Architecting the Data Management Platform Success Story

Authored by Deepak Kushwaha, Program Manager

The Data Management Platform (DMP) can be visualized as a data warehouse. DMP is a software that houses audience and campaign data from various kinds of information sources. DMPs talk to demand side platforms (DSPs) and help marketers unify audience and performance data across different sources. Get introduced  to DMP by reading our blog, ‘An Introduction to Data Management Platform (DMP)’.

According to a report by Digital Journal, the DMP market is expected to grow at 15% CAGR growth and reach USD $3 billion by the year 2023. DMPs have use cases that span industries that include eCommerce, Auto, Retail, Real Estate, Travel and Hospitality, and Financial products. With extensive use cases the DMP should see a 100% adoption rate, however, despite various off-the-shelf solutions in the market, DMP implementations are riddled with challenges. For success, CIO’s must overcome these challenges:

Challenges:

  1. Audience performance measurability across devices and channels, including real-time and offline.
  2. Scale and effectiveness with onboarding first-party, second-party and third-party data audiences
  3. Ability to syndicate custom audiences at scale across channels, including offline
  4. Lack of complete view of consumers in platforms such as Facebook
  5. Accuracy in cross-device linkages to consistently and accurately identify consumers
  6. Audience Data Loss after Integration with DSP and Ad Serving Platform
  7. Data pixels provided by DMP are too large to be implemented in Third-Party partners

Good architecture design is the key to overcoming these challenges, when stitching together with the data from various sources that work together to develop a 360-degree view of the audiences. Here is a six-stage architecture for a custom DMP.

  1. Data Sources Identification – Identifying all the data sources is the first step. These sources can be first/second or a third party such as WebApp, Mobile App, CRM, Partner Data, SAP, Retail Transactions, Google AdWords, Social sites such as FB, etc.
  2. Data Aggregation – Data Aggregation, is the second and one the critical steps, performed by existing solutions such as Google Tag Manager or Tealium.  Only the script provided by Tag Manager (GTM/ Tealium) is used in a client’s codebase, which extracts the complete required analytics data. The Tag Manager internally configures all the other analytics tags.
  3. Data Collection and Processing – This step utilizes external systems (Kochawa, AppBoy, Google AdWords, FB Analytics, AppFlyer) for fetching and processing data. A simple way to gather cross channel/device data. Tags have specific functions and should be reviewed and selected based on requirements. These tags are applied to capture user actions data performed on the data source (website or App).
  4. The DMP – The complete set of data from step 3 is cleaned, classified, stitched, processed and analyzed before storing in the DMP. At this stage, the DMP is a data storage unit. Choosing optimum cloud storage such as S3, AWS EMR, RDS, Redshift, Apache Hadoop, Hive is imperative; and based on volume of data, performance or other nonfunctional parameters.
  5. Data Analytics – Nearly the last step, data analytics forms the base for adding machine learning algorithms (based on R/Python/SQL/Spark) for building a customer profile, look-a-alike models, segmentation and custom audiences. Offline provisions are made based on the clients’ needs and the volume of data to be made available.
  6. Dashboard/Publish Data – In the final stage, the dashboard contains all the reports.  All real-time and offline data is shown through a secure Rest API’s to another 3rd party, DSP’s, Ad service platform. Email notification services are also integrated with DMP to campaign client ads/coupons/offers to the targeted custom audience etc.

From a review of the requirements to careful selection of technology used, the role of an architect is crucial to the successful DMP implementation. DMP’s help brands gain an edge by making their data more portable and easier to integrate with DSPs. It provides a competitive advantage by leveraging millions of data profiles from consumers; performing business analytics; and creating custom segments and audiences.

Infogain helps Data Management executives and CXO’s to achieve their business objectives. We invite you to connect with us at info@infogain.com.

Infogain at Google Cloud Next’19 | Robotic Process Automation (RPA) and Google Cloud AI

Infogain’s team will be in San Francisco for Google Cloud Next’19, which brings together some of the brightest minds in tech for three days of networking, learning, and problem solving. Connect with thousands of IT professionals and executives across industries while engaging in deeper technical content, hands-on learning, and more access to Google experts than ever.

