Thomas Vavra | New Ways of Handling Old Data

New Ways of Handling Old DataTom Vavra

AVP Software & Industry Insights and AnalysisIDC

2

Top Trends

© IDC Visit us at IDC.com and follow us on Twitter: @IDC

People Centric NetworksIntersection of People to People to DataCognitive Computing & Assistive Technology Work Context & flow

Organizational DynamicsSales, marketing, service not “working”Shifting workforce dynamics (for the 1st time

Millennials are the largest % of the workforce) Disruptive new connected business models

Applications are ChangingSocial: Inherent Ability to ConnectUnderlying Platform Services Distributed Information Access – Decision

Support

© IDC Visit us at IDC.com and follow us on Twitter: @IDC 3

Unstructured Content: Value Waiting to be Delivered

Unstructured content – email, video, instant

messages, documents, and other formats –

accounts for

of all digital information

Unlocking value from this content should be

the goal of every organization, but very

few are actually getting all the value

they should be.

THIS CONTENT IS LOCKED IN A VARIETY LOCATIONS AND APPLICATIONS MADE UP OF SEPARATE REPOSITORIES THAT DON’T TALK TO EACH OTHER – E.G., EMC

DOCUMENTUM, SALESFORCE.COM, GOOGLE DRIVE, SHAREPOINT, ET AL.

90%


IDC’s Big Data and Analytics Predictions (1)

1. Through 2020, spending on cloud-based BDA technology will grow 4.5x faster than spending for on-premises solutions; open source technology will represent the core of this new architecture.

2. By 2020, 50% of all business analytics software will incorporate prescriptive analytics built on cognitive computing functionality.

3. Shortage of skilled staff will persist and extend from data scientists to architects and experts in data management; big data–related professional services will have a 23% CAGR by 2020.

4. By 2020, 90% of databases (relational and non-relational) will be based on memory-optimized technology.

5. By 2020, distributed micro analytics and data manipulation will be part of all big data and analytics deployments.


IDC’s Big Data and Analytics Predictions (2)

5. Through 2020, spending on self-service visual discovery and data preparation market will grow 2.5x faster than traditional IT-controlled tools for similar functionality.

6. By 2020, data monetization efforts will result in enterprises pursuing digital transformation initiatives increasing the marketplace’s consumption of their own data by 100-fold or more.

7. By 2020, the high-value data part of the digital universe that is worth analyzing to achieve actionable intelligence will double.

8. By 2020, 60% of information delivered to decision makers will be considered by them always actionable, doubling the current rate.

9. By 2020, organizations able to analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity benefits over their less analytically oriented peers.


Why Do So Few Organizations Find Value in Their Information?

of knowledge workers regularly access 4 or more systems to get the information they need to do their jobs

of a typical knowledge worker’s day is spent looking

for and consolidation information spread across a

variety of systems

61% 36%

Nearly 15% access 11 or more systems

These workers find the information required to do their jobs only 56% of the time


Cognitive Software Attributes

• Performs deep natural language processing and analysis both for information ingestion and research as well as to provide human style communication (usually posed as questions and answers)

• Conducts learning in real time as data arrives • Has the ability to identify similar past experiences and use learning to

current situation • Predicts and recommends possible outcomes

• Score those outcomes with evidence for human analysis• Cycle back to the start so that the continuous learning is practiced, making

the system better over time

Cognitive software support human decision-making with more accuracy, confidence, speed, and agility based on broader and deeper bodies of evidence applied to a more comprehensive view of pertinent conditions without bias.

8

The Content Analytics, Discovery & Cognitive Systems Market Defined

Content Analytics• Text Analytics, Video Analytics• Categorizers and clustering engines• Speech Recognition, Language analyzers

Discovery• Enterprise search engines, information access

platforms, and applications for browsing and navigation

• Knowledge Base/Graph Generation• Rich media search

Cognitive Systems• Digital assistants• Automated advisors• Artificial intelligence, deep learning and machine

learning• Automated recommendation systems

© IDC Visit us at IDC.com and follow us on Twitter: @IDC

Clustering Engines

Speech Recognition

Video Analytics

Text Analytics

Kno

wle

dge

Bas

es &

G

raph

s

Ent

erpr

ise

Sea

rch

&

Info

rmat

ion

Acc

ess

Automated Advisors

Artificial Intelligence, Deep Learning and Machine

Learning

Clo

udO

n P

rem

ise

Commercial

Open Source

CADCS software analyzes, organizes, accesses, and provides advisory services based on a range of unstructured information and provides a platform for the development of analytic and cognitive applications.

9

Cognitive Solutions Ecosystem

Source: IDC

Behavioral Interactional

Performance

Long form

Geolocation

News

Personal data

Healthcare

Location

Sports & Entertainment

SocialCorporate

Logistics

Financial

Marketing

Sales

Procurement

Asset mgmt.

R&D

Logistics

HR

Anti money laundering

Retail pricing

Patient outcomes

Telco churn

IT performance mgt.

Retail

Travel

Media

Healthcare

Insurance

Investment

Commercial leasing

Advertising

Legal

Driverlesscars

Smart home devices

Self-flying drones

Robotic systems

Text analysis

Video analysis

Image analysis

Predictive analytics

NLP

APIs

ConnectorsData storesHypotheses generation

Machine learning

Speech Recognition

Dialogue Mgt.

