Transform your Organization with Edge Data

1. PRESCIENT WHITE PAPER Transform Your Organization with Edge Data

2. TRANSFORM WITH EDGE DATA |1 Aboutthispaper Asmore organizations are seeking waystoprofit fromtheir edge data,manylooktowardswaysto accelerate their edge data applications. Thiswhite paper guides business and technology decision-makersthrough the market potentialof edge computing, addressesthe common challenges businesses face during edge data projects, and ways businesses canovercome these obstaclesusing Prescient Designer. Written by Annie Xu, Andy Wang, Ph.D., Doug Levin

3. TRANSFORM WITH EDGE DATA |2 Whatis edge computing? Why is edge data on the rise? The foundationaltechnologyunderlying edge computing consistsof computers,network equipment,sensors, and data that are located “atthe edge”of a company’s cloud and physical computing infrastructure. Thisisoftenwhere products are manufactured,utilities are maintained and distributed, According to IDC,70%of enterpriseswillrunvarying levelsof data processing atthe edge. With thisshiftin focus,organizationswillspend over$16B on IoT edge infrastructure. Thisis driven bythe desire to accelerate value generation fromthe data of their connected devices. 70%of IoT deploymentswillinclude AI solutions for autonomousor edge decisionmaking. As a result,the edge computing marketis expected to expand to$64B by2025. and where professionals and personnel are consuming information and supporting many decentralized or distributed functions. Edge devices have sensing, computing and connectivity capabilities,manufactured forindustry 4.0smart factory equipment, assettracking and monitoring,predictive maintenance and manymore applications. These capabilities are puttogetherin different combinations by hardware manufacturers, resulting in a dizzying arrayof choices. Furthermore, $64B Estimated spending by2025(IDC) data acquired from edge devices are analyzed and $40B connected through networks fromthe edge tothe company’s data center and the cloud. 16.4% CAGR Spending on Edge Computing In2022

4. TRANSFORM WITH EDGE DATA |3 WAYS TO TRANSFORM YOUR ORGANIZATION WITH EDGE DATA Edge data pipelines Edge data pipeline referstothe processing of real-time data coming from data sourcessuch assensors, cameras, and machinesto extractinsightsorload into data warehouses, AI enginesor BI tools. Anoil and gasrefinery extracts, cleanses,transforms, and contextualizes data from150,000+sensors and feedstheminto an AI engine tooptimize operations. A warehouse robotics companypullslog data from 1500+robotstotrackrobotstatusinformation. A chemicalinstrumentation companypulls both instrumentation data and usermeasurement data to monitorinstrumentusage and generate alerts for abnormaloperations. Common challengesthese customers face The solutiontheywantincludes Howthey benefit from edge data Customers cannot deliverreal-time data totheir AI engine,limiting itsperformance Being able to extract150,000 data tags directly atthe edge Fastimplementation, automatic data ingestion and transformation Customers find it both time-consuming and error-prone to build data pipelinesmanually Fastimplementation,minimizes errors, improvesplantoperations Being able to filterout bad sensor data as well as detect and identify faultysensors Fastimplementation, autonomous, and improves DataOpsperformance Customers find integrating differenttypesof data fromvarying data sources a painfulprocess Being able tointegrate with other data sourcessuch aslab data, PowerBI and ERP

5. TRANSFORM WITH EDGE DATA |4 The role of the modern edge data stack for edge data The Modern Edge Data Stack While edge data pipeline soundslike ETL (Extract, Transform, Load),itisquite different fromthe traditional enterprise ETL. Unlike structured enterprise data, edge data isunstructured, uncontextualized,unreliable, and requiressignificantlymore data analysis. For example,sensors canmalfunctionor become uncalibrated overtime,therefore sophisticated detection algorithmsneed to be applied to find these conditions. Forthese reasons,organizationsshould followthe Modern Edge Data Stack(MEDS) frameworkto build edge data pipelines. The right edge data pipeline solutionshould support data protocols from common edge hardware and data sources, enable flexible data acquisition, cleansing,transformation, and contextualization capabilities,scale tolarge, distributed edge data systems, and are compatible with modern data warehouses and BI software. Analysis Extractintelligence Visualize Analyze Decide Data Warehouse Make data available Local Presistent Access Contextualization Make data meaningful Context Mapping Feature Extraction Transformation Ensure data consistency Integration Conversion Lineage Cleansing Ensure data quality Validation Detection Correction Acquisition Drag-and-drop connectors OPC-UA Modbus MQTT API Edge Hardware Compute atthe edge Controllers Gateways IPCs GPUs Data Sources Extract from anysource Sensors Machines Databases Files

