediately. 

What makes Odoo the best ERP solution for your business?

As business leaders, you may find that you’re spending too much time running your company rather than growing it. You may easily get bogged down due to exhaustive paperwork, long spreadsheets, or have to assemble discrete software to actually work together. This is where the open-source Odoo ERP comes to your rescue. With its vast suite comprising over 40 business productivity applications, Odoo provides a smoother and simpler way to run your business. The range of business apps offered by Odoo is highly comprehensive, fully-integrated, easy to use, and supports numerous different industries. Fingent, utilizing its partnership with Odoo ensures their clients are leveraging one or more apps from Odoo to boost their business efficiencies. 

With Odoo, you can easily create your professional website, manage your relationship with customers, design and launch your own marketing strategies, and manage online payments through e-commerce. You also have Odoo apps to manage your warehouse, accounts, and invoicing behind the scenes. 

Whatever be your business needs, Odoo provides apps that work together seamlessly. Though a relatively young contender, Odoo comes packed with powerful ERP features and a user-friendly interface like any other market leaders such as SAP, Oracle, MS Dynamics, and so on. 

Read more: Odoo CRM Vs SAP CRM: How to Choose Between Them

Here are 5 distinguishing features of Odoo that make it a reliable ERP for any business.

1. Easy to set up and use

Odoo has a very clean, consistent, easy-to-use interface. It is also easy to set up and configure. You can effortlessly carry out an entire transaction all the way from a quotation to the sales order, invoice, and payment due to the consistency between the various applications within Odoo. It helps you to get started within no time.

2. Powerful communication tools

Odoo has powerful inbuilt communication tools. They use messaging systems to keep track of internal communications on either your sales orders, invoices, or purchasing. So if you have a question on your sales order, you can write a message to all the followers of the sales order at the bottom of the documents. Thus, you can attach notes and messages and communicate directly from within the document giving you a great audit trail. All the team members stay on the same page both literally and figuratively!

Read more: Meeting Your HR Requirements with Odoo

3. Integrated front-end and back-end tools

You can have a full-blown website builder with a single click. So, once you install the web platform, you can use it integrated with all the other Odoo applications. Odoo provides powerful e-commerce tools that integrate with your website and are completely tied-in to the web framework. Moreover, the shopping cart looks clean, is built-in to the e-commerce app, and is ready-to-go. Another interesting feature of Odoo is that even if a customer didn’t make it to checkout and has just added items to his cart, you can view it as a draft in Quotations instead of a full Sales Order.

4. Great mobile support

Though Odoo is completely web-based, it looks pretty good on mobile phones and tablets. You even get a mobile preview to check how your website is going to look like. So, unlike a lot of legacy accounting systems that are still trying to catch up, Odoo was founded as a three-tier mobile application platform and it has continued to improve.

5. Powerful enterprise and cloud solutions

Odoo has very powerful enterprise and cloud solutions. You can host it yourself or on Amazon, or on digital cloud. You can also use Odoo.com or Odoo’s own hosted solution. Thus, there are a lot of different ways to host it as Odoo is very cloud friendly. Fingent extends support for deploying Odoo on web servers like AWS and other secure environments.    

Other factors that make Odoo unique

Odoo is highly customizable, completely open-source, utilizes Industry Standard Libraries, and is written in Python. The backend database is PostgreSQL which is also open source. Another important aspect is that you can build custom Odoo Applications without modifying the source code. You can easily upgrade or extend without breaking things.

Read more: A 3-day Odoo CRM implementation story!

Conclusion

The COVID-19 pandemic has taught us that going digital is no longer an option, but a necessity. Choosing the right ERP that suits your business requirements and unlocks the returns on your investment can help you get there. The modular framework has given Odoo a wide functional scope. While using Odoo, enterprises do not need to resort to 3rd party platforms or use additional bridging software to build fully integrated websites. Thus, Odoo supports the uniqueness of each business. This ability to customize every aspect of your system gives you a competitive advantage. And all this can be done at minimal cost, compared to the other ERP systems available in the market. As enterprises have to look at the eventual ROI before implementing an ERP system, Odoo shines because of its functionality, scalability, and affordability. Fingent’s partnership with Odoo ensures your projects are tailored for easy adaptability. Get in touch with us to plan the next dependable Odoo application that will assist in your daily business operations.

