Capturing Data to Facilitate Learning

With the ongoing use of Conversational AI, currently, customers have multiple channels of engagement to connect with a brand, thereby ensuring the generation of enormous volumes of unstructured data every moment through a multitude of platforms, including:

• Social Media – Facebook, Messenger, Twitter and other Engagement Channels
• Web & Mobile apps – Web and App for Sales and Service
• Voice Devices – Google Home, Amazon Alexa, Siri etc.
• Contact Centre – Dial in for Contact Centre Support

Leveraging Data to Derive Learning: Transforming this unstructured data into valuable and actionable business insights is a tedious process. This, especially without the right tools and platforms in place, becomes an impossible task. Therefore, the use of various text / speech analytics tools such as LivePerson, Verint, Clarabrigde, Nice, Lexalytics, etc. helps in analyzing the conversations, spot keywords, build rules, identify real-time customer sentiments, categorise conversations into positive or negative type, and analyse customer feedback and surveys. These tools are best leveraged combined with business intelligence experts, thus helping the companies derive actionable insights to improve:

• Customer Satisfaction
• Customer Service
• Operational Efficiencies
• Brand Campaigns
• Provide Personalised Offers, and
• More Insightful Customer Behaviours.

Social Media is a giant where we see comments and trolls; airline companies among many others receive millions of mentions each day. These mentions/comments range from traveler enthusiasts posting photos of clouds outside their windows to angry customers complaining about the service. Social media analytics (text analytics) helps the airline industry to gain real-time insights.

Amongst all comments/mentions around airline companies, the U.S based airlines are the ones that are usually discussed and have many mentions, however, they are mostly are negative. People complain about delayed flights, missing baggage, inflight & overall services. Winning this battle against the top competitors of the brand, while providing the best customer service can be challenging. Social media analytics, therefore makes the job easier.

Real-Time Feedback: Text/Speech analytics helps by alerting the management about all online events/talks. Passengers are online and are constantly talking about their experiences on various platforms. This data helps the team to analyze the information & dive deep into action with an emergency response arrangement. This is exactly how Southwest Airlines is using their social media listening command centre to overcome any crisis situation and deliver excellent customer service

Spotting trends: Like every other industry, the Airline industry also needs a loyal customer base and a strong online reputation. However, managing the business while keeping up with the constant online chatter and trends is tricky. Currently, the airline industry witnesses such patterns and trends.

For instance, looking at the current pandemic scenario and listening to the chatter online, there has been a lot of concern among the passengers with regards to safety measures/arrangements. In response to this trend, Delta Airlines has been announcing about safety protocols quite often over SMS, Emails, Chat & Voice, and Social media platforms.

The use of relevant and trending content is the most attractive tool to improve visibility of a brand. That’s how the giants in airline industry are making a difference and creating their niche

Handling the Crisis or Negative Feedback: Of all the chatter around in the airline industry, a predominant section is filled with negative comments. In the current digital age, one negative comment can cause a ripple effect to the brand’s image, this in-turn can cause considerable damage to the business. Especially with people keep complaining about poor service quality, boarding facilities, baggage queries etc.

Today, passengers also use third-party apps or agencies to book their travel plans & flights. Therefore, it becomes of utmost importance to please the customers through these third-party apps. Speech, text and voice analytics teams analyze the data generated through these channels as well for their business improvement insights.

Conclusion

This digital age has made the world more connected than ever: it has brought customers and companies closer. Every other company across the industries is using digital platforms along with Text Analytics, Speech Analytics, Social Media Analytics to stay relevant, stay connected, and offer maximum value to their customers and build brand image. Customers expect the same from the airline industry. Writing an update on Twitter about a delayed flight or upgraded safety measures goes a long way in building a trustworthy relationship with the customer. Additionally, Conversational Analytics helps the airline industry to stay focused on every important consumer insight in the most effective way possible.

Looking at the immense scope of speech & text analytics in the Airline industry, IGT has partnered with Nice – Nexidia to quantify the business problems and offer apt solutions.  Nexidia is an interaction analytics software that offers a one-stop solution to organize, analyze and transform the unstructured data from various data pools / databases into actionable insights for a brand. IGT is fully equipped with the right tools and business intelligence resources to provide intelligent insights to our clients. Amongst many projects, IGT is currently working with one of the leading American travel companies by providing a quantified and calculated statistical approach to pursue their business goals. Data mining & structuring, customer verbatim analysis, customer sentiment evaluation, root cause analysis of a business problem to improve customer satisfaction (CSAT) & metric driven operational efficiencies and inferential insights are few areas of IGT’s expertise.

