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Cloudwick TechnologiesEnabling Digital Transformation using AWS AI and ML services with Amorphic Data

Enabling Digital Transformationusing AWS AI and ML serviceswith Amorphic Data

Every company generates loads of natural language text and imagesin the form of handwritten/typewritten text, emails, spreadsheets,reviews and comments, videos etc. There is a lot of intelligencehidden in these assets. With the advancements in deep learningbased AI algorithms this intelligence can be unlocked and madeactionable. This paper looks into three main areas (a) businessimperative to aggregate existing text, image and video data sources ina cloud data lake (b) augment existing applications and workflowsusing pre-trained AWS AI and ML services (c) two industry vertical usecases.

Add intelligence to applications and workflows usingAWS pre-trained AI and ML application services.




About Amorphic Data

Amorphic Data makes any data stored in Amazonsharable, searchable and analyzable by any analyticuser, data scientist or any data-driven applicationdeveloper using Amazon S3, Redshift, Athena,SageMaker Machine Learning and any open source orcommercial analytic or machine learning tools. These aresome of the core capabilities that make Amorphic Datainvaluable to the data-driven business.

About AWS AI and Machine Learning Services

For developers who want to plug-in pre-built AIfunctionality into their apps, AWS provides solution-oriented APIs for computer vision, and natural languageprocessing. These application services lets developersadd intelligence to their applications without developingand training their own models.

About Cloudwick

Cloudwick is a leading AWS machine learning andadvanced analytic services and ISV partner focused ondelivering cloud and machine learning analytic businesstransformation to the Global 1000. Leading enterpriseswork with Cloudwick to develop cloud and analyticmodernization strategies and to also implement, operateand manage modern cloud solutions.

Authors & ContributorsSukhbir Singh SethiShikhar Malik

June 2019




What is Data driven Digital Transformation ?In today’s world data is the new line of business (LOB) as it has the potential to transformbusiness models and create new sources of value. There is data generated fromcustomer acquisition process, sales and service interactions, production, operations,consumption, retention, customer/partner interactions etc. While companies mainly keepstructured data in repositories, bulk of company data is also generated in naturallanguage formats i.e. printed and pdf documents, images, worksheets, sales reports,logs, audio conversations, videos, social media streams, reviews and comments etc. IOTfurther brings in additional data sources by bridging the physical and the digital worlds.These data sources are either in a structured, semi-structured or unstructured formats.Advancement in digital technologies has allowed us to do more of storage and computeprocessing of data over the years. Using these data sources, an organization can seekto provide answers to many “Why” business related questions in the form of predictions ifproperly constituted and mined. Digitization, automation and data driven insights allow usto transform the way we live, learn and play.

Deep learning attempts to simulate the way our brains learn and processinformation by creating artificial "neural networks" that can extractcomplicated concepts and relationships from data. Deep learning modelslearn(s) through complex pattern recognition in pictures, text, sounds, andother data in order to produce more accurate insights and predictions.Deep learning frameworks have been applied in various fields likecomputer vision, speech recognition, natural language processing andrecommendation engines. AWS AI application services provide pre-trained deep learning models that have been trained on these fields.Companies investing in building use cases that leverage these ready-made capabilities provided by AWS ML and AI application services, cankickstart their digital transformation journey and leapfrog competitionwithout investing in deep AI and ML expertise.

Enabling Digital Transformation usingAWS AI and ML services withAmorphic Data




Where are the data driven Digital Transformation opportunities ?Digital Transformation consists of different business themes where use-cases touch morethan one theme. Exhibit 2 is a generic digital opportunity area landscape and can bethought of as a matrix of elements layered based on how they are physically placed inthe organization. Each layer has multiple elements and each element interacts with

elements in the same layer or with elements in the layer above or below. Each interactiontakes place through natural language audio/text conversations, documents, images orvideo. A lot of intelligence embedded in these documents and natural languageconversations can reveal insights and help transform any of the opportunity area themeslisted above. Four different business themes illustrated in exhibit 2 are listed below

1. Customer Experience (CX): Any improvement in the top half of the opportunitymap will lead to a better customer experience. Improvements to the bottom halfwill make the ecosystem more efficient. Inter-channel awareness and seamlesscustomer journeys will help improve CX. Customer journey is a series oftouchpoint interactions an end user performs to discover, purchase andconsume a company’s product or service. CX improvement is about predictingand shaping customer journeys for a personalized and proactive care asopposed to reactive care. Voice of Customer (VoC) is an important tool whichinvolves collection of solicited and unsolicited feedback from customers and theinsights to be incorporated back into business functions

2. Productivity/Efficiency: The scope of this improvement is a combination ofprocess, people and technology. Digital technologies allow processes to besimplified and collapsed through the lens of data thus bringing productivity/efficiency gains.