Challenges Your Business Should Overcome in the Post-COVID Phase

• Introduction
• Challenges in Reopening Strategy
• Challenges of Shifting Customer Habits
• Challenge of Operations and Employee Safety
• Impact on Operations for Manufacturing Units
• Challenges of Finance and Banking
• Tax, Trade & Regulatory Challenges
• Crisis Management
• Focus on tomorrow

How Sanity.io manifests the value of headless CMS

In our recent white paper, we stated that customer experience will overtake price and product as the key brand differentiator in 2020. We also discussed how the rapid evolution of both customer expectations and CX technologies is a wakeup call for both CX and IT leaders.

So, that’s it! Any business failing to deliver omnichannel customer experiences or remaining inactive across their website, mobile application, voice assistant, email, social media profiles, or online customer support is sure to face peril. This has set in a new level of expectation as to how digital content should be managed. Definitely, this puts the onus on companies to adopt a headless content management system (CMS). 

Now, the question is what’s wrong with the traditional CMS? If WordPress, Drupal, or Joomla allows you to have both the CMS and the website’s design in one place, wouldn’t it be easier to make updates and manage your content seamlessly? Then why did leading brands like Cornerstone, Cloudflare, and Eurostar move to Sanity, one of the top headless CMS platforms? 

Here, we tell you what’s a headless CMS and why many industry leaders have adopted Sanity.io. 

The Headless CMS Movement 

Remember the “pre-CMS era” where you had to update HTML pages manually and upload them on the website via FTP and perform a lot more steps? The birth of popular content management systems like Drupal and Joomla not only gave us relief, also did they tempt us with convenience. However, these legacy platforms often force us to solicit assistance from developers working on a specific CMS and require us to spend more time, resources, and budget for maintenance and enhancements. 

A traditional CMS ideally fits an enterprise-level small business website or a personal website, especially if you do not have to share content with multiple digital devices or platforms. With cross-channel content dissemination at the speed of light becoming the Holy Grail, the monolith, single storage feature offered by traditional CMS is easily giving way to headless CMS.   

Read more: Top 6 Tech Stacks That Reign Software Development in 2020

Decoding Headless CMS 

In simple terms, a headless CMS is a content-first CMS where the content repository (body) is decoupled/separated from the presentation layer (head). It’s a back-end only content management system that makes content accessible to external clients for display through APIs. 

The result: Even a non-developer can create or edit content without getting worried about how the content will be displayed or consumed by the external systems. If you need to publish content on multiple platforms all at once, headless CMS is the best choice. 

The beauty: Headless CMS is front-end agnostic. You can choose the framework or tool you like for displaying content to the end-user. It allows front-end developers to solely focus on the presentation layer without thinking about how the content will be managed. 

5 reasons to go for Sanity, the popular headless CMS

“Build with structured content” is the byword of Sanity. However, the platform makes no assumptions about how your content is structured, created, validated, and presented. This offers you the flexibility to deliver structured content into any digital devices or applications via Sanity’s real-time, cloud-hosted APIs and customizable open-source editing environment.

1. Get started in no time

You can quickly get started with Sanity.io by downloading CLI from npm and use it to launch a new project. Alternatively, you can go to Sanity’s starter projects that will help you get started in minutes with its preconfigured Sanity Studio and a functional front-end with a range of frameworks to choose from, all deployed to Netlify with source code on GitHub.

2. Superior editing features

Sanity’s editor or the Sanity Studio is a flexible, open-source application that allows you to define content models with simple JavaScript. A single-page app built with React.js, Sanity Studio allows you to customize or extend it using your own React.js components. Its advanced features help you modify workflows for your editors. Along with customization, Sanity Studio offers core features like Block Content, Structure Builder, and a Dashboard plugin.

3. Exceptional APIs

The primary reason for choosing a headless CMS is its API-first pattern that allows you to access the content through APIs.