Infogain delivers value to our own and Google’s customers, by leveraging its strategic partnership with Google. The partnership allows Infogain’s enterprise customers a secure, flexible and cost-effective cloud platform. Our services include:

  • Cloud roadmap consulting
  • Workload migration
  • Cloud native application development
  • Managed support services
  • AI/ML using GCP Services
  • Automation

Visit INFOGAIN’S TEAM AT GOOGLE NEXT’19

Schedule a meeting with us at partner Automation Anywhere’s Booth S1669, in Moscone South – Lower Level

Contact us to schedule a meeting during the event.

For more information about Infogain, visit www.infogain.com.

An Introduction to Data Management Platform (DMP)

Authored by Sanjay Hooda,  Business Advisor

DMP or “data management platform” aggregates all kinds of data from various sources, including online, offline or mobile sources, by allowing users to access that data in various ways. The core idea behind a DMP is that businesses must be able to take advantage of diverse kinds of information from a variety of sources and collect that information in one central place in order to attain the business intelligence that truly benefits the enterprise.

DMP acts as a thread between a CRM, data vendors, and websites to deliver targeted content to customers and “look-alikes” around the Web.

DMP can be used for targeted advertising to customers and their look-alikes, and segment people with particular demographic or behavioral attributes. Running on the ad tech platform, DMP is to digital advertising what engines are to cars. They help forge better personalized content served on websites, emails, mobile apps and informs call center scripts. Research indicates 80% of consumers are more likely to make a purchase when brands offer personalized experiences. DMP helps companies to nurture existing customers and find new ones. According to Gartner, by the end of 2022, data management manual tasks will be reduced by 45 percent through the addition of ML and automated service-level management.

DMP can be instrumental in streamlining search campaigns by adding a layer of efficiency in several ways:

  • Optimized Messages and Bids for High Value Audience

Traditionally when launching a targeting campaign all searchers are treated equally. The performance report will not distinguish between someone who already researched the product and general search. In this instance, a DMP can help identify and approach these customers differently. With DMP, brands can bid on these high-value audiences that are rich with data – website visits, calls, queries, etc. by prioritizing the budget or creating different campaigns for different audiences.

  • Bridging the Online & Offline Segmentation

One challenge often faced when launching a search campaign is that once a prospect shows interest, there is a lack of knowledge on their offline activities. A DMP can be instrumental in the creation of campaigns that follow from website to offline experiences.

  • Adding Cross-Device Insights

When searches no longer have a single point of entry or continuity and has been spread across devices – laptops, mobile, tablets; mapping a customer buying journey can be tedious.

DMPs are handy in connecting users to their various devices, through both deterministic and probabilistic methods. This methodology is more transparent and gives advertisers control, compared to search engines that often don’t disclose which signals or methods they are using to stitch devices together.

  • One Experience Across All Channels

Search as a channel is unique, low-funnel and high-intent, making the metrics very different from other digital channels. However, all advertisers want brand cohesion, where all their search campaigns speak the same language as display and social campaigns.

When these campaigns are being managed by separate teams, each operating with a unique audience database, this can be a challenging task. DMPs simplify this challenge by ensuring that the same audience definition is used across all marketing efforts.

  • Privacy is Paramount

The recent turn of event GDPR, concerns regarding privacy are paramount, since the data is better safeguarded. DMP can help by adding an extra privacy layer and privacy controls. A DMP will ensure that your data is only yours and help your brand with the insights needed to tailor your search campaign.

DMP has a use case for all kind of businesses and industries:

Industry/Business Type DMP Use Case
eCommerce

 

Retargeting based on page depth

Time spent

Content consumed

Action taken on the site

Items added to the cart

Product value

Product sale value

Real Estate

 

Target audience in the CRM system based on email id/phone number

Collect email id of people who are filling out the form

Need to know which email ids are from our DMP

Financial Products Targeting ads based on movement in the stock market index
Travel

 

Tracking the PNR number or booking number of the conversions on an airline site
Telecom

 

People who have landed on client’s Facebook or YouTube pages

Targeting people whose plans are about to expire

Consumer Durables

 

Targeting users by appliances, which product did they add to cart etc.