3. Digitization / Automation: this means tighter integration between differentelements of the physical world with the digital world. This in most cases is the firststep towards digital transformation. IOT is a key enabler for digitization.Digitization can further trigger automation of services to remove humanlatencies. Digitization also generates loads of data which can be further mined tobring more insights for improvement.

Exhibit 2Digital TransformationOpportunity landscape




4. Customer Service: it consists of traditional Self-service and Agent based servicewhen looking at things inside out. Traditional customer service transformationinvolves building new capabilities to improve self and assisted service.

Following sections will get into details of how companies can leverage AWS AI and MLpre-trained services to extract intelligence from natural language data assets stored inthe organization.

AWS AI and ML Services with Amorphic Data OrchestrationAWS provides a variety of frameworks, ML services and AI services (see exhibit 3), whichhold the potential to provide ready-made intelligence to your application and workflows.These services are designed to ease the process of putting machine learning workflowsin production. AWS AI services do not require machine-learning experience and aredeveloped to make AI more readily consumable.

In order to realize the full potential of these services and discover more insights from yourorganizations data, they can be coupled with other compute and storage servicesprovided by AWS like S3, Athena, Redshift, DynamoDB, Elastic Search, API Gateway etc.These building blocks can be stitched together and integrated in a modern data lakesolution offering more agility and flexibility than a traditional data management system.Amorphic Data bridges this gap by automating the process of stitching the data lake inconjunction with Analytics and AI components - making it easy for all users to access anduse their data, thereby removing the technology barriers for easy adoption and asuccessful business implementation.

Amorphic Data is the first cloud orchestration SaaS or Managed Service platform thatmakes it easier for IT, business and data science users to easily access and manageAWS AI and ML services. It supports the ingestion, transformation and analytics of allstructured, unstructured and semi-structured data specific to your use cases. It providescloud data, advanced analytics and machine learning services in a single pane of glassfor all users. Next section discusses how a company can build advanced analyticalcapabilities adopting a phased approach with AWS AI and ML services using AmorphicData orchestration.

Exhibit 3AWS Framework, AI andML Services

Source: https://aws.amazon.com/machine-learning




Phased approach to extracting business value from Data withAWS Data Lake and AI/ML servicesExhibit 4 illustrates building of analytical and AI/ML capabilities to extract business valuefrom data-driven opportunities.

A Data lake is required for staging structured/unstructured data to provide the underlyingdata plumbing for analytics and AI/ML applications. Descriptive capabilities are the mostbasic form of reporting and analytics. Diagnostic capabilities help perform root causeanalysis and predictive capabilities help answer “What will happen”. Prescriptivecapabilities help in decision support/automation to assist/replace human beings indecision making. These capabilities pose an increasing order of implementation andoperational complexity required for realizing business value. Sample use cases are listedin exhibit 4 along with each capability for easy reference.

In order to extract business value from the above capabilities, organizations mustundertake a phased approach to analytics maturity shown in exhibit 5 highlighting twotypes of activities in orange and grey squares for each phase. Orange activities are topdown starting from business and grey ones are bottom up starting with technology.

1. Phase 1- Identify and build: Any approach to extract business value from datawill start with a proper understanding of the problem before an attempt is madeto solve the problem. It is important to understand which customer journey oruse-case is important for digital transformation. A customer journey/use-casemapping [1] workshop can be conducted with the relevant departments to arriveat a detailed walk through of the challenges faced. In this workshop, data-drivenopportunities are identified with associated data sources which contribute to theparticular problem/use-case. A bottom up activity is now performed where a datalake is built and data sources ingested (AWS Kinesis) into the data lake built onAWS.

2. Phase 2 - Explore and Analyse: Once a journey/use-case is identified and datasources aggregated in one place, dashboards, visualization and analytictechniques (Path analysis, Journey analytics etc.) can be applied to explore theproblem/behaviour. Business analysts can query and analyze the data sets toexplore business relationships in the data from multiple sources. This phaseinvolves active dialogue between business and data teams to arrive at the rightconclusion that can be used for downstream ML analysis.