Sanity offers two powerful APIs for reading, writing, querying, and patching documents:

• api.sanity.io which is the live uncached API
• apicdn.sanity.io which is the CDN-distributed, cached API

Sanity also supports deploying GROQ and GraphQL APIs to query your content. The platform’s Data Store resides in the cloud and can be accessed via Sanity API either using Sanity’s client libraries or through the HTTP API directly.

4. Matured technology stack

Sanity is a cloud-hosted CMS with a real-time content studio and hence all the data is synced instantly. The underneath architecture involves mature technologies such as  PostgreSQL, ElasticSearch, and JavaScript, and the blazing-fast React. It doesn’t save HTML, XML, or rich text in the database, but in rational object structures. For instance, if you want Alexa to read from your text fields, then you don’t have to parse HTML. Sanity comes packed with its well-maintained JavaScript, HTML, and PHP clients allowing you to be up and running fast with the front-end framework of your choice.

5. User-friendly headless content models

Though you require someone with basic JavaScript knowledge to get started with Sanity, it isn’t hard to find a person who is familiar with the popular web programming language. Sanity enables content editors, visual and interaction designers, and technology professionals to collaborate on building the information architecture. Front-end developers can save their efforts by accessing content fields instantly through APIs.  

Need more reasons to fall for headless CMS? 

To give you an example, Sanity prevents you from getting “locked-in”. Despite being a SaaS platform, Sanity allows you to easily export your content and use it wherever you need. Same with the front-end frameworks that we saw above. Customized editorial experiences, structured content approach, minimal hidden costs, pay-as-you-go pricing model, and a number of stunning features will make you say “Yes” to the headless CMS model.

If you would like to know more about Sanity.io or any other headless CMS platform that you’re currently considering, drop us a line immediately.

Data Lake vs Data Warehouse

A data lake is a location where new data can enter without any hurdles. Since any kind of data can reside in a data lake, it is a great source to unearth new ideas and experiment with data. However, due to this openness, it suffers from a lack of meaningful structure. The larger business audience may find that the data lake is a mess. This is where the scalability traits of the data warehouse gain significance. In data warehousing, we try to match dimensions and measures into queryable components that are consistent. This makes it easier for an ever-scalable audience to consume this data. 

Let us now take a deep dive and compare the properties of a data lake and a data warehouse.

Read more: How Data Warehousing Adds Value To Data Visualization & Reporting

7 key differences between data lake and data warehouse

1. Type of Operation:

• A data warehouse is used for Online Analytical Processing (OLAP). This includes running reports, aggregating queries, performing analysis, and creating models such as the OLAP model based on whatever you want to do. These operations are carried out typically after your transactions are done. For example, you want to check all the transactions done by a particular client. Since the data is stored in a denormalized format, you can easily fetch the data from a single table and showcase the required report.
• A data lake is used typically to perform raw data analysis. All the raw data i.e XML files, images, pdf, etc. are just gathered for further analysis. While capturing data, you don’t have to define the schema. You may not know how this data can be used in the future. You are free to perform different types of analytics to uncover valuable insights.

2. Cost of storing data:

• In data warehouses, the cost of data storage is high. This is because the software used by these data warehouses are expensive. Additionally, the cost of maintenance is also high since it consists of power, cooling, space, and telecommunications. Another point to consider is that since a data warehouse contains large amounts of data in a denormalized format, it tends to take up a lot of disk space.
• Contrarily, in data lakes the cost of data storage is low. They use open-source software which costs less. Also since the data is unstructured, data lakes can scale to high volumes of data at low cost. 

3. Schema:

• Data warehouses use schema-on-write. Before storing the data, it has to be transformed and provided for application in analytics and reporting. You need to know for what purpose you’ll be using the data prior to importing it into the data warehouse. As new requirements arise, you may have to reevaluate the models that were defined earlier.
• On the other hand, data lakes employ schema-on-read. Without the necessity of a single schema, users can store any kind of data in the data lake. They can discover the schema later while reading the data. This means different teams can store their data in the same place without relying on the IT departments to write ETL jobs and query the data.

4. Data Quality:

• A data warehouse contains high-quality data. As the data undergoes extreme curation before storage, it can be considered as the central version of the truth. 
• A data lake contains raw data that may or may not be curated.