Targeting people across Facebook and display who have visited the website, but not yet converted

Auto

 

Partnership with auto portals to pass on to audience (if we do direct deal with them)
B2B

 

Ability to find audiences that are similar to our current audience – HR professionals, CIO/CTO etc.
FMCG

 

Ability to target a demographic audience precisely. For example, female 25-35 accurately (DSPs also work with 70% accuracy)

How to achieve 90% accuracy by intersecting DSP targeting, third-party data & data from women oriented websites

Several data management platforms available right now:

  • Oracle BlueKai
  • Adobe
  • Lotame
  • Simpli.fi
  • Salesforce DMP
  • Synthio
  • ClicData
  • MediaMath (TerminalOne OS)
  • The Trade Desk
  • Mapp

Digital Journal has predicted 15% CAGR growth during the forecasted period and USD 3 billion by the year 2023. That statistic alone tells us that DMP may be the answer to the success of your future marketing campaigns, by helping to bridge cumbersome cornerstones. When you use data to inform and optimize search campaigns, brands can drive search campaigns that are relevant, interesting and informed.

Infogain helps Data Management executives and CXO’s to achieve business objectives. We invite you to connect with us at info@infogain.com.

The Impact of Artificial Intelligence and Machine Learning on Mobility

By Upakul Barkakaty, Global Practice Head of Mobility Solutions at Infogain

Mobility is no longer a fringe topic. Mobile devices have become an integral part of our lives. With the power and proliferation of high-end smartphones and tablets on the market, people are using laptops less for tasks which can be done through their mobility devices, including wearables, virtual voice assistants such as Alexa, Siri, and Google Assistant.

We are using mobile devices for everything from personal finance, shopping on Amazon and Flipkart to filling out timesheets and exploring company directories in the workplace.

Enterprise mobility solutions are undergoing a seismic shift as well. AI, machine learning, and data analytics are transforming mobility from a mere transactional function to take on a more strategic role in the organization.

Defining AI

“A machine with the ability to perform cognitive functions such as perceiving, learning, reasoning and solve problems are deemed to hold an artificial intelligence”

Although the idea of AI is not new, the pace of recent breakthroughs definitely is. There are three main factors fostering this acceleration:

  1. Computing Capacity: Advances are emerging beyond the current generation of central processing unit (CPUs) and Graphics processing units (GPUs). This capacity has been aggregated in high scalable data centers and is accessible through the cloud.
  2. Big Data: Huge amounts of information (images, voice, video, location, sensor information) are collected through IoT processes and can be used to train AI models.
  3. Machine Learning (ML): Algorithms have advanced significantly through the development of deep learning based on neural networks.

How is AI transforming Mobility?

AI and Apple’s A11 Bionic Chip. Apple’s iPhone X has introduced A11 Bionic chip. The chip contains a neural engine to power its AI features and applications like Face-ID recognition and Augmented Reality. How powerful is the A11 Bionic Chip?

Samsung is also at work to add AI-specific CPU cores into its mobile chips, and San Diego chipmaker Qualcomm announced its Snapdragon 845 chip, which sends AI tasks to the most suitable cores thus enhancing operations for energy-hungry features.

Use cases for AI in mobile app development 

AI-powered solutions in mobile app development are changing the organizations modeling with innovative applications and solutions.

