#DBS2016 Reaping the Rewards- Digital Business Uses of Data Science
Post on 13-Apr-2017
<p>PowerPoint Presentation</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>Julien Escribe, Partner, ISG</p> <p>Reaping the RewardsDigital Business Uses of Data Science</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>2</p> <p>AbstractData science in all its forms data learning and mining, predictive analytics, guidance for prescriptive decision-making, machine intelligence, and autonomous systems is being trialed, tested and deployed for both machine- and people- related business applications, including the consumer and industrial IoT. This discussion focuses on strategic, tactical, and operational business applications of data science that change businesses and markets.</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>Digital Business is Powered by Data ScienceThe Concept: Data science accelerates business results by using intelligent digital machines and processes" to further automate market/customer and supply chain interactions, while augmenting human labor & decision-making.Key Enablers today: Data unique to the enterprise, data external to the enterpriseData mining/machine learning development platformsIntelligent machine processing (IMP)Natural Language Processing (NLP)Robotic process automation / autonomic process automationAdaptable/Reusable IMP software with Spark/Python/scripting/PMML/PFA formatsHumans and business processes</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>The basic concept of the uses of data science in the enterprise include the automation of decision-making and routine business processes. This session will covers some of these uses, the broad market trends we see occurring, and where the business value from these uses are occurring.4</p> <p>AgendaDefinitionsMarket TrendsUse CasesBest Practices</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>What is Data Science?</p> <p>Dev-Deployment Platforms: todayDev-Learning Platforms: todayData Mining &Machine Learning</p> <p>IMP + NLP+ RPA + AP</p> <p>Forecast: years from nowMachine Intelligence& Reasoning (MIR)</p> <p>Natural Language Processing: e.g., the human IMP interface, including speech, vision, narrative, sentiment analytics, facial recognition, text, OCR, etc.Machine Learning: e.g., machines train on data and are used to construct algorithms for learning and making predictions on dataData Mining: e.g., people (data scientists) discover patterns in data using supervised, unsupervised, reinforcement, deep, neural networks and then reuse/transform for further useIntelligent Machine Processing: e.g., machines that have been trained and programmed on data by DM and ML platforms for production use. May contain NLP, RPA and AP.Robotic Process Automation, e.g., the production uses of IMP, NLP, and/or RPA To augment/replace a machine/business processAutonomic Processing: the production uses of adaptable computing modelled on the human immune systemCognitive~Machine intelligence and reasoning: e.g., thinking, reasoning, adaptable, and self-learning machines modeled on the human brain/processing</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>First, wed like to cover some basics by defining what were talking about. The field of cognitive computing is very broad, and encompasses data science. Today data science covers the realms of data mining, machine learning, intelligent machine processing, robotic process automation and autonomic process automation. </p> <p>The larger field of cognitive, including artificial intelligence, is on a quest of machine intelligence, where machines think and reason independently.</p> <p>The reality is that today, and for the next decade or so, applications of data science in the enterprise will be leveraging whats working today with the technology, as well as advances that arise from the research labs and academic centers around the world.6</p> <p>What are the Business Rewards? Insights and foresight will shape outcomes and industries Within operating functions, across operations, governance functions, tactical and strategic functions of the organizationAccelerated business performance & industry shaping possibilitiesSmart self service powered by machine automation and dataFaster market responseImproved supply chain efficiencyDiscovery and ability to take action on unseen profit pools hidden in silos of dataIncreased operating efficiencies</p> <p>Next generation digital business capabilitiesDiscovery and leverage of value from data-driven digital business processesDigital business processes driven by data from within and outside the companyNew possibilities for the organization Man + Machine combination = next-level digital business capabilities </p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>The business rewards of using data science include increases in performance result, the ability to reshape industries, and increased operating efficiencies: in short some competitive advantages.