6. TRANSFORM WITH EDGE DATA |5 WAYS TO TRANSFORM YOUR ORGANIZATION WITH EDGE DATA Data quality management With the proliferationof massive numbersof edge data sources and applications,managing these data sources has become a significant challenge. A major HVAC companyis connecting itsindustrial HVACstothe cloud. These HVACs have been builtinthe lastseveral decades, and the data coming outof them can have different format, data rate, and accuracy. A method is As expected,the data qualitymanagement challenge will further exacerbate inthe coming years asmore edge data sources are installed. The solutiontothis challenge is touse a data qualitymanagementsoftware. A data qualitymanagementsoftware allowsuserstoproduce high-quality, consistent data fromlarge numbersof diverse edge data sources. A good data qualitymanagementsoftware allowsusersto easily define data models,validationrules,transformationrules and thentakes care of data validation and transformation automatically. It cansignificantly ease the painof managing and transforming edge data. needed to automaticallyidentifyover1000 data schemas and convertthem into a homogenized schema. A leading automotive equipmentmanufacturer collects data from manufacturing robotstoimprove its equipmentperformance. The data schema fromthe robots can be changed bythe humanoperator,which can potentially disruptthe analytics. A method isneeded to automatically detect data format changes and ensure a consistent data schema forthe analytics. Common challengesthese customers face The solutiontheywantincludes Howthey benefit from edge data Customers don’t have sufficientsoftware developersto supportthe complexityof data qualitymanagement Simple, can be done by operationsteammembers Being able to create and manage thousandsof different data schemas Simple,unified workflow, 12x fastertoimplement Customers find data management difficultto implementwithoutstrong software expertise Being able to homogenize and manage data schemas from different data sources Customers find it challenging to build advanced analytics and AI functions Being able tointegrate advanced analytics and AI inthe same workflow Enable advanced functions and future expansion

7. TRANSFORM WITH EDGE DATA |6 WAYS TO TRANSFORM YOUR ORGANIZATION WITH EDGE DATA Low-code DataOps Edge data sources are very diverse and there are many different data protocolsinvolved, edge data solutions require the flexibilityto acquire data from differentsources and integrate with different hardware/software systems. Low-code DataOpssoftware isincreasingly a preferred solutiontosupport complex data integrationtasks atthe edge. Whetheritisloading data sourcesinto analytics,or integrating results coming outof analytics,low-code DataOpssoftware can accomplish these operationsquickly, automatically, and withoutrequiring technical expertise. With the wide applicationof these edge data projectsin almost everyindustry, edge computing has become a focalpoint for companiesmoving forward with digital transformation. The challengesin designing and deploying these and manyother projectsmake the case for a prescientsolution featuring low-code edge data design and automation capabilities. Anoil and gas AI solutionrequiresinput data from sensors, PI Historian,lab measurement, Power BI, and ERP. Anenergy AIsolutionrequiresinterfacing with sensors, automation controllers, Variable Speed Driver, and other typesof equipment. Common challengesthese customers face The solutiontheywantincludes Howthey benefit from edge data Customers find traditionalsoftware implementationtime- consuming and scaling end applicationsvery challenging Being able topost-process AI inference resultsto extractthe specific features for each end application Very fasttosupport different end applications; can enable customersto build theirown end applications Improvesperformance, canscale tomany AI use casesthatrequire differentmodels Being able to automate AI modeltraining and deployment formultiple models Customers find the AI modeltraining and deploymentprocess manual,slow and unable toscale whentheyneed many AI models

8. TRANSFORM WITH EDGE DATA |7 ④ Afterthe projectinception, businesses encounter difficultiessourcing partsof the infrastructure from differentvendors. 7 common edge deployment challenges The lackof interoperabilityof these sourced partswith the company’s existing operations and informationaltechnology components create an extra layerof frictionin edge data projectimplementation. Overthe years,the Prescientteamspoke with hundredsof cloud and software architects, DataOps and data engineers,projectmanagers, IoT systemintegrators, and senior business and technology decisionmakersto discoverwhy65%of edge and IoT projects are unsuccessful and whyonly12%of successfulprojectswill experience the fullrange of benefits fromtheir edge data. We concluded the challengeslie inthese 7 factors: ⑤ Businesses encounter challenges associated with processing and acting onvoluminousquantitiesof data. Extracting insights fromunstructured and noisy data fromthe edge can be complex and present challengesto data engineers and analystswhowant an easier and fasterwaytoworkwith data. ⑥ Many edge data solutionsleave the “lastmile”of edge data application developmentuptothe end user. ① Edge data solutions are negativelyimpacted bythe absence of a well- defined projectroadmap and budgetwith a clearsetof objectives. Byshying away fromimplementing an end-to-end edge data solutionthatputsthe end user and customers first, businesses end upmissing outonthe keywayto capitalize ontheir edge data project. ② Traditionally, edge data deploymentsrequire a massive teamto implement complex and customized solutions. ⑦ Continuousintegration and continuous deployment isoften difficulttomaintain. This caninclude edge hardware integration and signalprocessing specialists, software architects, engineers and developerstoutilize SDKs, containers, code repositories, corporate networksto helpmake continuousintegration and continuous deployment(CI/CD)successful. Itwill alsorequire the involvementof cloud architects, DevOps data engineers,security architects and projectmanagers. Edge data projects growout-of-date quickly due tothe quick advancementsin edge computing technologies. Businesseswho cannotiterate and optimize their edge data solutionsquicklystruggle tostay competitive even aftermaking huge investmentsintotheir edge data projects. ③ Edge data ofteninvolves a delicate balance of public,private and hybrid cloud activities. Most businesseswill encounter a numberof these challenges,leading edge data projectsto deploylate,or fail afterrelease. These staggering numbersonly suggestthat aninnovative solutionneedsto be established,which recognizes these challenges and can helporganizationsovercome these obstacles. Cloud-only data solutions cannotmeetthe needsof companiesthat are concerned aboutsecurity fortheir edge data. A significantportionof enterprisesstillrequire data and computing tostayonpremise. Moreover, edge processing reduceslatency, cost, and bandwidth totransmit data tothe cloud.

