sergey yablonsky, 2015 - ibm€¦ · sergey yablonsky, 2015 •we illustrate, analyze and classify...

Sergey Yablonsky, 2015

Objectives

1. Status of technology and strategic opportunities for IBM to adopt the Watson Cognitive solutions building a multi-sided platform ecosystem.

2. Semantic Web representation techniques - a common language that makes it easier to visualize ICT-enabled Watson innovations.

3. The pilot version of IBM Watson lightweight ontology. 4. Business models. The IBM Watson multisided

platform and ecosystem. 5. Classification and examination of the current status of

IBM Watson services and possible ways of further evolution of technology and services both for the companies and their customers.


IBM Watson ontology


• Our goal is to discuss a common language that makes it easier to visualize ICT-enabled Watson innovations using a common set of domain-specific Semantic Web representation techniques.

• Good representation of Watson innovations ontology can help to illuminate important dimensions of a problem—in our case, to understand and classify Watson innovative services and ecosystem.

IBM Watson ontology


• We illustrate, analyze and classify representation of Watson innovative services and ecosystem by means of ontologies, in particular lightweight ontologies – taxonomies.

• Our research was motivated by the observation that various descriptions of Watson services implement the concept of an e-service on similar core concepts with distinct features.

• The goal is to capture the common core of different approaches to facilitate research in Watson services and ecosystem. Thus we illustrate how the ICT innovative services taxonomy can be complemented by the real instances in the global and the emerging market of Russia.

IBM Watson


IBM Watson Technology


IBM Watson Technology: cloud


IBM Watson platform


Platforms are defined (Gawer, 2009) as building blocks (products, technologies or services) that act as a foundation upon which an array of firms (a business ecosystem) develop complementary products, technologies or services, proposing such requirements for a platform:

• it should perform a critical function of the overall system or should solve a crucial technological issue of an industry,

• it should be easy to connect to, ‘‘build upon’’ and provide space for new and unplanned usage.

Platform market


We shall use such definition (Evans et al. 2008; Hagiu and Wright, 2015a,b): a multi-sided market exists, when at any point in time there are

• two or more distinct groups of customers;

• the value obtained by one kind of customers increases with the number of the other kind of customers; and

• an intermediary is necessary for internalizing the externalities created by one group for the other group.

It is suitable to think about MSPs as businesses which are both platforms and market intermediaries.

Difference between MSP, reseller, input suppliers, and vertical integrator

caption.


MSP


We think that proposed definition (Hagiu, and Wright, 2015 a,b) offers a more precise notion of MSPs:

• Multi-sided platform (MSP) is an organization that creates value primarily by enabling direct interactions between two (or more) distinct types of affiliated customers.

A two-sided network has four network effects

• A same-side effect for each side, i.e., preference regarding number of other users on own side

• A cross-side effect in each direction, i.e., preference regarding number of users on other side

• Each effect can be positive or negative

Side 1

Platform

Side 2


Watson platform ecosystem

This is Moore’s (Moore, 1993, 2006) definition of an ecosystem: “An economic community supported by a foundation of interacting organizations and individuals—the organisms of the business world. The economic community produces goods and services of value to customers, who are themselves members of the ecosystem. The member organisms also include suppliers, lead producers, competitors, and other stakeholders. Over time, they coevolve their capabilities and roles, and tend to align themselves with the directions set by one or more central companies”.



The relationship between platform and platform ecosystem exhibits a huge amount of variety and as yet this area had not been entirely explored.

The idea to offer software systems based on core products combined with complementary solutions from an ecosystem is applied in both the business-to-business (B2B) and the business-to-consumer (B2C) sector.



• IBM is working hard to commercialize the Watson platform.

• Today Watson platform ecosystem has simplified configuration shown on below and includes all spectrum of B2B,B2C, and B2G relationships with different value and potential.


Conclusion We summarize status of technology and strategic opportunities for IBM to adopt the Watson innovative technologies building a multi-sided Watson platform ecosystem.

IBM Watson represents a first step into cognitive systems. The combination of the following capabilities makes IBM Watson unique:

• Natural language processing by helping to understand the complexities of unstructured data, which makes up as much as 80 percent of the data in the world today.

• Hypothesis generation and evaluation by applying advanced analytics to weigh and evaluate a panel of responses based on only relevant evidence.

• Dynamic learning by helping to improve learning based on outcomes to get smarter with each iteration and interaction.


Conclusion Although none of these capabilities alone are unique to Watson, the combination delivers a powerful solution: • To move beyond the constraints of programmatic

computing. • To move from reliance on structured, local data to

unlock the world of global, unstructured data. • To move from decision tree-driven, deterministic

applications to probabilistic systems that co-evolve with their users.

• To move from keyword-based search that provides a list of locations where an answer might (or might not) be located, to an intuitive, conversational means of discovering a set of confidence-ranked responses.


Conclusion

• We discuss a common language that makes it easier to visualize ICT-enabled Watson innovations using a common set of domain-specific Semantic Web representation techniques

• Good representation of Watson innovations ontology can help to illuminate important dimensions of a problem—in our case, to understand and classify Watson innovative services and ecosystem.

• We illustrate, analyze Watson innovative services and ecosystem by means of ontologies, in particular lightweight ontologies – taxonomies.


Conclusion • In this paper we use fragments of the pilot

version of lightweight ontology (hierarchy of taxonomy concepts), and a set of relations holding between those concepts.

• The total number of all ontology features is too complex to be represented here in its entirety.

• We provide some counterparts: Technology domain, Cloud computing sub domain, IBM Watson MSP Platform (simplified) and ecosystem domain and formulate basic features of the MSP side actors.


Conclusion

• Knowledge Management tools based on IBM Watson ontology/taxonomy could be used for the platform strategic management opportunity to improve and align business processes with this platform.


Conclusion • Improving of the University Programs side will

indorse IBM Watson platform ecosystem. It will mobilize students and instructors on board of IBM Watson multi-sided platform and will enhance Watson professional education and training.

• At the same time we can foster the capabilities of Watson Solutions starting with pilot university courses and R&D projects. That might push Watson brand with Watson multi-sided platform ecosystem globally and locally. Enabling of the university partnerships might explore and overcome barriers in the global and local markets.


Conclusion For Russian market the major barriers were identified as • the lack of educational and training materials

in Russian language, use cases, • non-existing functions of Analytics and

Cognitive solutions in Russian language, • limited understanding of Watson value

preposition in the main Russian industry sectors,

• legal restrictions to foreign cloud solutions and

• sensitivity of certain sectors concerning data privacy.


Conclusion Finally key strategic options for IBM Watson technology adoption in Russia are presented deriving from the prior analysis. The major recommendations are to • establish an expert Watson team for Russia, • use corporate network and Russian

universities networks to enhance professional education and training in Russian language,

• foster the capabilities of Core Solutions starting with pilot R&D projects,


Conclusion • push Watson brand with Watson multi-

sided platform ecosystem,

• enable partnerships to explore and overcome barriers in the market,

• map possibilities to implement Cognitive Solutions without the necessity of Russian langue and

• plan pilot Russian localization of Watson Cognitive.


Thank you!


sergey yablonsky, 2015 - ibm€¦ · sergey yablonsky, 2015 •we illustrate, analyze and classify...

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