  • Empowering Search Engines – Artificial intelligence and machine learning have introduced a new way to use images and voice, unlike text mode.
  • Artificial Intelligence Combined with the IoT – AI lets your devices communicate with each other. AI is collecting real-time data. That data is processed so that devices learn to function on their own.
  • Smartphone Camera Are Getting Better in Subject Detection – By using AI, the interface of a smartphone camera can easily detect the subject in the camera frame. Amazon is already using this function. Instead of scanning a barcode, users can simply place an order by taking a photo of an object. Similarly, there are home design applications using AI and AR.
  • High App Authentication – With AI and ML, security concerns have been reduced by giving alerts to the users about possible threats and vulnerability by analyzing the user behavior
  • Virtual Colleague Chatbot – Meet Nysa, Infogain’s virtual employee. She has answers to questions related to Infogain ecosystem 24x7x365. Nysa never asks for vacation time, she never complains about the food in the cafeteria, and most importantly, she doesn’t ask for a raise!
Machine learning, on a global scale, makes mobile platforms more user-friendly, improves the customer experience, maintains customer loyalty, and aids in building consistent omnichannel experiences.

 


Six most lucrative use cases for machine learning in your mobile app.

Use-cases in the Insurance Industry

An insurance tech startup (Lemonade) based in New York City, processes claims within three seconds with the help of an AI-powered claims settlement bot. The bot verifies details of claims, and runs them through a fraud detection algorithm and passed instructions to the bank to transfer the claim amount.

Natural Disaster Management: Early warnings of a disaster can be determined with accumulated data related to a specific geographic location using remote monitoring tools to perform analytics with AI & ML.

Health Monitoring: Patients wearing health and fitness trackers can have their vitals continuously sent to the doctors or to the smart device manufactures with the help of IoT platform. That data can be processed to create advanced smart devices and better decision making for disease cure.

AI-powered tools and insurance: Natural language processing, machine learning, chatbots, predictive analysis, marketing personalization — a lot is happening in the insurance industry, and it’s all being driven slowly by advancements in artificial intelligence. There is great potential for cost savings, product improvement, and improvements in customer experience.

Conclusion

AI, machine learning, and data analytics are transforming mobility, and their impact will continue to yield promising solutions, especially with the application of IoT.

With the impact of AI and ML on mobility, the possibilities are endless. If you’d like to learn how AI and ML can improve your mobility solutions, contact an Infogain Mobility expert here.

Infogain and Automation Anywhere Partner Joint Roundtable at Grand Hyatt, San Francisco

February 20th, 2019

Infogain & Automation Anywhere Joint Roundtable
Grand Hyatt, San Francisco
5 p.m. – 8 p.m. PST

Enterprises today are changing their operating models to ones that incorporate digitization and leverage automation. RPA has proven its ability to deliver meaningful results and is leading to an era of Intelligent Automation (through integration of AI/ML).

Learn from technology experts, industry influencers and your peers, as we:

  • Discuss – on key topics including managed services model for RPA, process transformation, scalability, enterprise adoption, and industry trends
  • Hear about your peers’ experiences – challenges and success stories
  • Learn from technology experts, industry veterans and influencers about strategies for successful implementation of RPA processes and technologies

Agenda highlights:

  • Topic for the event “Transforming Business Through Managed RPA”
  • Keynote by Gans Subramanian, Vice President, Head Digital Solutions – Infogain
  • Keynote by Manish Rai, VP of Product Marketing – Automation Anywhere
  • Moderator-led discussion and case studies
  • Networking dinner

Space is limited, register today for a seat at the table.

Infogain in association with TECHGIG joint Roundtable on February 15th 2019, The Leela, Gurgaon

Infogain and the Times Internet will host the roundtable: “Platform Engineering and what it means for business” on February 15th in Delhi.  This invite-only, closed-door event will host senior engineering practitioners and IT leaders driving technology related initiatives within their organizations in retail, FMCG, eCommerce and other industries.

The fast emerging ‘platform economy’ presents companies with a unique opportunity to disrupt the status quo and achieve operational excellence in the process. Platform Engineering has the potential to help companies find new avenues of growth and reinvent themselves. The difference between leaders and laggards will be determined by how well we use the platform economy to our advantage.