9. TRANSFORM WITH EDGE DATA |8 7 ways to combat edge data challenges with Prescient ④ Prescient Designer gives Node-RED users an enterprise option. Our edge data solutionsplatformis based on Node-RED,one of the mostpopularopensource options for building edge applications. It’sused bythousandsof integrators and engineers, and adopted bymajor corporationslike IBM, Siemens, Schneider, Intel and more,making itone of the mostreliable and battle-tested foundationsto build on. Prescient’s engineering team developed significant capabilities beyond Node-RED’s basic tooling functions,making it a preferable option for those who are familiarwith Node-RED, butwanttouse it fortheir businessoperations. ⑤ ① Prescient Designeris builtwith enterprise-grade security. Prescient Designermakes data automationsimple with low-code. Our enhanced security and enterprise reporting and auditing featuresmonitorthreats and changesinthe application code inreal-time for all devicesinside the entire system. Our proprietarysecuritytechnologysealsoff edge devices and allows for content-based application monitoring and complete tracking of every actionthat happensinthe system for every device forunprecedented security and observability. Our graphical approach to edge computing buildsonthe increasinglypopularmethod of low-code programming toreduce or eliminate software development complexity. By using symbolic and graphicalwaysof representing sensor arrays, edge devices,network and cloud computing resources, Prescient Designermakesit easy forusersto build edge data solutions,visualize self-documenting workflows, and enable themto focuson creating theirsolutionratherthan figuring out howto code it. ⑥ Prescient Designer enables continuousinnovation. ② Prescient Designer hasseamless edge-to-cloud integration. Using data workflows and automated orchestration, Prescient Designer bridgesthe gaps between development and operation activities and shortensthe technology development cycle, reducing time required between experimentationtooptimizationto deploymentstages. While it istypically a challenge to build efficient and flexible CI/CD pipelines for edge data systems, Prescient Designertakes care of the automation behind-the-scenes,sothe useronlyneedsto focuson her edge data application development. Most businessoperationsthatuse edge data need both edge and cloud computing. Our platform eliminatesthe usual and customaryruntime errors and cloud/data center complications by giving users controlover both cloud and edge processes. Users can build data workflows and deploythemtoruninthe cloud or atthe edge,with secure data communicationtaken care of behind-the-scenes. ③ ⑦ Prescient Designerlets enterpriseskeeptheir data atthe edge. Prescient Designer helps businessesview and actinreal-time. For customerswhowanttokeeptheir data securely atthe edge, Prescient Designer enablesthemtoworkonthe data atthe edge,notinthe cloud. Customers have the best of the both worlds–they can build data workflowsinthe cloud,which makesit easyto workon, collaborate, and manage, and then deploythe workflowstorun atthe edge, which makesthe data secure. Our drag-and-drop approach meansusers can build sophisticated functionality, access and visualization. The dashboard includesinteractive components and letsuserstake actionwhen needed,whether forthe physicalwarehouse orthe data warehouse. These can be integrated seamlesslywith edge and cloud applicationswithoutneeding to build separate APIs. Our platform also gives full customizabilitywith HTML/CSS forpowerusers.

10. TRANSFORM WITH EDGE DATA |9 Reap the benefits of edge data The demand for edge data solutionsrange acrossindustries, and those who have succeeded with their edge data projects can capitalize ontheir results. Ease-of-use and deployment-readinessof edge devices and edge data applications are stillmajor challengesthatkeep businesses from unlocking their edge data insights. Prescient addressesthese common edge data challengeswith an agile and flexible solutionthat helps organizationsreapthe benefitsof edge data solutions after deployment. We are here to answeryourquestions aboutyourunique edge data needs. Reach outtous atwww.prescientdevices.com/get-a-demo