Exhibit 4Building AdvancedAnalytics and AI/MLcapabilities




3. Phase 3 - Train and Prototype: Data pipeline is built to train data models from thedata ingested into the data lake using AWS ML Sagemaker. Design Sprints [2] canbe conducted to short-cut the learning curve through prototyping exercises. Futurestate workflows are mocked to gain learning before putting AI/ML assistedworkflows into production.

4. Phase 4 - Deploy and operationalize: The last step of this process is productiondeployment and change management. This involves Dev Ops integration tooperationalize the new use-case business workflows. Phase 4 can also pre-emptearlier phases when AWS pre-trained Deep Learning models are leveragedprovided as API service by AWS AI application services.

Exhibit 5Phased approach tobuilding data-driventransformationcapabilities with AWSData Lake and AI/MLservice

Industry Use casesIn this section we walk through industry use-cases to see the how AWS services can beused to solve real-world problems. We study two cases to demonstrate how an existingbusiness or a start up can leverage AWS AI/ML services to build AI/ML capabilities to helpaddress business challenges in their respective domains. First use case is fromhealthcare while the second one is vehicle insurance. The idea of this section is more toeducate potential users of AI/ML services on numerous possibilities where these servicescan add value.




Use case 1: Improved patient care with AI/ML assisted ChronicDisease managementLet’s look at a real-life use case of how a healthcare startup can build a chronic diseasemanagement offering for diabetes using AWS AI and ML services along with AmorphicData orchestration service.

Although healthcare digitization has eased the storage and management of data,streamlined processes and helped in improving patient outcome, this process ofimproving patient care has largely been restricted to acute conditions, which are suddenand severe in onset. A chronic condition by contrast is a long developing syndrome suchas Diabetes or Asthma, which may lead to further complications in the later stages ofpatient’s life. Chronic conditions require holistic patient care approaches throughout apatient’s journey. Holistic care solutions are particularly important for people with chronicconditions as patients are at the increased risk of serious complications. Poor diabeticmanagement can lead to more complications like cardiovascular, renal, neurologicalconditions etc.

HealthXYZ Inc, a healthcare start up, has decided to build a cloud based offering with thefollowing vision “To provide superior chronic disease management and care solution fordiabetes using digital technologies”. HealthXYZ has put forward four clear objectives fortheir product/service

1. To provide a single repository for diabetes patient journey data I.e. from wellness,prevention, diagnostic and treatment phases typically siloed across healthcarepractitioners and service providers.

2. Patient focus with adequate human or machine intervention guided by data-drivenclinical intelligence.

3. Better healthcare access and delivery with analytical insights derived from datacollected across patients.

Exhibit 6A typical diabetic patientlife journey




Mapping the Patient Journey for areas of opportunityA cross-functional stakeholder workshop can reveal a diabetes patient journey map. Adiabetic patient journey will cut across different healthcare stakeholders in the patient carecontinuum. Both structured and unstructured patient data will get generated and stored ininformation silos along the journey. With disparate sources of information available acrossdifferent stages of the patient journey, any technology solution should provide the facility toingest these data sources on a single platform for a single source of truth. In this process ofstitching together these data sources, AWS AI services can be utilized that extract relevantinformation from free flow text data. For example, patient info in the form of writtenprescriptions, medications, blood and X-ray reports, hospital discharge summaries etc. havenatural language clinical information. Exhibit 6 highlights a sample diabetes journey map withpain points and opportunities for improvement using technology automation.

Technology Set upHealthXYZ decide to bring consumers onto their platform through a mobile app throughwhich patients can input their medical information including prescriptions and reports. Thisapp will be connected to the cloud based AWS back-end to consolidate all patient info in adata lake.

The back-end AWS setup for HealthXYZ is illustrated in Exhibit 7. AWS Comprehend Medicalis an AWS AI service that detects useful information in unstructured clinal texts. It usesNatural Languages Processing models to retrieve relevant information from unstructured textdata. AWS Comprehend Medical extracts relevant text and classifies them into differentcategories - medical condition, anatomy, test treatment procedure, medicine etc.

After ingesting various data sources into the AWS platform we can develop continuous andholistic view of how patient is interacting across the various touch points. This can helpidentify the opportunities for improvement and provide timely intervention for better patientoutcomes. Some features of the service are listed below.