Author:

Apurva Sale is a Lead Business Consultant at IGT Solutions’ Intelligent Automation and Conversational Analytics Practice. She is an Analytics expert with 8 years of experience across Travel, Telecom and Retail domain. With a strong background of Business & Data Analytics, Apurva has extensively worked on elevating customer experience with Insights & sentiment analysis, Social Media Analysis & Insights. She can be reached at Apurva.sale@igtsolutions.azurewebsites.net

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Continuous refinement of technology has redefined and enabled the user to harness various benefits of digital transformation, on the other hand, it has also provided the same advancement to a hacker who has been sharpening hacking attempts and malware attacks.

In the year 2022, it has become prudent for individuals and companies to be well prepared for tackling ever increasing cyber threats and continue to fine-tune and upgrade security strategies and benchmarks.

One of the solutions is to timely invest in security testing but it would be important to know about the trends to expect this year.

Artificial Intelligence: The key use cases of artificial intelligence are seen in the fraud detection on financial portals and intrusion detection systems. It is very useful in analyzing the data and finding unusual patterns of cyber-attacks on the systems. Artificial intelligence by its core is capable of analyzing humongous amount of data from network traffic to assess possibility of cyber threat on the system.

Given the fact that hackers are trying to utilize machine learning methodologies to automate and implement malicious events in organizations, it is prudent for the organizations to enhance their cyber defense systems by harnessing the power of artificial intelligence.

Ransomware Threat: The year 2021 had seen a spike in ransomware attacks. The UK National Cyber Security Centre reported the number of ransomware attacks increased by three folds in the first quarter of 2021 compared to the whole of 2019. The various reasons attributed to these spikes are due to the increase in financial activities on digital platforms due to the pandemic, enhanced usage of e-commerce and enough controls not in place for employees working from home.

Many companies and governments are still using outdated technologies, processes, protocols, and procedures, which ultimately increases the vulnerability of getting attacked by ransomware. We have to fix these issues with proper budgeting and management alignment, in addition to other measures such as improved security, better monitoring and reporting.

Cloud Security: With the popularity of cloud implementation and moving organizational sensitive data to the cloud, it becomes very important to secure cloud infrastructure. Cloud though is very secure and has several layers of security from user management, network management, to secure key management. It has its own challenges in terms of security, as any security issues on cloud hosted applications will impact the authenticity, integrity and availability of the data.

One trend of year 2022 would be a possible growth in terms of cloud adaptation, it will also mark for newer ways of cyber-attacks on the cloud infrastructure.

Cybersecurity Talent Shortage: Cybersecurity talent is scarce in the market and there is no doubt about it.  The increased number of cyber-attacks in the year 2021 has further fueled the demand for trained cybersecurity professionals across the globe. It is unlikely that this gap will be filled in a timely manner, respite may come from artificial intelligence, where it will be used to detect malware in the network by analyzing a vast amount of data more quickly than humans, detecting issues such as phishing attacks, privilege escalations, and insider threats.

Internet of Things: Statista, a market research company, projects global spending of 1.1 Trillion USD by 2023 and consumer spending on smart home systems worldwide in the tune of 123 bn USD by 2021. The risk of cyberthreat is going to increase in the year 2022 with more proliferation of IoT devices. As we all are aware of the high vulnerability of IoT devices owing to different communication protocols, operating systems, integrations and lack of any standardization in space.

Conclusion

IGT with its Security Testing COE is well positioned to achieve shift-left of security testing and thereby finding security vulnerabilities earlier in the lifecycle, and working with all stakeholders to ensure these vulnerabilities are taken care of before releasing the software on production. IGT cyber assurance covers OWASP, PCI-DSS, HIPPA, ISO 27001 and other standards of cyber security.

Author:

Yatender has 20+ years of experience in software test engineering. As the head of Testing Practice at IGT Solutions, Yatender is actively involved in innovations related to test engineering covering new tools, technologies, and solutions, and enabling IGT’s clients to achieve faster time to market quality improvement, and optimization of developer efforts in overall SDLC. A result-oriented leader, proficient in delivering high customer value and achieving excellence in service delivery management with proven skills in consulting and managing large and complex test programs. When away from work, he enjoys reading on a variety of topics and spending time with kids.

The post Top Cyber Security Trends to look out for in 2022 appeared first on IGT Solutions.

What are No-Code and Low-Code platforms?

These are the development environments that are used to rapidly develop and deploy custom applications by minimizing coding and using drag & drop for pre-build libraries and components for creating user interfaces, business logic, and data services.

 Main challenges in the traditional approach of application development:

• Key technology skills are in shortage: There is a shortage of skills in key technologies these days, making it difficult to deliver the application development project on time, with the agreed quality and scope.
• Time to market is the key: In today’s hyper-demanding market, the expected delivery time is in days and weeks and not in These kinds of timelines are not feasible in the legacy ways of working.
• Technological complexity: The technology is changing very rapidly and staying on top of all related areas of GUI, interfaces, security, scalability is an ardent task and quite time-consuming.
• Business innovations: By the time the technical team develops and is ready with an application, there is already a demand for new changes.