Session Agenda: 

  • The rise of the Platform Economy and what it means for businesses
  • Why incorporating “Customer Experience” is the key to ensuring Platform Engineering success
  • Innovation, Integration, Investment and Transactional – Which type of Platforms should be the focus areas?
  • How are Platforms used in managing supply chain transactions to improve its efficiency?
  • How are emerging technologies like IoT and Cloud Computing acting as a catalyst for Platform Engineering success?
  • Platform Engineering – Redefining Products in 2019: A 2019 Roadmap

 For more information, click here or email us at Events.India@infogain.com

IncubateIND: 5 Big Data Analytics Trends for 2019 – Naveen Arigapudi

An authored article by Naveen Arigapudi, that appeared in IncubateIND’s tech forum.

The promising evolution of Big Data and Analytics

Hyperscale data generated by digital connected devices and real-time Customer Experiences are driving a dramatic shift in analytics environments and driving actionable outcomes. Which technology trends should CIOs and CDOs be leveraging to boost their data driven initiatives?

Here are the Top 5 Big Data Analytics Trends in 2019 and beyond:

Augmented Analytics

Augmented analytics, an approach that automates insights using machine learning and natural-language generation, is the next wave of disruption in the data and analytics market. augmented analytics automates as many processes as possible, giving business users access to real insight and act on data quickly and accurately.

Gartner analyst Rita Sallam calls the approach “the next disruption in Analytics and BI”.By 2019, 50% of analytics queries will be generated using search, natural-language query or voice, or will be auto-generated.

Self-Serve Analytics Platforms and Citizen Data Scientists

The prevalence of data science and self-service analytics platforms such as Alteryx and Alpine Data will continue to give rise to citizen data scientists, those business users from cross-functional teams across enterprises who use data for everyday business decisions. These users can perform advanced analytics without having the skill set of a data scientist and with minimal dependency on IT. This frees up trained data scientists to be involved in more critical data analysis.

Edge Computing

Edge Computing, which allows information processing at the device or gateway level rather than within the cloud or a data center, has been a part of the technological space streaming network performance for quite a while now. McKinsey estimates that of the $500 billion in growth expected for IoT through 2020, approximately 25 percent will be directly related to edge technology. Edge computing will continue to help improve data compression and transfer in the connectivity layer of the technology stack, reducing network bandwidth and making a wider range of IoT applications possible.

Emergence of Enterprise Data Catalogs

The Enterprise Data Catalog serves as a single place for an organization to contain its assets. It’s an AI-powered data catalog that provides a machine- learning-based discovery engine to scan and catalog data assets across the enterprise—across cloud and on-premises, and big data anywhere. A recent report from Gartner, Data Catalogs Are the New Black in Data Management and Analytics finds that demand for data catalogs is soaring as organizations struggle to inventory distributed data assets to facilitate data monetization and conform to regulations.

Cognitive Technologies and AI

Cognitive technologies, a product of artificial intelligence (AI), has been evolving over decades, but the technology has improved dramatically in the last few years, especially in the areas of computer vision, natural language processing, speech recognition, and robotics.

According to research by IDC, between 2017 and 2021, global spending on AI-focused systems is expected to grow at a CAGR of 50 percent reflecting $200 billion in cumulative spending across an array of sectors including health care, retail, banking, and manufacturing.

(The author is the AVP – BI and Big Data Analytics at Infogain – a Silicon Valley headquartered company with expertise in software platform engineering and deep domain skills in travel, retail, insurance, automotive, and high technology. Infogain accelerates the delivery of digital customer engagement systems using digital technologies such as cloud, micro-services, robotic process automation, and Artificial Intelligence.)

Robotic and Cognitive Automation: Range, Reach and Transformations

Infogain and Automation Anywhere held a joint Roundtable on November 14th in Santa Clara, CA to discuss strategies and real-world examples of how Robotic Process Automation (RPA) implementation boosts business outcomes in remarkable ways.

Key topics of the roundtable included:

  • How advancements in RPA technology drive Robotic and Cognitive Automation that replicates human actions and judgement at tremendous speed, scale, and quality at a lower cost.
  • RPA led enterprise transformation, enterprise adoption, and industry trends.
  • Strategies for defining an RPA roadmap from ‘Rule’ based operations to ‘Cognitive’.