</p> <p>Enterprises choosing to not participate in leveraging data science for the business will fall behind</p> <p>Using the technologies of data science will not confer competitive advantages though.</p> <p>It is where data science is focused, for which business purposes, using data that is unique to the enterprise, and for which business models, that will help define competitive market advantages.7</p> <p>How Real Is It Today?Survey Data: > 1/3 of Firms Have the Core Tech in ActionSurvey Question: Rank the following emerging technologies on a scale from Not Deployed to In Production as they are in use today.Source: ISG Insights 2016 global IT leader survey; n = 352The Deployment Reality</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>We thought it might be useful to see how real data sciences are today. Based on our most recent market research conducted in August, about one-third of enterprises have the core technology of data science in production.</p> <p>About another one-third are trailing and testing data science applications with plans to deploy the applications. </p> <p>Those not planning to use data science capabilities are in the minority or in industries where those uses such as robotics do not apply.</p> <p>8</p> <p>Vertical industryplatformsDigital labor/operationsplatformsEvolution of Data Science in the Enterprise2000 2005 2010 2015 2020 2025 2030 2035 2040BusinessoptimizationplatformsDevelopment tools & platformsTimeMarket impact</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>Based on our research, we are forecasting a rapid adoption of data science uses in one of four major categories:Development platformsDigital labor and operationsBusiness optimizationVertical industry application</p> <p>And, we expect this market formation will develop rapidly over the next 5 years, and fully mature within the next decade.</p> <p>In terms of value, we expect industry-specific uses and applications will drive the most reward for enterprises, followed by uses of business optimization platforms.</p> <p>What is interesting to note is that getting to these higher-order value / reward uses of the data science will require enterprises to travel though development platforms for two significant reasonsThe data and machine learning are being development todayData science is the new Dev Ops platfroms for digital business, learning from large troves and may types, of data 9</p> <p>Digital Labor/Operations PlatformsMachines augment/automate productivity and efficiency gains for operations.IT OperationsMarketing</p> <p>Customer service</p> <p>Regulatory</p> <p>...Sales</p> <p>Logistics</p> <p>Supplychain</p> <p>Finance/ HR</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>One of the platforms now being used in the market are those focused on automating labor and business processes. These show up / and are in use in every functional domain of the enterprise, from sales through supply chain and customer services.</p> <p>There are differences between these platforms: the operational platforms aim to accelerate decision-making for existing business processes, as do the digital labor platforms. In addition, some of the digital labor platforms make it possible for enterprises to recast / reengineer existing processes by augmenting current processes.</p> <p>We are seeing these platforms in use in every industry.10</p> <p>For IT operations, emerging solutions and service provider offerings are being deployed to improve operational awareness, provide straight-through processing for incident resolution, and shift-left for organizational effort dedicated to IT operations.Job MonitoringDB & MW MonitoringHypervisor MonitoringDesktop ManagementNext-Gen IT OperationsTicketing & Service DeskCMDBProcess AutomationNetwork ManagementAdvanced Event CorrelationUser Experience MonitoringCI Visualization (Faster RCA)Addl Process AutomationDashboard & ReportingDecision Engine (ML Self-Heal)Security MonitoringServer AutomationServer & App MonitoringBackup & Storage MgmtRTPaaS MonitoringProject Requirements & Outcomes:Mashups of various solutions/software>$$$ in labor/non-labor deployment feesScripting team writes knowledge itemsSix to twelve month deployments are typicalBusiness case savings of >20% of TCOExample of Digital Labor/Operations in IT</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>An example of this is the use of data science platforms for digital labor / operations for IT, where these uses are automating specific functions in IT to improve productivity.