These challenges of traditional approach on software development are the key enablers of no-code- and low-code platforms as these very challenges are resolved with a new approach.

How to finalize low-code and no-code platforms for your development team?

Usually we need to perform comparative assessments of available platforms with respect to the organization’s budget, business needs and team composition. Gartner’s magic quadrants are a good starting point. We can  get information on vendors from other industry forums and publications as well.

Gartner’s Magic Quadrant for Enterprise Low-Code Application platforms:

As per Gartner, the low-code development technologies space is expected to reach $29 billion in revenue by 2025 (with a compound annual growth rate of more than 20%).

Key reasons for the rapid growth of low code platforms are:

• Business users get the opportunity to develop their use cases and features and dependence on core techies is reduced
• Automation and hyper-automation initiatives are very well supported by low-code applications, as pre-built components are available to integrate with other 3^rd party APIs and systems
• It provides greater composability of application services, functionality and capabilities, which enable teams to implement applications across different technologies

Key features of low-code applications:

A low-code platform can be used to create applications with the below features:

Low-code and no-code development in the Travel and Transportation industry:

Travel and transportation organizations can develop user–facing and self-services applications for customer registration, flight booking, baggage claim, cargo management, payment and loyalty programs using low-code and no-code platforms.

The Travel and Transportation applications have longer development cycles and have various interfaces with payment gateways, loyalty management systems, booking engines, reservation systems, and industry bodies such as IATA, HFTP, HTNG, GSTC and local Government authorities. The use of low-code and no-code platforms can help create these applications faster, and help achieve scalability, maintainability, and security.

The built-in libraries and components in low-code and no-code applications make it easier to exchange data among various functions of internal and external 3rd party systems, thereby enabling the development of the internet of things (IoT), artificial intelligence (AI) and machine learning (ML) applications.

Conclusion

IGT has partnered with various no-code and low-code platforms, we offer end-to-end digital solutions and services to Travel and Transportation customers. IGT’s no-code application digital assurance services employ an in-house developed digital assurance framework with pre-built use cases and are faster, and easier to deploy, manage, and optimize ensuring a higher ROI for our customers.

Author:

Yatender has 20+ years of experience in software test engineering. As the head of Testing Practice at IGT Solutions, Yatender is actively involved in innovations related to test engineering covering new tools, technologies, and solutions, and enabling IGT’s clients to achieve faster time to market quality improvement, and optimization of developer efforts in overall SDLC. A result-oriented leader, proficient in delivering high customer value and achieving excellence in service delivery management with proven skills in consulting and managing large and complex test programs. When away from work, he enjoys reading on a variety of topics and spending time with kids.

Sources: Gartner

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The proliferation of Agile and DevOps adaptation in order to improve efficiency, agility, customer experience, and profitability is the need of the hour to remain relevant and thrive in the current market dynamics.

Faster, Faster, and Faster is the mantra of digital strategies which leads to faster development, faster deployment, and faster delivery. This means shorter testing cycles while still covering all the risk areas.

The existing ways of performing quality assurance of the products are no longer adequate in today’s time, where software applications are supposed to be available anywhere, on any device, on every screen size, on every browser, and provide amazing customer experience to its users.

Sharpen Digital Assurance further with AI

We all know that finding and fixing a defect in the software comes at an undesirable cost which we all want to minimize. Having said that, if defects remain undetected and are passed onto the production stage, it might cause a severe impact on the project delivery cost, not to mention a dent in brand image leading to customer attrition. The problem at hand is to optimize the testing process to achieve a balance of risk mitigation and cost/efforts/timeline incurred.

Tremendous improvements have been seen in the software quality assurance domain in the last decade, such as automation testing, shift-left, lean testing, and so on. At the same time, the application complexity, devices supported, and speed of delivery have also increased manifold. The gap has become wider between the current state and target state in the software quality assurance domain.

The Key approach here will be the utilization of AI/ML in software testing. The AI/ML based algorithm will train the model to predict the areas of maximum risk so efforts are optimally aligned to achieve maximum ROI.

The AI / ML model training itself is tricky, as it needs a large volume of data to train the model on, and any error in data if not identified at right time, would amplify the outcome of AI thus doing more wrong than good. Careful calibration of the model and availability of correct test data is going to be useful to enable AI in the Testing process.

The To Be state is when the model will provide a probability of defects in the area of the code, so automation testing (static and dynamic) is focused on a specific area, thus helping achieve results in a shorter time.