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

5. Users:

• Typically business professionals who deal with reporting use data warehouses. Again, since the operation costs of a data warehouse tend to be higher, large and established organizations that deal with tons of data opt for it. 
• Data scientists and analysts generally use data lakes. With raw data the possibilities are endless. They can perform various types of analytics to glean insights and identify patterns to convert the data at hand into valuable information. 

6. Security:

• Data warehouses tend to store extremely sensitive data for reporting purposes. These could be compensation data, credit card information, healthcare data, and so on. The data security for data warehouses is mature and robust since this technology has been around for quite a while now. Only authorized personnel can access the data warehouse.  
• Data Lake is a relatively new technology and hence data security is still evolving. As mentioned, a data lake is created using open source technologies. Therefore its data security is not as great as that of a data warehouse.

7. Technology:

• Data warehouse applications use relational database technologies. This is because relational database technologies support quick queries against structured data.
• The Hadoop ecosystem is well-aligned to the data lake approach because of its agility. It can easily scale to large volumes and can handle any structure of data. 

Read more: Power BI or Tableau: Which is the better choice for your business

How both data lake and data warehouse can go hand in hand

Both data lake and data warehouse are the principal constituents of modern data architecture. A data lake usually serves as the starting point from where organization-wide data is onboarded. It is also the stage at which the data warehouse structures its data. An organization that incorporates both data lake and data warehouse will exhibit the traits of entrepreneurship and diligence, which means the organization will be both open-minded and scalable. 

The BI industry has tools that cater to highly unstructured data lakes that enable open-minded discovery. Also, there are tools that are designed to scale as a structured information delivery platform concurrently with your data warehouse. Though these tools oppose one another, they have very little in common. They are purpose-built according to the needs of an organization. So before choosing a tool you need to determine which one would be right for your needs and help your organization grow. Contact us now for more information!    

Transforming Businesses with RPA- Leading Use Cases in HR and Banking

Various organizations use RPA tools to automate simple to complex tasks and perform them with minimal or no human intervention. 

From an IT perspective, you tend to bucket all RPA uses cases into data integration or testing. However, from a business perspective, you need to find out how to get a better time to value and how to overcome obstacles that hinder the business value. Then you can determine use cases that fit into this characterization. 

For example, you want to roll out a change in your business process, and need integration into another system. You can do that in two ways:

• either through APIs and get into the IT changed management routine, 
• or by using RPA to drive interfaces without an API and get the change rolled out in weeks instead of months. 

So, time to value is the calculation that businesses need to do, and check whether the change is worth it. 

Read more: What Makes a Business Process Apt for Automation

Suppose you have to perform tasks that are very repetitive in nature – like filling in excel forms, web forms, things like visual basic or word with data which you already have access to, or which you need to aggregate from various systems. Here you can have an RPA bot to pull that data or even push out that data to multiple systems. You won’t have to rekey that information manually. You can always use an RPA bot to do that in an automated fashion. In both these cases, you can write integrations or you can have a system do it for you. 

RPA gives you a way to configure that behavior rather than write a code for it. In other words, RPA use cases need to be data-intensive, rule-driven, and repetitive. The drivers almost always tend to be time to value, time to market, and so on.

Now that you’ve understood where to use RPA in your business, let’s have a look at some of the use cases. 

RPA Use Cases in HR 

According to UiPath, 40% of your HR professionals’ time can be reclaimed using RPA. Robotic Process Automation can be combined with your existing HR systems like SAP or Workday that allows you to create digital process automation with ease. Here are the two key HR areas where automation leads to transformation.

1. Payroll: 

Payroll operations consist of a large number of repetitive, rule-based tasks with activities like data collection, calculations, and scheduling tasks. Payroll workers have to collect data from various departments or units in different formats. The next step is data validation and entering that information into other applications. All these tasks are prone to error. 

These activities can be automated using RPA technology since all the data that payroll staff deals with is structured. RPA can make payroll more organized without using expensive software. 

The benefits of RPA in payroll are improved accuracy, lower costs due to reduced manual labor and data security. Since the number of menial, time-consuming tasks performed by employees is reduced, they can focus on tasks with higher strategic value.