 

Keynote: Digital Workforce Transformation

After 20 years of automation technologies, a majority of business processes are still manual. Why? Anubhav Saxena, EVP, Partnerships, Strategy & Operations at Automation Anywhere, said the reason is that the traditional automation approach is not sufficient.

A more efficient approach, and the vision of Automation Anywhere, is to enable human intellect to achieve greater things by:

  • Taking the robot out of the human
  • Putting human-like intelligence into the bot.

Saxena compared today’s workforce to the workforce of the not-too-distant future-a digital workforce for the modern enterprise with key elements of RPA, cognitive and smart analytics. He discussed Automation Anywhere’s Intelligent Digital Workforce Platform and gave an example of a digital accounts payable clerk from the platform’s Bot Store:

Outcome Driven Intelligent Process Automation

RPA has proven its ability to deliver meaningful results and is leading to an era of intelligent automation through the integration of AI/ML. Gans Subramanian, Infogain’s VP & Global Head, Digital Experience & Insights, presented the mind-boggling benefits of RPA highlighted with real-world examples:

RPA Bots:

  • Are designed to work faster than humans
  • Can work 24×7
  • Do not commit typos
  • Create cost savings

As a result, enterprises are now looking at intelligent automation for accelerating their digital transformation, given that now RPA is capturing significant process performance data.

Subramanian outlined Infogain’s RPA solutions which span the entire spectrum,
from setting up an integrated center of excellence to building an Agile automation factory.

The Bottom Line

Robotic and Cognitive Automation replicates human actions and judgment at tremendous speed, scale and quality, and lower cost. Business benefits include:

  • Lower cost of operations with cost savings of 25-30%
  • Enhanced precision and improved productivity by up to 80%
  • Uninterrupted business flow with non-intrusive, disruption-free RPA integration and implementation
  • Automated operations that minimize manpower dependency
  • Accelerated business value creation

Infogain delivers the expertise, tools, and frameworks to accelerate the digitalization process with RPA. Our holistic approach to automation includes end-to-end methodologies, process selection, ROI measurement, vendor recommendations, reusable automation frameworks, training kits, and launch plans.

Contact an RPA specialist here.

Silicon India, CXO Insights: Humanizing User experience through AI

This is a Guest Column for Silicon India, authored by Sumit Sheth, Head-Creative Imagineering, Infogain Corp

Headquartered in Los Gato, Infogain is a leading IT consulting firm specializing in technology solutions for the High Tech, Retail and Insurance industries and delivers digital customer engagement systems using digital technologies, such as cloud, micro-services, and more, to our clients.

Over the course of the last 60 years, Artificial Intelligence has radically transformed the ways that we interact with machines. AI refers to the replication of intelligence in machines, programming them to think and behave like humans. In science fiction novels, it is often depicted in the form of robots with human characteristics, and for many of us, these are the images we still think of when we hear about AI. But whether we realize it or not, Artificial Intelligence already plays a big role in our lives today. From Siri to Google’s search algorithms to IBM Watson, AI is adding substantial value to our digital experiences and driving growth for businesses across industry landscapes.

Hello Alexa
Amazon’s voice assistant, Alexa, has taken the world by storm. Alexa is revolutionary in ‘her’ ability to decipher speech in order to help us perform tasks such as setting alarms, scheduling appointments, finding information on the web, and much more. The success of Alexa and voice assistants like ‘her’ could be signaling a shift in technological trends, making invisible interfaces the new standard. With the rise of voice interactions, digital products are now being designed to emulate emotional intelligence, such as the ability to decipher tone. By doing this they are able to better predict user intents at each interaction point and, based on these predictions, to provide appropriate responses that meet user needs.

Smarter Homes Powered by AI
Nest, the learning thermostat that was acquired by Google, has a sensor that can pick-up your presence from a distance in order to display the time, indoor temperature, and current weather forecast. It uses behavioral pattern algorithms to make predictions based on your heating and cooling habits, anticipating and modifying the temperature in your home based on your preferences. It now offers security features as well, and can support voice integration with Google Assistant and Alexa.