AmorphicData inaction

Exhibit 7AWS set up with AmazonComprehend Medical(Extracted MedicalInformation)




Patient mobile app for monitoring and alertsHealthcare ecosystem is very fragmented and the onus to stitch the context across theecosystem is on the consumer/patient. HealthXYZ wants to use the mobile app as a singlepoint of engaging with the ecosystem. Also, mobile app plays the role of inputting documentsfrom primary/secondary physicians, vital measurements, lab reports, discharge summariesinto the AWS cloud for a single source of truth about the patient. Mobile app can become anentry point for written doctor prescriptions, pharmacy bills, blood sugar measurements etc.Once the data from every patient is at a single place a journey map can be plotted for bettercorrelation between siloed sources of information. Medication alerts, well ness monitoring,doctor appointments, follow-up interventions can be monitored and enforced from the app.

Diabetes Complication and Hospital Readmission Rates PredictionOnce a journey map is plotted, it can be utilized to identify opportunities that improves patientcare for chronic conditions. For example, diabetes and pre diabetes conditions often pair withuntreated depression which can impact patient’s self-efficacy and can make caremanagement difficult. A depression prediction model can thus help in proactively screeningpatients for depression by using various variables like patients who are not making anappointment, not filling medications or not following up on referrals etc. A patient’s sugar test,detailed lab test and BP measurement can also be used to develop other diabetescomplication prediction models like the onset of retinopathy, neuropathy and nephropathy at3, 5 and 7 years from first visit at the clinic/hospital/center for diabetes.

These prediction data sources as well as data from patient emails and text messagesextracted via Comprehend Medical AI services, and structured data sources ingested froman EHR can also be used to predict hospital readmission rates. Once a patient with anincreased risk of readmission is identified, intervention can be developed to provideadditional assistance to the patient.

Drug Utilization Review (DUR)Managing patients with type 2 diabetes often involves the use of multiple drugs and deliveryforms. Optimizing drug treatment requires frequent monitoring of glucose levels, especiallyfor patients whose dosage requirements might change as they adopt lifestyle modifications.Due to the various complications that can arise in a chronic disease like diabetes, properimplementation of drug utilization review programs specifically catered to such chronicdisease is very important. So, a DUR becomes an important part of a patient’s journey andpharmacist or medical practitioners should use a storage and management system that is inline to an integrated approach to manage the various processes involved in a DUR. Thetechnology should also have historical cataloging for retrospective DUR and also have thecapability to set remote monitoring targets as and when required.

Due to variety of formats in which patient data is stored - PDF, Images, Text, CSV etc, anytechnology solution that helps in the integration of these sources, should have the ability toleverage AI services that can extract relevant information from them. While customizedprediction model trained on specific disease data like diabetic complication, can bedeveloped by using AWS ML services. This technology solution should not only ingest thedata sources but also be able to consume these AI or ML services and perform ETLtransformations and joins as and when required. This technology should help organizationsreach the goal of moving towards holistic patient care models for chronic conditions. The newcare model can help providers be responsible for payments that are based in part onoutcomes such as avoiding hospital readmissions and reducing complications through earlydetection of risks. At the same time, additional financial assistance can be provided topatients with a higher risk of hospital readmission. Thus, any technology solution that getsused in the back end should be in line with these models with the ultimate goal of reducingpatient cost for chronic diseases.




Use case 2: Faster insurance claims processing with AI/MLassisted Vehicle Damage assessment.If a customer is involved in a car accident, it is important to assess the damage for carinsurance purpose. But filing a car insurance claim (see exhibit 8) can be an unnervingtask and can make a customer uneasy if it is his first claim and he does not know what toexpect. After an accident happens, the process of inspection can lead to unnecessarilydelay in repairs especially when the customer has financial impediments and would like toget the claim estimates right away. The customer needs to be available to answerquestions, the claim adjuster need to be available to assess the damages in person andthe body shop needs to coordinate with both the customer and the claims adjuster.Whether your car gets repaired quickly plays a larger role in determining whether you aresatisfied with your insurance carrier or not. This process seems to be fraught with delaysand thus it provides opportunities for transforming the claims process using digitaltechnologies.

In order for an effective digital transformation process, a proper understanding of theproblem needs to be done before an attempt is made to solve the problem. Our objectiveis to speed up the rental car insurance claim and repair process. Is it possible to changethe existing workflow to turn around the damage assessment faster?

Exhibit 9 shows a simplified journey map of a rental car accident. On the left it has 3personas I.e. Customer, rental company and insurance/repair company. The map is selfexplanatory showing key phases I.e. Pre-accident and post-accident with actions/stepsperformed by various personas.