2. Onboarding and offboarding:  

Every time you get a new employee, the candidate’s details have to be uploaded to all systems that you use. They may need a Windows account, access to your time reporting tool, email addresses, IT equipment, and so on. If someone from the HR team manually enters all this data they would be stuck in mundane tasks. Instead, you can have a script doing these repetitive tasks. With RPA, you can automate the entire onboarding procedure since the process is the same for every new employee. 

Employee exits too, have to be managed consistently. Manual processing makes these tasks error-prone and may raise auditory concerns. If RPA is implemented in this case, the bot analyzes the incident to find out which tasks need to be executed. It notifies the IT team to terminate access and recover the equipment, terminates the employee from the HCM, revokes system access, generates exit documents, and processes final payments. 

Read more: Jaw-dropping Facts about Robotic Process Automation

RPA Use Cases in Banking

A slow economy and rising customer expectations have caused banks to look for cost optimization methods. The back-end processing activities in the banking sector consist of tasks that are rule-driven, repetitive, labor-intensive, and high in volume. RPA technology can help to automate these processes, thus eliminating the need for human intervention. Here are the two major banking functions that can be automated for improved results.

1. Loan application processing: 

The processing of loan applications is a tedious process. For document verification, employees need to manually verify different documents and associated information and then organize all data into a single file. Very often, employees get stuck in this task and spend too much time on it. RPA employed in this procedure can automate the whole process by opening different web portals and validating the information. The bot then initiates an email to the employee for a final decision. Thus, the bot helps to save valuable time and improves the time to client response.

2. Account opening: 

The account opening process is cumbersome, time-consuming, and prone to errors. RPA can help speed up this process and make it more accurate. Bots draw out information from forms and enter it into separate host applications. Thus RPA eliminates errors and improves the quality of data in the system.

Read more: How Robotic Process Automation Simplifies Business Operations

RPA tools have the potential to help various industries improve efficiency, drive faster operations, and reduce costs than most automation techniques. RPA is gaining popularity as enterprises try to counter competition, increase productivity, and meet customer expectations. Early adopters of RPA have reaped its benefits and its high time that you did too. Get in touch with our experts to learn more about how RPA can simplify your business operations. 

6 Hot Technologies that Handhold Businesses Amid COVID-19 Impact

The COVID-19 pandemic has had wide-ranging ramifications for several businesses. Forrester predicts that the retail sector will endure a 2.1 trillion-dollar loss in 2020 due to COVID-19. It also said that it will take four years for retailers to experience the growth seen before the pandemic. As COVID-19 continues breathing threats down the neck of businessmen, hot technologies are emerging as a relief to counteract them and lead businesses towards their goals. We will discuss a few of these specific technologies such as cloud, eCommerce, eLearning, automation, virtual collaboration, and contactless services that can help you minimize the effects of COVID-19 on your business. 

Read More: Fingent’s Response to COVID-19 Business Implications

1. Cloud Adoption During COVID-19

With physical interaction no longer being an acceptable form of communication, organizations and institutions have had to swiftly shift to digital solutions to retain productivity. The domino effect of COVID-19 was seen in various sectors, accelerating the adoption of facilities for seamless remote work. Cloud computing has emerged as an essential technology for critical application and scalability of infrastructure in this regard. 

Companies from various sectors are now starting to realize the benefits and value of cloud computing as far-reaching beyond the scenario created by the pandemic. As a result, businesses will have to scale up their digital transformation efforts and invest in cloud resources without delay. If anyone had reservations about investing in cloud computing before this, COVID-19 has proved that its necessity is indisputable. 

Read More: 5 Trends That Will Transform Cloud Computing in 2020

2. E-Commerce

CCInsights reported that as of 21 April 2020, US and Canadian e-commerce orders have seen a 129% increase.

With restrictions on the number of people that can be gathered in one place, gone are the glory days of shopping malls and brick and mortar stores. COVID-19 has changed shopping behaviors overnight, necessitating brands to adapt and be flexible to meet changing needs. 

For example, the Buy Online Pick up In Store (BOPIS) capability has become vital to maintaining sales volume with the restrictions in mind. A good example of this is the mobile phone industry. When foot traffic is curbed, then Mobile Point of Sale programs can be set up to take orders and payment at the same time for business continuity. Membership or Loyalty cards can be now digitalized through mobile applications. 

