#dbs2016 reaping the rewards- digital business uses of data science

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2016 Information Services Group, Inc. All Rights Reserved.

Julien Escribe, Partner, ISG

Reaping the RewardsDigital Business Uses of Data Science

2016 Information Services Group, Inc. All Rights Reserved.


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.

2016 Information Services Group, Inc. All Rights Reserved.

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

2016 Information Services Group, Inc. All Rights Reserved.

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

AgendaDefinitionsMarket TrendsUse CasesBest Practices

2016 Information Services Group, Inc. All Rights Reserved.

What is Data Science?

Dev-Deployment Platforms: todayDev-Learning Platforms: todayData Mining &Machine Learning


Forecast: years from nowMachine Intelligence& Reasoning (MIR)

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

2016 Information Services Group, Inc. All Rights Reserved.

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.

The larger field of cognitive, including artificial intelligence, is on a quest of machine intelligence, where machines think and reason independently.

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

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

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

2016 Information Services Group, Inc. All Rights Reserved.

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.

Enterprises choosing to not participate in leveraging data science for the business will fall behind

Using the technologies of data science will not confer competitive advantages though.

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

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

2016 Information Services Group, Inc. All Rights Reserved.

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.

About another one-third are trailing and testing data science applications with plans to deploy the applications.

Those not planning to use data science capabilities are in the minority or in industries where those uses such as robotics do not apply.


Vertical industryplatformsDigital labor/operationsplatformsEvolution of Data Science in the Enterprise2000 2005 2010 2015 2020 2025 2030 2035 2040BusinessoptimizationplatformsDevelopment tools & platformsTimeMarket impact

2016 Information Services Group, Inc. All Rights Reserved.

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

And, we expect this market formation will develop rapidly over the next 5 years, and fully mature within the next decade.

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.

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

Digital Labor/Operations PlatformsMachines augment/automate productivity and efficiency gains for operations.IT OperationsMarketing

Customer service





Finance/ HR

2016 Information Services Group, Inc. All Rights Reserved.

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.

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.

We are seeing these platforms in use in every industry.10

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

2016 Information Services Group, Inc. All Rights Reserved.

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.

We are seeing these platforms in use in every industry,.11

Intelligent machines discover profit opportunit


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