Why Digital Assurance function is best suited for AI modelling

Everyone is fascinated by the results promised by Artificial Intelligence (AI) and there is a lot of buzz in the media too. Let’s go a bit deeper and take a look at the four fundamental elements of AI:

Categorization: Categorization involves creating metrics specific to the problem, for example:

• There is a huge rework cost of defect identification, fixing, and revalidation post-deployment to production. Example metric: rework cost is 20% of overall project delivery cost
• Customers are finding it difficult to use the application and finding the right section on the browser / WebApp; this is driving the customer to move to a competitor product. Example metric: Customer Satisfaction surveys mention 3.1 (which is lower) on a scale of 1 to 5
• The delivery cycle of the organization is 4 weeks while several companies within the same domain/market have started adopting a two-week delivery cycle, thus having the advantage of delivering faster to market. Example metric: SDLC cycle takes 4 weeks from planning to production deployment.

Quality Assurance: Quality Assurance provides the data needed to create foundations of AI/ML applications, such as historical defect data categorized into modules, impacted code area, releases, developers, type of issues, etc.  QA also provides details on test cases executed/pass/fail, first-pass rate, etc. It helps join the dots and create problem statements.

Classification: Once a problem is categorized into various areas, the next step is to identify classifiers for each category to direct the user for analysis and conclusion. For example, in the airline travel domain, if the problem identified has to do with making a booking, the team needs to start classifying the possible causes of the problem: Web Application, Mobile App, Authentication, Authorization, Calendar, Pricing, Payment, and Reservation Factors and so on

Machine Learning: Now the problem is categorized and classified for domain-specific terms, the team can start feeding this data to machine learning.  There are various algorithms and techniques broadly divided into supervised learning and unsupervised learning. Supervised machine learning with neural networks is becoming popular. Few other applications of machine learning are feature discovery, event correlation, and time series anomaly detection.

As the quality assurance function is generating a huge volume of data such as test cases, code reviews, defects data, test execution data, etc. it is pertinent to use this huge volume of data related to code quality, testing results etc. in order to train the model.

Collaborative Filtering: It is used to sort through large volumes of data and starts using AI based solutions. This helps in turning data collection and analysis into meaningful insight or action.

Challenges of using AI/ML into Quality Assurance

The key requirements for an AI system are:

• Enormous sets of data
• Validity of testing data collected from various source
• Integrity of data

The challenge is with the availability of a large amount of verifiable data. If there are outliers in test data, those should be taken care of while massaging.

Another challenge is the non-availability of a continuous stream of data, as most testing is done on a discrete basis. In such a scenario, it would be difficult to find patterns in the QA data of one release and other releases.

Since there are various attributes involved in training ML models, and QA data of different types of industry/programs may have different outcomes, it becomes a bit difficult to have a single ML model for all projects.

Conclusion

Future belongs to AI/ML and we should be ready to embrace the changes. At the same time, we also need to ensure authenticity, integrity, and availability of correct data. If the above-mentioned challenges are taken care of, AI can be very beneficial if used along with digital assurance to work and deliver solutions, thereby achieving predictive threat modeling. This will provide benefits such as shorter delivery cycles, improved risk management, and cost optimization.

Author:

Yatender has 20+ years of experience in software test engineering. As the head of Testing Practice at IGT Solutions, Yatender is actively involved in innovations related to test engineering covering new tools, technologies, and solutions, and enabling IGT’s clients to achieve faster time to market quality improvement, and optimization of developer efforts in overall SDLC. A result-oriented leader, proficient in delivering high customer value and achieving excellence in service delivery management with proven skills in consulting and managing large and complex test programs. When away from work, he enjoys reading on a variety of topics and spending time with kids.

The post Deliver Insightful Results with AI and Digital Assurance appeared first on IGT Solutions.

The Business Intelligence analysis technology has been present since the 1980s when the ‘Gen One’ BI was first introduced in the market. It could generate business reports but heavily relied on IT professionals and was inaccessible to those with no IT background. However, the reports generated by ‘Gen One’ BI were beneficial for enterprises, and hence it sparked the interest of many business analysts.

A few years later, the ‘Gen two’ BI was introduced as an updated version of its predecessor. The second-generation BI made the data more accessible and consumable to business owners. Overall, the ‘Gen two’ BI made the analytical tasks easier for businesses, but there were still many flaws when used for Big Data Analysis. According to a report by Business Wire, the adoption of Big Data is still an issue for 73.4% of enterprises. Another problem with the ‘Gen two’ BI was its high cost and time-intensive nature.

To solve these problems, a couple of years ago, the third generation of BI, which is commonly referred to as ‘Next-gen BI’, was introduced, and its prospects reflect the visions of CXOs across the world. The ‘Next-gen BI’ utilized Artificial Intelligence (AI) for providing real-time insights and predictive analytics from Big Data through visualization.

As the third generation of Business Intelligence came into the picture, many enterprises have been adopting it. As of 2021, the BI software segment is expected to generate a revenue of 22,845.99 million U.S dollars.

The following article will discuss different aspects of Next gen-BI, like how data visualization is done in BI, how it can add value to businesses, and many more.