Read More: How a Smart Product Ordering System Helps Retailers and Wholesalers

3. Virtual Collaboration

Many developed nations are now stipulating that employees of non-essential businesses work remotely for an indefinite time, making video conferencing vital.  Schools, colleges, and universities are also leveraging video conferencing platforms through live or recorded lectures. 

This has brought many virtual collaboration solutions to the forefront that facilitate video conferencing, instant messaging, task and calendar management, work collaboration, file sharing, attendance tracking, and so on. A few examples include Zoom, DingTalk, WeChat Work, Zoho Remotely, and so on. 

At Fingent, we use InfinCE, a powerful cloud-based enterprise collaboration software that offers support for remote work. 

Read Our Case Study: How Fingent enabled a smarter digital workplace solution for Sony Mobile

4. E-Learning 

During this time of crisis, the entire education ecosystem is coming together to ensure that students do not suffer. Educational applications, platforms, and resources offer functionalities across multiple categories such as:

• Resources to provide psycho-social support
• Digital learning management systems
• Digital systems designed for use on basic mobile phones
• Massive open online course platforms 
• Self-directed learning content
• Mobile reading applications
• Tools for teachers to create digital learning content

Read More: E-Learning Taking A New Front: How Can LMS Technology Help

5. Automation

Automation has been helping businesses mitigate disruption by enabling them to stay connected across teams and systems while maintaining customer support in times of uncertainties such as this pandemic. 

Robotic Process Automation improves the efficiency and reliability of work outcomes and automates the time-consuming, repetitive tasks that weigh down intelligent workers. The benefits are:

• Digital workers do not need to have the weekend off. They can work 24 hours a day, 24/7 to respond to spikes in business activity. 
• They do not have travel restrictions nor are they at the risk of COVID-19 infection or affected by physical office closure. 

During the pandemic, companies that have already invested in automation technologies are doing exponentially better than those who did not. It is obvious, that automation can pave the way for a better future.

Read More: How Automation Ensures Businesses Stay Afloat During COVID-19 Crisis

6. Contactless Services

The coronavirus pandemic has driven a preference for self-service purchasing, boosting contactless services. Consider a few examples available now and upcoming in the future: 

• Dining experience: Technology can take care of everything: reserving a table at the restaurant, pre-ordering your food, digital valet services, contactless seating, contactless payment, and online feedback.
• Contactless payment: It lets shoppers integrate their payment information to their loyalty account through an app and then use a QR code for payment through self-checkouts. 
• Contactless delivery: This ensures end-to-end hygiene because a customer places an order, makes the payment online, and gets the food delivered without ever coming in contact with the delivery agent. 

Read More: Contactless Services: The New Normal in Retail

Grab a Slice of Hot Technology

While the end of the pandemic remains elusive, capturing even a slice of these hot technologies could make a huge difference to businesses. They can even help smaller businesses gain a stronger foothold during this pandemic and into the future. Get in touch with us and help us guide you through this pandemic by implementing the right technology solutions for your business. 

The Need for Digitalization

Digital technologies have penetrated into every aspect of our lives, transforming the way we seek and receive information. For instance, today, we search for products and services on search engines rather than in yellow page directories and other offline media. We communicate our experiences with other people through chats, email, blogs, or social media posts. In other words, the media we use, the content we consume and share, the customers we engage with – all benefit from digitalization and digitized data.

Read More: A Road Map To Digital Transformation in 2020

Why is digitalization inevitable for businesses and how can organizations benefit from it?

Before we discuss the advantages of digitalization, we need to understand the difference between digitization and digitalization. According to Gartner’s IT Glossary, “digitalization is the use of digital technologies to change a business model and provide new revenue and value-producing opportunities, whereas “digitization is the process of changing from analog to digital form.”

Digitalization is already influencing the way we do business

Digitalization already has had an impact on our business – from the way we acquire and retain customers to the way we present our business and manage our reputation. While in the past, brick-and-mortar stores were sufficient to establish your business, now consumers want to know what services or products you offer before deciding to make the purchase. 