Exhibit 8Existing insuranceclaims process

Exhibit 9Simple journey map ofExisting insuranceclaims process andtarget problem area




In the journey map, a target problem area to be solved was identified as the damageassessment step shown with red circle in exhibit 9. HMW (“How might We”) technique cancome in handy where different improvement areas are articulated in the form of HMWstatements. In this case an HMW “Digitize and automate damage assessment for fasterturn-around of claims processing” is used to guide the solution design and prototyping.

Technology to solve target problem areaAWS AI and ML services can be useful in transforming the process of insurance claims.The native pre-trained solutions are either available as an API call or they can besubscribed from AWS Marketplace. AWS Marketplace contains various pre-trainedalgorithms and model packages uploaded by other developers so that customers canconsume these services to build solutions and run their businesses.

In order to reduce the turn-around time for vehicle damage assessment and claimsprocess, the insurance company needs to digitize and automate the damage assessmentstep for a faster turnaround. The technology solution used to solve the problem shouldtake the vehicle accident images as inputs and provide the damage assessment to thecustomer without the need of personal inspection by the agents. There can be manyunstructured data (vehicle damage images) and structured data sources (car make,model and part costs) that can be used in the process. Once all the data sources areingested into the Amorphic Platform, we can connect these data sources to pre-trained MLsolutions from AWS Marketplace to solve our problem. Exhibit 10 illustrates the AI/MLassisted vehicle damage assessment pipeline thus eliminating the need for physicalinspection for faster claims processing. This pre-trained model will take the vehicledamage images provided by customer as an input and identify the category of damagesto the vehicle – windshield, frontal damage, side damages etc. Using Amorphic Data ETLcapabilities, these predicted damage categories can be connected with the car make,model and part cost data available in the CRM system. This process can help arrive at thedamage claims for an accident without the need for physical inspection of the vehicle.

The pre-trained models available with AWS can thus help simplify the claims process. Theinsurance agents can track the real time damage claims and accidents filed by thecustomers. The agent can track the total cost by manufacturer and model on a real timebasis. Because of the availability of the data, ETL and machine learning components all inone single platform, the insurance claims process is streamlined and made timely for boththe consumer and agent.

Exhibit 10AWS AI/ML assistedvehicle damageassessment pipeline

AmorphicData inaction




ConclusionThis paper demonstrates how companies can start their AI/ML journey without investingin data engineering and data science resources. Organizations have information storedin the form of text, images, video, pdfs etc.. which can be stored in one place and madeshareable and searchable across the organization using a self service AWS data lake.Further, AWS AI and ML services can be leveraged to make use of pre-trained valuemodels to add intelligence to existing applications and business workflows. All this canbe achieved without investing heavily on data engineering and ML resources. Amorphicdata alongwith AWS AI/ML services makes organizations less dependent on IT and bigdata resources to get the benefits of AI and machine learning.

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References[1] HBR article by Adam Richardson, Using Customer Journey Maps to Improve

Customer Experience, November 15, 2010. https://hbr.org/2010/11/using-customer-journey-maps-to

[2] Sprint: How To Solve Big Problems and Test New Ideas in Just Five Days - JakeKnapp, John Zeratsky and Braden Kowitz from Google Ventures. https://www.gv.com/sprint/

[3] Deloitte paper, Turning the tide on diabetes management - How leaders in healthcare are using multi-faceted approaches. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/life-sciences-health-care/us-dchs-diabetes-management.pdf

[4] IEEE paper by Raid M. Khalil ; Adel Al-Jumaily, Machine learning based predictionof depression among type 2 diabetic patients. https://ieeexplore.ieee.org/document/8258766

[5] Machine Learning Methods to Predict Diabetes Complications. https://www.ncbi.nlm.nih.gov/pubmed/28494618

[6] Machine Learning on AWS, https://aws.amazon.com/machine-learning/

Sukhbir Singh Sethi is a Digital Transformation consultant based out of Bangalore, India.His passion includes bridging the divide between Business and Technology bydemonstrating business value of Digital Technologies. He works with customers acrossmultiple industries to help them carve out business-technology roadmaps and maximizebusiness benefits from technology enabled initiatives. Sukhbir can be reached [email protected].

Shikhar Malik is a Data Scientist in Cloudwick Technologies based out of Newark,California. He is passionate about using statistical and ML techniques to solve abstractbusiness problems facing organizations. He has worked with clients in telecom, financialconsultancy, and electronics manufacturing - solving a wide range of problems from churnprediction, AI chat-bot development, supply chain analytics and more. Shikhar can bereached at [email protected].