How Is Data Visualized In BI?

Data visualization is a process of presenting raw data in eye-catching and graphical formats such as bar graphs, pie charts, bar graphs, etc. The purpose of visualization is to recognize trends and patterns among the different units.  Traditionally it was done manually on software like Microsoft PowerPoint, excel sheets, and photo-shops.

The modern and more efficient way of doing visualization is by using BI tools. But before we can see the visualization of the data, there is a long process involved that raw data has to go through.

Here is how visualization is done in BI,

• Initially, the types of data and data sources that are going to be visualized are defined.
• After the first step, the transformation methods and database qualities are identified.
• Then, the data is sourced by BI from its initial stages. Here the data is cleansed, mapped, and standardized to a unified format.
• With the help of APIs, data is moved to the staging area.
• The cleaned data is finally moved into the storage, and this data is then visualized.

What Can The Next-gen BI Offer To Businesses?
There are numerous benefits that organizations can gain by opting for Next-gen BI. All the tedious and repetitive tasks can be automated with Next-gen BI. It can help businesses to perform better because of its following capabilities:

Data Is More Accessible, And The User Interface Is Intuitive: According to Dataversity, the customs data visualization offered by the next-gen BI has significantly upgraded the outcomes of BI. By opting for Next-gen BI, many steps in the enterprise workflow can be automated, improving the overall efficiency of the workflow.

The automation of processes allows employees to focus more on their primary tasks rather than looking into data for hours every day for insights. This reduces the friction throughout the entire data lifecycle.

Next-gen BI has allowed businesses and customers to access data from their fingertips. Nowadays, there are AI chatbots created especially to answer all the data-related queries. These bots analyze the data and put forward the best visual representation for the users.

It means that companies now don’t have to invest their time creating pie charts or bar graphs anymore.  Anybody can generate relevant information in a matter of seconds just by asking some set of questions.

This is a considerable improvement in the data analysis field as earlier businesses required human resources to extract, analyze, and visually represent the data. With the constant development of technology, next-gen BI is no longer just a tool for companies, and it has become more conversational and intuitive.

Real-time insights: In today’s world, companies need to act on real-time data. The next-gen BI provides real-time numbers that enable businesses to analyze live data and gain real-time insights. A report by Forrester shows that insight-driven companies are growing at the rate of 30% per year.

The Next-gen BI is incorporated with Artificial Intelligence (AI) and Machine Learning (ML) technologies. ML algorithm makes next-gen BI a self-learner and allows it to learn which insights are helpful for the business making the presented data more refined without any human feedback. AI assists it in automating the process and producing actionable valuable real-time insights in an increasingly timely manner.

Better Predictive Analytics: As Next-gen BI performs Big Data Analysis, it can offer predictive analytics. Predictive analytics enables top executives to lay out a plan of action for their business’s further development and expansion.

Assist Companies To Make Better Decisions: Another way the ‘Next-gen BI’ adds value to the business is by allowing them to make better decisions through its insights and hence decreasing the risk factor. According to TechCrunch, a company with the help of next-gen BI software witnessed an increase of $100,000 revenue per year.

The ‘Next-gen BI’ also offers insights into customers’ behavior, which can also assist in delivering a better customer experience.

Who Can Benefit From The Next-gen BI?

All the types of enterprises can benefit from the next-gen BI:

MNCs: As MNCs have to deal with massive amounts of data, they can benefit from next-gen BI for Big Data Analysis. Automation of monotonous business tasks and access to real-time insights that are very difficult to generate manually can be handy for MNCs or any business in general.

Mid-sized Businesses: As Mid-sized companies don’t have the budget for hiring a data analytics team, ‘Next-gen BI’ can help them with data visualization and exportation that can cut costs across the board. Therefore, it becomes more beneficial for businesses to utilize this software technology.

For Small and Medium-sized businesses utilizing BI-as-a-service is a great way to integrate this technology. By using BI-as-a-service, companies don’t have to invest in creating an on-premise infrastructure, making it a viable option. We will discuss more on BI-as-a-service in the further section.

Small-sized Businesses:

• BI’s desktop tools like Tableau and Microsoft Power BI are a more popular choice among small-sized businesses. Along with these desktop tools, many small-sized companies and startups go with the option of SaaS and cloud services.One of the reasons small-sized businesses opt for these tools is because of their simplicity. They work very similar to Excel and PowerPoint, applications in which most analysts already have high proficiency. Today’s BI tools also utilize AI-ML algorithms along with natural language for generating visual data.
• Microsoft Power BI is now integrated with Siri shortcuts. It means business owners will only have to say, ‘Hey Siri, open my reports,’ and the tool will present the user with all the relevant data. Power BI offers centralized reporting capabilities so that enterprises can make most of the self-service analytics.
• Tableau is another tool that is similar to Power BI.  It adds AI to the workflow and makes data more accessible by sharing the reports on Slack. Tableau has gifted its user with the ability to converse with the data. It offers suitable visuals and interactive explanations to all the data-related queries of business users. Tableau also provides AI-based predictions for Salesforce reports and sends them to Slack.
• Qlik Insight Bot is a conversational BI tool that provides automated insights and allows business owners to make data-driven decisions. The data and reports can be accessed via Qlik Sense and also through other platforms like Slack, Skype, Salesforce, and Microsoft Teams.  All the insights are driven by Natural Language Generation (NLG).
• Microsoft Power BI is now integrated with Siri shortcuts. It means business owners will only have to say, ‘Hey Siri, open my reports,’ and the tool will present the user with all the relevant data. Power BI offers centralized reporting capabilities so that enterprises can make most of the self-service analytics.
• Tableau is another tool that is similar to Power BI.  It adds AI to the workflow and makes data more accessible by sharing the reports on Slack. Tableau has gifted its user with the ability to converse with the data. It offers suitable visuals and interactive explanations to all the data-related queries of business users. Tableau also provides AI-based predictions for Salesforce reports and sends them to Slack.
• Qlik Insight Bot is a conversational BI tool that provides automated insights and allows business owners to make data-driven decisions. The data and reports can be accessed via Qlik Sense and also through other platforms like Slack, Skype, Salesforce, and Microsoft Teams.  All the insights are driven by Natural Language Generation (NLG).

Future of Business Intelligence

• According to Statista, the revenue of the BI software market is going to reach 32,652.95 million U.S dollars by 2026.
• In the future, the BI tools will get better at providing collaboration among the team. The current BI tools don’t have the feature to connect it with a broad network, but many researchers are working on it so that colleagues can work side-by-side, according to
• Third-party integration will also be introduced. It will simplify data processing and allow businesses to take appropriate action on the generated insights. The BI software and tools would become more accessible as they will send real-time insights to users via emails, text messages, and notifications.
• The ever-improving AI and ML technology will make it an even better self-learner. The coming generation of BI software is expected to become more intuitive and predictive in nature. Also, the AI/ML-based are good at learning from their past experience, making BI software more efficient with time.
• It will have Data Proactivity. This means that only relevant data will be provided to business owners, and all human efforts required to do this job would be omitted. This concept of Data Proactivity is co-related to third-party integration. Users don’t necessarily have to engage with the tools; instead, all the business data would be brought to users without any engagement.
• There will be an advancement in the network. A solid network is needed for any software. Many Computer Engineers are looking for a way to develop hardware networks and automatically increase the volume of data of BI tools.

Business Intelligence-As-A-Service

As discussed earlier, BI-as-a-service is another ingenious way to use BI technology if a company has limited IT resources at its disposal. Traditionally Business Intelligent solutions require extraction of the relevant data, organizing the data in the data warehouse, and then deriving insights from them by applying various algorithms.

But things have changed drastically ever since cloud computing came into the picture. It has made BI solutions more scalable and cost-effective for SMEs. Businesses can employ an end-to-end business intelligence-as-a-service solution instead of investing money in a data warehouse and maintaining it. Companies that already have incorporated BI solutions can also utilize BI-as-a-service for more streamlined and agile solutions which have better accessibility of data.

Sometimes people think BI-as-a-service is the same as Software-as-a-service. The SaaS providers only offer the part of what BI-as-a-service brings to the table. SaaS companies deliver an optimized user interface for interacting with data. But BI-as-a-service providers, along with intuitive UI, also do the job of extraction, organization and offer real-time insights into your data.

If you are also looking to hire a BI-as-a-service company, you must choose one that caters to all your business needs. As a BI-as-a-service company will do most of the heavy lifting, it becomes essential for you to choose the right Business Intelligence solution provider.

The question that comes to mind is how to choose the right Business intelligence solution.

Here are some criteria based on which you can make the right decision:

Reporting Abilities: The BI-as-a-service company should have the ability to provide dynamic reports that can be customized as per your insights requirements. By looking at these reports, you will be making important business decisions; therefore, the service provider must offer dynamic reports that perfectly fit your purpose.

Seamless Integration: The solutions that the service provider would deploy should be able to work in harmony with any other existing solution that you might have deployed in the past.

Accessibility of Data: You should be able to access data anytime, anywhere, and from any device you want. Look if the BI-as-a-service company has the ability to offer real-time insights and reports on any mobile application.  It will save a lot of time and increase the efficiency of the business.

Company’s Expertise: The employees of the service provider should have the relevant industry knowledge and experience. A BI-as-a-service company with a highly skilled team can let you gain a competitive advantage through their above-par BI services.