Disney’s Magical Wristband
Disney’s Magic Bands are designed to create a frictionless experience at the Disney World Park. When you book your tickets online, you are prompted to select your ride, food, and sight preferences. Disney then analyzes your selections and develops an itinerary that optimizes your routes from one location to the next. Before your trip, your personalized wristbands arrive in the mail, containing all the data you previously entered. A system of sensors in the park then detects your presence when you are near your interest point, allowing staff to provide you with personalized experiences at each location.

“As AI evolves, we expect to see AI systems continue to transform industries, drive growth, and encourage innovation”

A New Age App Using the Power of AI & AR

Infogain’s Creative Imagineering and Mobility teams conceptualized a mobile application harnessing AI and AR technologies. The app enables the users to analyze the visual data in order to identify the car’s make, year, and model with a photo. For instance, the user could upload a photo of the car, and the AR/AI then offers a listing of tire types that are suitable for the said vehicle, accounting in other data points such as weather conditions at the user’s location.

Amazon Go
Amazon Go is a reimagined storefront that is powered by AI technology. Customers simply walk in, grab the items they’re looking for, and walk-out. AI algorithms are programmed to watch video feeds and identify the customers and the items they’re picking-up. Amazon bills them as they walk-out the door. A few moments later, an app on their phone sends them a receipt detailing their purchase.

Human-like Chat Experience
Adding a chat tool to your online store has become standard in the e-Commerce industry but hiring chat personnel and live user attendants can be costly and inefficient. Online stores often cut expenses by replacing human operators with simple chatbots. The robotic and unnatural responses produced by these bots can ultimately harm user experience. To improve this model, many AI chat applications are powered by natural language processing (NLP) technology that makes replies come across as natural and genuine. NLP uses knowledge of idioms, sentence structure, and machine-learned pattern recognition to match your words to a specific ‘intent’, one of several its been programmed to identify and act upon. NLP is part of a new field of UXreferred to as ‘Conversational UI’ that has grown significantly in recent years, particularly around e-Commerce.

AI for IA (Information Architecture)
AI can also be instrumental in designing better information architectures. Information Architecture relies on creating appropriate content groupings with labels that are meaningful to users. Artificial Intelligence can discover and propose relationships between content by analyzing content-related data much faster than a human could. Large-scale data analysis by an AI system, when combined with user research, can better identify relationships between content types and improve content groupings and cross-linking. You could group content and label it in a more meaningful way for your users, offer the right related links at the right time, and generally make your site, service or product feel more intuitive.

AI can also be used to analyze internal and external data to help you determine how best to build both internal information structures for content managers (e.g., for your content management system) and navigation structures for end users (e.g., the menu for your site or app).

AI for IM (Information Management)
In addition to supporting information architecture design, AI presents exciting opportunities for information managementby increasing the potential for latent findability and recommendation. You would never have to tag the content you upload into your corporate document management system ever again, because the AI system could infer the meaning and relationships between documents. It could also proactively notify you when someone uploads a document of interest to you, even if no explicit phrases appear in the file, by analyzing the unstructured content and mapping it to similar content you have bookmarked.

We’re far from living in a world occupied by the human-like robots depicted in our favorite science fiction novels and movies. Still, Artificial Intelligence is a part of our lives and many of us interact with it daily. AI is already substantially improving our workflows, powering better decision-making, and enhancing digital experiences. As it evolves, we expect to see AI systems continue to transform industries, drive growth, and encourage innovation.

To view the original article on SiliconIndia.com, click here.

