#DBS2016 Reaping the Rewards- Digital Business Uses of Data Science

© 2016 Information Services Group, Inc. All Rights Reserved.


Julien Escribe, Partner, ISG

Reaping the RewardsDigital Business Uses of Data Science


Abstract

Data 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.


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 enterprise Data mining/machine learning development platforms Intelligent machine processing (IMP) Natural Language Processing (NLP) Robotic process automation / autonomic process automation Adaptable/Reusable IMP software with Spark/Python/scripting/PMML/PFA formats Humans and business processes


Agenda

1. Definitions

2. Market Trends

3. Use Cases

4. Best Practices


What is Data Science?

Dev-Deployment Platforms: today

Dev-Learning Platforms: today

Data Mining &Machine Learning

IMP + NLP+ RPA + AP

Forecast: years from now

Machine 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 data

Data Mining: e.g., people (data scientists) discover patterns in data using supervised, unsupervised, reinforcement, deep, neural networks and then reuse/transform for further use Intelligent 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 process

Autonomic Processing: the production uses of adaptable computing – modelled on the human immune system

Cognitive~

Machine intelligence and reasoning: e.g., thinking, reasoning, adaptable, and self-learning machines modeled on the human brain/processing


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 organization

Accelerated business performance & industry

shaping possibilities Smart “self service” powered by

machine automation and data Faster market response Improved supply chain efficiency Discovery and ability to take

action on unseen profit pools hidden in silos of data

Increased operating efficiencies

Next generation digital business capabilities

Discovery and leverage of value from data-driven digital business processes

Digital business processes driven by data from within and outside the company

New possibilities for the organization

Man + Machine combination = next-level digital business capabilities


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.

Robotics

Robotic Process Automation (RPA)

Machine learning / AI / Cognitive

Voice Recognition / Natural Language Processing

Data Science / Big Data

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

23%

22%

13%

13%

6%

18%

22%

21%

18%

18%

24%

24%

29%

32%

23%

23%

20%

23%

21%

29%

11%

12%

14%

16%

24%

Not Deployed, No Plans to Use Researching, But No Current TrialsSource: ISG Insights 2016 global IT leader survey; n = 352

The Deployment Reality


Vertical industryplatforms

Digital labor/operationsplatforms

Evolution of Data Science in the Enterprise

2000 2005 2010 2015 2020 2025 2030 2035 2040

Businessoptimizationplatforms

Development tools & platforms

Time

Mar

ket i

mpa

ct


Digital Labor/Operations Platforms

Machines augment/automate productivity and efficiency gains for operations.

IT OperationsMarketing

Customer service Regulatory...Sales Logistics

Supplychain

Finance/ HR


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 Monitoring

DB & MW Monitoring

Hypervisor Monitoring

Desktop Manageme

nt

Next-Gen IT OperationsTicketing & Service Desk

CMDB

Process Automation

Network Manageme

nt

Advanced Event

Correlation

User Experience Monitoring

CI Visualization (Faster RCA)

Add’l Process Automation

Dashboard & Reporting

Decision Engine

(ML Self-Heal)

Security Monitoring

Server Automation

Server & App

Monitoring

Backup & Storage Mgmt

RTPaaS Monitoring

Project Requirements & Outcomes: Mashups of various solutions/software >$$$ in labor/non-labor deployment fees Scripting team writes knowledge items Six to twelve month deployments are

typical Business case savings of >20% of TCO

Example of Digital Labor/Operations in IT


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

Sales &marketing

ProductionSupply chain

Logistics, distributionand customer service

Business OptimizationPlatform results


Vertical Industry Platforms

IMPs augment human processes and decision-making to improve agility, efficiency, quality, customer retention, field trials, patient compliance, healthcare diagnostics… for industry specific uses.

OperationsLegal...

Industrials,ProductionTransportGovernment

Retail &Consumer

BFSIInsuranceCPGMedia


Example of Vertical Industry Platform: Fraud loss Detection/Prevention in Insurance Industry

For agent/broker operations, protecting the company’s 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.

Claim Filed Storage Analysis

Stop Payment, Report to Authoritie

s

Fraud

Risk Portal


Sample IT Providers

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 others

Digital 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 others

Business Optimization platformsAdvanced Software Applications, Applied Predictive Technologies, ClearStory Data, FICO, IBM, R4 Technology, SAS and SmartOrg among others

Vertical industry platformsIBM, Quid, RiskIQ, SmartOrg, Strategic Analytics and TrendSpottr among others


Overcoming Some of the Challenges

Some of the challenges Overcoming the challenges Let’s 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 business

Use its flashlights/floodlights to improve results. It is about people and process, more than technology

Process reengineering is more difficult than the data science

Embrace it: data-science driven business is digital business process reengineering

Focus on what the data says is possible and not the business

Digital business is informed by data – but it needs to be channeled by the business


Best Practices

1. Define the business opportunity: do not boil the ocean

2. BPR: embrace data enabled business reengineering

3. KYD: know your data sources and limitations

4. Experiment: it’s a combination of art and science

5. Use a fan-out organizational structure: centralize scarce data scientist/programmer/ data engineering resources, fan out using business analysts as conductors-liaisons, embed with SMEs everywhere

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