Finance

Risk mgmt.

Weather

10

Cognitive Systems Use Cases Healthcare

• Diagnosis and Treatment Systems• Education and Training Systems• Pharmaceutical Research and Discovery

Retail• Expert Shopping Advisors & Product Recommendations• Automated Customer Service Agents• Automated Training Systems

Finance/Insurance• Automated Financial Advisors• Policy Advisors & Question and Answer Systems• M&A Investigation and Recommendations

Government• Police Investigation Systems• Program Advisors and Recommendation Systems

Manufacturing• Operational Improvement Systems• Asset Maintenance Systems

11

Source: IDC, 2015

2014–2019 Revenue ($M) with Growth (%)($M) (%)

2014 2015 2016 2017 2018 20190

5001,0001,5002,0002,5003,0003,5004,000

051015202530354045

827 1075 14191916

2644

3683

Cognitive Total growth (%)

Game Changer

Commercial cognitive software platforms have just begun to emerge on the market scene. This category of software used to build “smart” applications and expert advisors will grow rapidly over the next five years enabling a multi-billion dollar intelligent applications market.

2014–2019 Revenue ($M) with Growth (%)($M) (%)

Game Changer

Commercial cognitive software platforms have just begun to emerge on the market scene. This category of software used to build “smart” applications and expert advisors will grow rapidly over the next five years enabling a multi-billion dollar intelligent applications market.

Worldwide Cognitive Software Platform Forecast

Worldwide Cognitive Market by Industry, 2015


WW Cognitive Systems Spending (US$M) by Use Case, 2015

Adaptive Learning

Asset/Fleet Management

Automated Claims Processing

Automated Customer Service Agents

Automated Threat Intelligence and Prevention Systems

Defense, Terrorism, Investigation and Government Intelligence Systems

Diagnosis and Treatment Systems

Expert Shopping Advisors & Product Recommendations

Fraud Analysis and Investigation

Freight Management

Merchandising for Omni Channel Operations

Others

Pharmaceutical Research and Discovery

Program Advisors and Recommendation Systems

Public Safety and Emergency Response

Quality Management Investigation and Recommendation Systems

Regulatory Intelligence

Sales Process Recommendation and Automation

$- $ 100 $ 200 $ 300 $ 400 $ 500 $ 600 $ 700

Value (USD M)


European Cognitive Systems Spending (US$M) by Use Case, 2015

Adaptive Learning

Asset/Fleet Management

Automated Claims Processing

Automated Customer Service Agents

Automated Threat Intelligence and Prevention Systems

Defense, Terrorism, Investigation and Government Intelligence Systems

Diagnosis and Treatment Systems

Expert Shopping Advisors & Product Recommendations

Fraud Analysis and Investigation

Freight Management

Merchandising for Omni Channel Operations

Others

Pharmaceutical Research and Discovery

Program Advisors and Recommendation Systems

Public Safety and Emergency Response

Quality Management Investigation and Recommendation Systems

Regulatory Intelligence

Sales Process Recommendation and Automation

Supply and Logistics

$- $ 20 $ 40 $ 60 $ 80 $ 100 $ 120

2015

2

3

9

11

12

1

Source: IDC, 2016

Practices to Implement Cognitive Systems Initiatives

Source: IDC, 2016

1 - Setting Expectations


Be Realistic

Issues Business and IT both assume Cognitive will

replace humans Cognitive can only assist Outputs are never a “sure bet” Requires collaboration between IT and LOBs Relevant data is needed, not just more of it


Real World #1: Bankers vs. Robots

Source: IDC, 2016

2 – Leverage Cloud Services


Cloud as a Facilitator and Problem Solver

Issues Cognitive systems require vast data and

processing power On premise investment can be expensive and

time consuming Cloud services can do “heavy lifting” and

alleviate up front costs and time… … but, not all Cognitive solutions are Cloud-

ready or appropriate


Real World #2 :When Planes Love Clouds

We Cloud

Source: IDC, 2016

3 – Identify Repetitive Routine Actions


Choosing a Starting Point

Issues Identifying the right use case takes time and

thought Need to start by documenting current business

processes to identify resource-intensive tasks Narrow down list for cognitive applications Free up human resources to analyze exceptions

and outliers


Real World #3 : Letting Doctors be Doctors

Source: IDC, 2016

4 – Validate Outputs


Cognitive systems don’t fix bad inputs and untrained users

Issues The lack of quality inputs and expert trained

users will, necessarily, result in mistakes and bad outputs

Surprises are common in the early stages Constant validation is required to minimize

erroneous results Feedback on errors must be part of the regular

workflow


Real World #4 : Matching Clients with Hotels

Source: IDC, 2016

5 – Manage Data to Avoid Inaccurate Results


Data management is always central and key to any initiative

Issues Data can have many issues: inconsistency,

varied formats, ownership, (lack of) governance Need to map data: Where is it? Who owns it? Is

it connected/integrated? Third-part data is often required to complement

existing sources Value of data increases exponentially when

different types and sources are combined


Real World #5 : IT Mfg + Internal IT + External provider

Thank you!

Tom Vavra

Tel: + 420 221 423 140

[email protected]

Associate Vice President

IDC CEMAMalé naměstí 13110 00 Praha 1Czech Republic

www.idc-cema.comwww.idc.com

CEMA Region