Today, businesses have to be in close proximity with their customers to find out about their opinions and improve customer experiences. When you know more about your customers, you have more data at hand. Digitalization helps apply this data to make better business decisions. 

With digital technologies, you have more tools that make work easier. This can lead to increased productivity and reduced costs. Digital tools such as dashboards and collaboration tools such as messengers and video chats help align your employees with business goals and improve internal communication. 

What does digitalization mean to you?

Digitalization does not just mean implementing various technologies into daily business. You need to also rethink your business and operating models to implement the technology. Technology is just the tip of the iceberg. Together with it, you need to understand your digital maturity as an organization. Technology does enable digital engagement, but you need to assess the big picture of what digitalization means to your business – whether you are a public organization, a small private company or a global player. To gain control over customer relationships, you need to develop end-to-end strategies to reach customers. 

Top 3 opportunities for digitalization

1. Converting excel sheets to dashboards

In any organization, we find an opportunity gap between the growth of the company on a revenue basis and the growth of the support staff. This part of the company needs to focus on improving automation and scalability to better support the rest of the company. 

Read More: How Automation Ensures Businesses Stay Afloat During COVID-19 Crisis

Most of us still enter information in excel spreadsheets. For example, if we track our projects (work in progress), contracts, savings, etc. in separate spreadsheets, we’re only entering the same data many times. Converting to dashboards can help you quickly view and analyze your entire data in one place. 

Along with consolidated views, dashboards provide opportunities for business intelligence by allowing users to display only what’s required through the use of filters. Once you start adding insights and recommended actions along with the summary, the dashboard becomes more useful by closing any communication gaps between various departments in your company. In short, you save time, provide clear communication, and drive business goals. 

2. Document Management systems

The advent of digitalization has opened up new avenues of data, making its’ management complex. The traditional file and paper methods have become archaic as well as cumbersome. Document management systems have proved themselves indispensable for businesses of all sizes. 

With digitalization dominating the trend in business, enterprises are looking for new ways to streamline documentation by using various automation techniques. The rise of cloud-based document management systems has simplified the creation and sharing of digital documents for enterprises. 

Read More: Top 6 Reasons Why You Should Move to a Cloud-Hosted ERP

Managing huge volumes of data has also become relatively easy. Today you have document management systems that vary in scope from simple systems that cater to small enterprises to more sophisticated ones that cater to large global enterprises. Document management systems reduce physical storage, enable quick access of documents, promote security, make maintenance and customization easy, and reduce monotonous tasks.

3. OCR Technology

Another technology that helps with the digitalization of businesses is OCR. The ability of Optical Character Recognition (OCR) software to automatically extract data from an image file or scanned document has helped businesses to streamline their operations. It reduces the time required in manual data entry and extraction. A robust and accurate OCR can extract data from multiple document formats. Thus, it saves time in data collection, reduces human effort, and aligns business processes with customer needs. AI-powered OCR eliminates manual entry, thereby reducing errors and improving productivity. Businesses that are equipped with AI-powered OCR technology can stay afloat in the digital wave that has swept across the world.

Digitalization can drive recovery from COVID-19

As the coronavirus pandemic continues to take a toll on people’s lives, it has also stimulated change in the social, personal, economic, and corporate forefronts. The focus has now shifted from growth, business development, and digitalization to just riding out the storm, that is, ensuring business continuity. However, organizations should not lose sight of the long-term effects of the crisis. Companies would have to rethink their business models according to changing customer demands. This crisis has forced organizations to invest in their digitalization strategies to establish sustainability. 

Read More: Business Process Re-engineering: Facing Crisis with Confidence

Companies need to direct their digitalization strategies towards increasing resilience and optimization. Rather than just focusing on increasing productivity, a sustainable and comprehensive digitalization strategy should focus on maintaining productivity during future challenges. Write to us to know more. 

What is Exploratory Data Analysis?

Exploratory Data Analysis (EDA) is a statistical approach used to analyze data and produce descriptive and graphical summaries. Analysts may or may not use a statistical model, but EDA primarily foresees what the data can reveal to us beyond formal modeling. 