</p> <p>We are seeing these platforms in use in every industry,.11</p> <p>Intelligent machines discover profit opportunities from operational silos of data.Optimizes decisions to maximize pricing/market mix/customer yields/inventory/distribution/supply chain.Resources and costs are optimized.Market and profit opportunities are accelerated.Business Optimization Platforms</p> <p>Sales &marketingProductionSupply chainLogistics, distributionand customer serviceBusiness OptimizationPlatform results</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>Another platform now in use in the market are the business optimization uses that identify the hidden opportunities found in the silos of data throughout the enterprises, and then helping people to make informed / best case business decisions to alter revenue / profit / customer / partner / inventory mix to optimize business results. </p> <p>We are seeing these platforms in use in almost every industry.12</p> <p>Vertical Industry PlatformsIMPs augment human processes and decision-making to improve agility, efficiency, quality, customer retention, field trials, patient compliance, healthcare diagnostics for industry specific uses.OperationsLegal</p> <p>...Industrials,ProductionTransport</p> <p>Government</p> <p>Retail &Consumer</p> <p>BFSIInsurance</p> <p>CPG</p> <p>Media</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>In addition, specific vertical industry platforms are in use and will continue to flourish, as data sciences are used to address different business processes unique to each industry.</p> <p>Whether the industry is automotive, banking, financial services, government, telecommunications, transportation or any other industry, data science applications will become embedded into core business processes through these platforms. 13</p> <p>Example of Vertical Industry Platform: Fraud loss Detection/Prevention in Insurance IndustryFor agent/broker operations, protecting the companys loss ratios are more important than ever.Emerging solutions and service providers help companies protect their customers and investments against fraud by utilizing cognitive technologies. </p> <p>Claim Filed</p> <p>Storage</p> <p>AnalysisStop Payment, Report to Authorities</p> <p>Fraud</p> <p>Risk Portal</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>An example of a core business platform that is expanding beyond its beachhead is fraud loss. Initially this started in the banking industry, expanded into the wider financial services realm, including insurance, and is now almost universal in retail, telecommunications, utilities, government and many other industries focused on reducing fraud losses.14</p> <p>Sample IT Providers</p> <p>Open Source DevOp platforms Apache Spark, Apache Mahout, H2O, Kaggle, KNIME, LIBLINEA, MLLib, Octave, OpenNM,Orange, PredictionIO, R, RapidMiner, Tangara, Scikit-Learn, SciPy, Sense and Weka among others Commercial DevOp platformsActian, Amazon, Angoss, BigML, CognitiveScale, Dato, Dell, DigitalGenius, Fuzzy Logix, Google, HPE, IBM, Lavastorm, Microsoft, Oracle, Pivotal, Predixion, RapidMiner, SAP, SAS, Skytree and Tibco among othersDigital labor/Operations platformsActionIQ, ADP, Altryx, Aragon, Ariba, Aviso, Bottlenose, Clarabridge, Blueriver, Brightfunnel, ClearStory Data, Crimson Hexagon, Dataminr, DecisionIQ, Deep Genomics, DigitalGenius, Entelo, Everlaw, Gainsight, Framed, FICO, Fuzzy Logix, Honeycomb, IBM, iCIMS, Infer, Infor, InsightSquared, Inventure, IPSoft, JDA, Kana, Lavastorm, Liftigniter, Mattermark, Microsoft, Miradoor, NetMap Analytics, Oracle, Owlin, Predicsis, PTC, Quantfind, Qubit, Radius, Ravel, Salesforce, Salespredict, SAP, SAS, Sentient, Sense, SugarCRM, Taleris, Textio, Workday, Zaius and Zoho among othersBusiness Optimization platformsAdvanced Software Applications, Applied Predictive Technologies, ClearStory Data, FICO, IBM, R4 Technology, SAS and SmartOrg among othersVertical industry platformsIBM, Quid, RiskIQ, SmartOrg, Strategic Analytics and TrendSpottr among others</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>Here we are showing just some of the IT providers with products and services in each of these core platforms segments. Some of these providers are focused on just a specific core area, whereas others are focused on delivering products and services across multiple platform segments.</p> <p>A special note should be made of the development platforms: most of the emerging developments in data science are open sourced, and we expect this to continue in the future. Many but not all of the commercial development platforms make it somewhat easy and some make it easier to integrate emerging data science breakthroughs into their platforms.15</p> <p>Overcoming Some of the ChallengesSome of the challengesOvercoming the challengesLets just buy an off-the-shelf software packageData science is the new data-driven DevOps. It is not an off-the-shelf software package - yet.Data science finds the sacred cows of the businessUse its flashlights/floodlights to improve results. It is about people and process, more than technologyProcess reengineering is more difficult than the data scienceEmbrace it: data-science driven business is digital business process reengineeringFocus on what the data says is possible and not the businessDigital business is informed by data but it needs to be channeled by the business</p> <p> 2016 Information Services Group, Inc. All Rights Reserved. </p> <p>The...</p>
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