The Rise of Kotlin: A statically-typed programming language

Authored by:

UPAKUL BARKAKATY | Practice Lead, Mobility Solutions

JAGDEEP SINGH | Consultant

More than 10 years ago, in September 2008, the first commercial version of Android was released by Google. While the ‘Core Android coding’ is done using C and C++, Android Applications were written predominantly using Java. The Go Programming Language is also supported for a limited set of APIs. Programmers often contend with a variety of Java issues, including:

  • Null references that are controlled by the type system
  • No raw types
  • Arrays in the Java language are covariant
  • Java works with SAM-conversions
  • Use-site variance without wildcards

While Java is still used in many applications, the Kotlin programming language has been gaining in popularity among developers. Well known applications, such as Netflix, Twitter and Pinterest now use Kotlin. Let’s explore the background and features that are leading to the ‘rise of Kotlin.’

Introduction to Kotlin

Kotlin was introduced by JetBrains in July, 2011 for purpose of having the features that most of languages don’t have (Wikipedia). After a year of development, it was open sourced under the Apache 2 license. The language was named after an island, near St. Petersburg, Russia. Kotlin v1.0 was released on February 15, 2016 as a stable release, with the latest version v1.3, released in October 2018 containing many more features. With the release of Android Studio 3.0 in October, 2017 Kotlin is fully supported by Google for the use of Android Operating System. In addition, Google is in the midst of a lawsuit against Oracle for the commercial use of Java API, while the company is considering Kotlin as their official language.

“In 2018, among both innovative and mainstream IT adopters, Kotlin has an emerging market share that is growing rapidly, and we predict that it will have a competitive market share by 2020”. –

Gartner, IT Market Clock for Programming Languages, 2018, Mark Driver, Thomas Klinect, 24 April 2018

Why Kotlin?

Application developers have chosen Kotlin because the code is safer, leading to reduced errors and bugs. With Kotlin, the code base shrinks and increases in quality. Kotlin is equipped with many powerful features that speed up development including:

o Object declarations
o Parameter values
o Extension functions
o Null safety through nullable and non-nullable types, safe calls, and safe casts.
o Extension functions.
o Higher-order functions / lambda expressions.
o Data classes.
o Immutability.
o Type aliases

Migration from Java to Kotlin is easier with Kotlin. Since Kotlin is fully interoperable with Java code, this allows developers to gradually migrate from Java to Kotlin. Due to a reduced number of code lines, Kotlin saves time and the quality of code is increased. InfoWorld reports that Kotlin reduces the lines of code by 20-30%. Kotlin also supports REPL (Read-Eval-Print Loop) like Python, which actually helps developers to quickly run a part of code directly without actually running the whole application. REPL comes in the JetBrains IDE under the tools menu.

Key Features

Kotlin has an abundance of features which speed up the development time. Several features cover extension function, default and named arguments, inline functions, data class, companion object and more. Kotlin provides a clear and compact codebase that makes the code in production more stable and consistent. Bugs get detected at compile time, so developers can fix errors before runtime. In addition, Kotlin is not just limited to the Android platform. The language has proved that it’s a great fit for developing server-side applications, allowing a programmer to write concise and expressive code while maintaining full compatibility with existing Java-based technology stacks and a smooth learning curve.

“Null-safety” is one of the most popular key features of Kotlin. Nulls are the most error-prone points when working with Java. In Kotlin, nulls do not exist unless otherwise stated. No variable by default can be set to ‘null’ in Kotlin. If a developer wants a variable to accept ‘nulls,’ they have to mark the type with a ‘?.’ From then on, the compiler will force the developer to check the ‘null’ before doing anything with the variable. Due to this, a ‘Null Pointer Exception’ does not occur in Kotlin. Other than this, it has ‘let’ and ‘Elvis operator’ which comes handy when the code inside the let expression is executed only when the property is not null. Thus let saves us from the ‘if else’ null checker too!

To date, only a small number of applications are using Kotlin on Google PlayStore yet most of the developers are shifting towards it. These applications include Twitter, Pinterest, Netflix and others as mentioned on the Developer site of Android. Many external libraries are also adopting the Kotlin language.

Kotlin can make an Android Developer’s life a lot easier. You can also get many Sample apps on the Codelabs developer site by Google with step by step development. In short, you should consider implementing Kotlin in your current or next Application.

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