With EDA you can analyze your data as it is, without the need to make any assumptions. EDA further validates and expands the practice of using graphical methods to explore data. EDA gains insights from statistical theories that give easily decipherable insights. Exploratory data analysis techniques can also be used to derive clues from data sets that are unsuitable for formal statistical analysis.   

Exploratory Data Analysis displays data in such a way that puts your pattern recognizing capabilities to full use. The patterns are evident to an examination that is careful, direct, and most importantly assumption-free. Thus, you can understand relationships among variables, identify problems such as data entry errors, detect the basic data structure, test assumptions, and gain new insights.  

Purpose of Exploratory Data Analysis

The prime purpose of EDA is to study a dataset without making any assumptions. This helps the data analyst to authenticate any assumptions made in devising the problem or operating a particular algorithm. Researchers and analysts can, therefore, recommend new schemes that were not previously considered. 

In other words, you apply inductive reasoning to obtain results. These results may be in opposition to the theories that directed the initial data collection process. Thus, EDA becomes the driver of transformation. This approach allows you to oppose planned analyses and probe assumptions. The ensuing formal analysis can continue with better credibility. EDA techniques have the potential to uncover further information that may open new areas for research.        

Role of EDA in Data Science

We need to understand the role of EDA in the whole process of data science. Once you have all the data, it has to be processed and cleaned before performing EDA. However, after EDA, we may have to repeat the processing and cleaning of data. The cleaned data and results obtained from this iteration are further used for reporting. Thus, using EDA, data scientists can rest assured that the future results would be logical, rightly explained, and relevant to the expected business circumstances. 

EDA helps to clean the feature variables that are to be used for machine learning. Once data scientists get familiarized with the data sets, they may have to go back to feature engineering since the early features may be unable to serve the objective anymore. After completion of the EDA, data scientists obtain a feature set that is required for machine learning. Each dataset is generally explored using multiple techniques. 

Read More: Top 10 Must-Know Machine Learning Algorithms in 2020

Methods of Exploratory Data Analysis

Exploratory data analysis is carried out using methods like:

• Univariate Visualization – This is a simple type of analysis where the data analyzed consists of a single variable. Univariate analysis is mainly used to report the data and trace patterns.
• Bivariate visualization – This type of analysis is used to determine the relationships between two variables and the significance of these relationships.
• Multivariate visualization – When the data sets are more complex, multivariate analysis is used to trace relationships between different fields. It reduces Type I errors. It is, however, unsuitable for small data sets. 
• Dimensionality Reduction – This analysis helps to deduce which parameters contribute to the maximum variation in results and enables fast processing by reducing the volume of data. 

Using these methods, a data scientist can grasp the problem at hand and select appropriate models to corroborate the generated data. After studying the distribution of the data, you can check if there’s and missing data and find ways to cope with it. 

Then comes the outliers. What are your outliers and how are they affecting your model? 

It’s always better to take small steps at a time. So you need to check if you can remove some features and still get the same results. More often than not, companies just venturing into the world of data science and machine learning find that they have a lot of data. But they have no clue how to use that data to generate business value. EDA techniques empower you to ask the right questions. Only specific and defined questions can lead you to the right answers. 

Exploratory Data Analysis: Example with Python

Read More: Why you should migrate to Python 3

 Suppose you have to find the sales trend for an online retailer. 

Your data set consists of features like customer ID, invoice number, stock code, description, quantity, unit price, country, and so on. Before starting, you can do your data preprocessing, that is, checking the outliers, missing values, etc. 

At this point, you can add new features. Suppose you want the total amount. You multiply quantity and unit price to get this feature. Depending on the business requirement, you can choose which features to add. Moving on, by grouping the countries and quantity or total amount together, you can find out which countries have maximum and minimum sales. Using Matplotlib, seaborn, or pandas data frame you can visually display this data. Next, by grouping the year and total amount, you can find out the sales trend for the given number of years. You can also do the same for each month and find you out which time of the year has shown a spike or drop in sales. Using this same method, you can identify further problems and find out ways to fix them. 

Read More: How to Use Data Analytics to Improve Enterprise Sales Numbers

The key to exploratory data analysis is to first understand the LOB and get a good hang of the data to get the desired answers. Get in touch with us to know more about EDA.