From the above article, we can see that the future of Business Intelligence and visualization is looking very bright. To ensure that the BI models are integrated seamlessly and working without any troubles, outsourcing this job to a BI-as-a-service company with a team of skilled and experienced professionals is another effective way to adopt this technology.

Sources: Statista, Gartner, Techcrunch, Dataversity, Deloitte, Businesswire, Blugranite, Forrester, ComputerWeekly

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Today, influential megatrends are reshaping the industry adoption of Conversational AI, reimagining value delivery in unforeseen ways. It includes the emergence of voice commerce, data-driven chatbot design, customer sentiment analysis, multi-lingual support, M2M conversations, voice assistants, and much more.

With proven benefits, enterprises are increasingly putting Conversational AI into action for enriching customer experience, improving bottom-line performance in the process. As per Deloitte’s Beyond Touch: Voice Commerce 2030 study, voice commerce will increase online sales. The decisive factor here is unimagined convenience for the customer, with the study predicting that over 30% of e-commerce sales will be driven via voice commerce by 2030.

Applications of Conversational AI

Businesses leveraging Conversational AI can become an emphatic organization that is more responsive to the customer’s needs. Chatbots depicts one of the most popular forms of Conversational AI. However, there are diverse applications of this emerging technology across business verticals, some of which include:

Online Customer Support: Conversational AI is being implemented by many companies for providing 24/7 customer service support at an optimized cost and much greater reliability.  E-commerce platforms use virtual assistance for product selection and customer service, which helps the business achieve higher customer satisfaction and service relevance.

HR Processes: Most of the time, employees call HR managers to know the information they can look up themselves. To maximize the HR department’s efficiency, a 24/7 employee service AI chatbot can be implemented to answer all the employees’ FAQs.  The HR chatbot is also helpful in passing any relevant information and real-time updates related to any changes in the company’s policy.

Retail & Ecommerce: According to Statista, worldwide e-commerce retail sales are expected to reach 5.4 trillion USD by 2022. At this growth rate, e-commerce platforms often struggle with manual sales and support systems that have their limits to delivering a seamless customer experience. As a respite, e-commerce and retail platforms are shifting towards Conversational AI to increase engagement, elevate the customer’s purchase journey, and offer personalized communication for the buyers.

Gaming: Gamers are usually engrossed and if there is a challenging level to complete, and they happen to face a technical glitch, they want it resolved immediately so that the momentum is not lost. Nowadays, many gaming companies have set up service centers to assist gamers whenever they feel stuck at a level. To help resolve common issues, an AI chatbot is set up to answer these queries. With this help, there is a high chance that the slump faced by gamers will end, and they are less likely to end the game mid-way.

Travel & Hospitality: The traveling customers expect to have their queries resolved on demand. It is where an AI-based chatbot can come in handy for companies that provide travel & hospitality services. Conversational AI is used to keep track of customers booking details, room preferences, opted traveling packages, travel dates, and many other details.

How Does Conversational AI Add Value to A Business?

Conversational AI is a technology that guarantees optimized ROI for growth-oriented enterprises. It brings value for both the customer and the employees and promises exceptional benefits for businesses, some of which include:

Substantial Cost-Savings: About 15 -70% of cost reduction opportunities are available for businesses with Conversational AI depending on customer channel interactions. The algorithm powering the Conversational AI bot can be trained to answer repetitive questions, allowing valuable human resources to focus on more strategic roles like customer relationship development and grievance redressal.

Increased Sales & Customer Engagement: Conversational AI allows customers to engage more frequently with the brand, creating a sense of trust among the target audience and ultimately increasing revenue.

The personalization feature can keep track of customer activity and recommend products or services depending upon their previous preferences.  

Deep Customer Insights: The customer’s interaction with the Conversational AI-based bot provides companies with data on what problems customers face and which issues need to be targeted and resolved on priority.

How Are Enterprises Implementing Conversational AI?

Let’s take a look at some real-life instances of some world-class organizations using Conversational AI to improve value delivery.

A Leading Airline Company Applies AI Chatbots

Germany’s largest airline company implements Conversational AI to update passengers on canceled or missed connection flights. The AI chatbots can answer all the FAQs that a passenger can have while boarding the plane.  If passengers are not satisfied with the answers of Chatbot, then they also interact with human agents without any waiting period. This way, customer service executives can spend more time answering complex and specific queries.

A Mental Healthcare Chatbot

A mental healthcare company makes use of a conversational AI-powered mental health chatbot that has made mental healthcare services more accessible for patients. It breaks all the barriers of mental therapy and allows people to have real-time interaction with the bot. Researchers developed the AI chatbot in collaboration with Stanford University to deliver behavioral therapy to patients at their convenience. It provides psychoeducation to the users and offers insights to deal with their mental issues by conversing with them.

Implementing Conversational AI:

• Making A Smart Adoption Strategy: While making the adoption strategy, businesses should figure out detailed customer persona, narrow down the scope, and use the data that is already available to set up the scene.
• Conversation Design: A lousy conversation design can lead to a bad customer experience. Conversation design plays a massive role in delivering a superior customer experience. A suitable conversation design must be sought out earlier in the development stage as it is easy to change.
• Choose The Right Channels for Integration: Before jumping into the development of Conversational AI, you should figure out which key channel and existing software you are to integrate with it, for example, company website, social media channels, CRM, emails, etc.

Even though Conversational AI has many applications today, there is still much room left for its development. Chatbots will become more personalized with time. According to a survey carried out by Deloitte, personalization is one of the significant fields of innovation and is responsible for 16 % of patents filed today.

Sources: Deloitte, Statista, Juniperresearch,

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Consumers use various mediums like email, chatting mediums, and social media platforms to raise their concerns and expect real time resolution to their issues thus it becomes time-consuming and taxing for an entity who is working behind the scene to process and respond in-situ, to the real-time data that is being generated rapidly across these various platforms.

Henceforth, it becomes the responsibility of the organization to identify the problem and provide a solution to the consumer in real time, after following the adequate process.

WHY WE NEED AUTOMATED RESPONSE GENERATORS?

In the current scenario where customers’ expectations are changing and organizations want to maintain a reputation in the industry where they are seen as a brand that respects the needs of its consumers, it has become imperative to automate the process of Real Time Response generation.

For a business entity to respond in real-time, it needs to develop a model that is capable of apprehending, filtering and analyzing data to make useful business decisions. The model should be capable of determining that a response is required and at the same time should also be able to intelligently identify what, when & how of the responses or reactions.

IGT’s NLP SOLUTION FRAMEWORK:

The fast-growing human-generated content, which includes text, email, speech voice recordings, videos, social media post, etc. are almost unstructured. And customers expect organizations to uncover the deep insights from these unstructured data and transform them into actionable insights, accelerating the speed of the outcome.

Developing a machine learning pipeline is generally a complicated process. Text analysis is about leveraging tools, techniques, and algorithms to process and understand linguistic data, which is usually in unstructured forms like text, speech, etc. Within this pipeline, we use the tested and well-experimented strategies, techniques and workflows to gain useful insights by leveraging our travel and hospitality expertise.

At IGT, we developed an NLP solution framework with the capabilities to mine the incoming query text, parse that text and do text preprocessing, extract relevant information and classify them into various entities to evaluate the intent/sentiment of the query.

Having all this in place, a tailor-made response is generated and shared with the concern.

The process starts at the data ingestion point which can be from multiple sources like email, chatbox or social media/forums.

The framework is capable of recognizing who the customer is by checking the information of the query sender against the customer database. This enables us to provide improved tailor-made responses for that customer based on his/her historical data regarding queries if it exists.

The incoming query text is ingested by the NLP solution framework where the text is analyzed based on text processing rules that the machine learning model has. Then, an appropriate response is generated and is provided to the customer instantaneously.

This framework has not only reduced human effort, but also has improved efficiency and accuracy. And because of a set framework in place, it has become easy to recognize the pattern and types of queries of the concern which has significantly also reduced the turnaround time.

With the help of this Machine Learning based automated response generation system, the task has become less taxing because of its capability to analyze and respond in real-time. Along with this, the instantaneous generation of responses with accuracy and efficiency has also lead to customer satisfaction, trust and loyalty.

Let’s understand with an example of a customer email to a leading US carrier about baggage query:

At the core of our system, whenever a customer sends an email to the carrier enquiring about his baggage, the system gets triggered and starts handling the incoming message, it does the required preprocessing on the text corpus and feeds it into the feedforward Neural Network (NLP Layer), which in turn predicts the most likely response and provides the information related to the baggage like its current location, why it’s being delayed, etc. to the customer.

The NLP framework also extracts the relevant information so that the response is curated to match the customer’s question. The system aims at understanding the customer email and then starts to action based on that understanding and convey meaningful information.

About the Authors:

Ambuj Mittal is a Data Science Engineer at IGT Solutions’ Travel Analytics group. As a Machine Learning practitioner, he loves challenging real-life travel problems which can be solved using the power of Data Science and ML. He is skilled in Python, Machine Learning, Deep Learning, and Data Engineering. He is involved in development of AI and ML solution for our Travel and Hospitality customers. For further information he can be reached at ambuj.mittal@igtsolutions.azurewebsites.net

Qurratulain Saleem is a Data Engineer at IGT Solutions. He is skilled in Python, Data Structures, Algorithms, Machine Learning and Deep Learning. He is a Statistics and Math enthusiast. He is a passionate programmer and like solving algorithm problems and philosophizing about AI, algorithms, machine learning and their effects on our world and real life problems. He can be reached at Qurratulain.Saleem@igt.in

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