towards an Intelligent enterprise ecosystem

www.directing.gr – [email protected]

1.1. INTRODUCTION

Today an enterprise is evolving into a complex economic, social and business

environment, co existing with suppliers, producers, competitors, other stakeholders

and customers. Global trends of production and distribution in one hand, business

dependence on technology evolution on the other (Internet of things, Cloud and Smart

Systems) are leading into an era where people, machines, devices, sensors, and

businesses must all be connected and able to interact with each other.

A new paradigm of doing business is necessary, an Intelligent Enterprise Ecosystem

that will offer an operational and profitable symbiotic relationship between an

enterprise, technology, its environment and knowledge generated by (and influencing)

this relationship.

1.2. ASSESSING ALIGNMENT

For a successful Intelligent Enterprise Ecosystem the alignment of business,

technology and knowledge is sine qua non. The history of theory-building around the

concept of alignment is still young and has only been going on approximately 15

years. The most widespread and accepted framework of alignment (even if does not

include knowledge and by extension analytics as an integral part of business) was

proposed by Henderson and Venkatraman in 1993.

This theoretical construct, also known as the strategic alignment model (SAM),

describes the phenomenon along two dimensions. The dimension of strategic fit

differentiates between external focus, directed towards the business environment, and

internal focus, directed towards administrative structures.

The other dimension of functional integration separates business and IT. Altogether,

the model defines four domains that have to be harmonized in order to achieve

alignment.


1.3. OVERCOMING PROBLEMS IN ALIGNMENT IMPLEMENTATION

While trying to align technology with the business, many enterprises experience a

quite fuzzy target. With what ‘business’ should technology and knowledge models

align? According to the ‘Strategic Alignment Model’, a first answer should be with

the business strategy. In practice business strategy is not often a clear target following

a linear model allowing a blueprint with guidelines.

An enterprise must be able to be responsive to developments in its environment. The

company strategy is therefore not a destiny that is ever reached. In reality strategy

provides a trip, not an Ithaca. On the second level of the Strategic Alignment Model,

the alignment is aimed at the business processes and organization.

The organization provides only limited information about the business requirements.

It is focused on hierarchical structure, but not on information content. An additional


problem is that in many enterprises the organization structure is not very stable.

Departments and job titles change frequently. The business processes in the other

hand tend to be more stable. In the development of technology applications they

provide an important basis for the analysis of the information requirements. A

problem however is that there are multiple views of the business processes, all with

different goals and different content.

As a result of this, most IT Departments development projects will build their own

process models according to their own modeling conventions. And every Business

Intelligence project will be standing on some business requirements and on IT

capabilities, with no global strategy.


1.4. A NEW BUSINESS/INTELLIGENCE ALIGNMENT MODEL

Based on 20 years of experience, I propose a new model that incorporates SAM

model into a larger, including knowledge creation and management, knowledge

generated especially from the web. This new model is defined in two axes : Content

Base Axe and Process Based Axe.

1.4.1 CONTENT-BASED AXE

Competition goes beyond established industry rivals to include four other competitive

forces as well: customers, suppliers, potential entrants, and substitute products. This

market-based view of strategy is interested in the resources businesses have and treats

their behavior as a “black box”. Competitive strategy determines how the organization

gains an advantage over its rivals within chosen market positions. Content Based Axe

is critical as it is the one generating profits for an enterprise


1.4.2 PROCESS-BASED AXE

The alignment models corresponding to the process stream of strategic management

focus on the dynamism of business–technology alignment, the co-evolutionary

development of both strategy and IT strategies and on the social dimension of

alignment. These models highlight the importance of the process in which internal

politics, organizational culture, managerial cognition and skills help achieve and

maintain high alignment.

1.4.3 STRATEGY AS PRACTICE

Targeting efficiency above all, the strategy-as-practice approach understands practice

as being “closer” to reality and delivering a “more accurate” description of the real

world phenomena than formal theories populated by multivariate analyses of firm or

industry level factors. Sometimes is also necessary to start with a strategy focusing to

partial business objectives such as loyalty and acquisition, stock management

optimization, etc... Based on this consideration I propose the following methodology.


1.5. INTELLIGENT ENTERPRISE ECOSYSTEM. METHODOLOGY

Directing Intelligence in Enterprise Ecosystem methodology is deployed through the

following ontological values that constitute 5 hierarchical levels :

Business Blueprint. The ability to anticipate unpredictable internal and

external changes : ÷ Business strategy axes description, KPIs, governance,

operational models. ÷ Identification of change drivers and their impact on key

business processes. ÷ Implementation planning through existing or new

information systems and data analytics.

Open Architecture. The guide and reference for collaboration of people

involved at all levels, for present and future projects. ÷ Architectural planning

of applications and systems to be deployed, their interactions and their

relationships to the core business processes of the enterprise.

Shared Values and Communication. The common language inside an

enterprise that permits an effective communication between business and

technology people. ÷ Capturing critical business information, without being

tied to specific technologies. ÷ Modeling of the logical software and hardware

environment that is required to support the deployment of new strategies.

Network of People, Systems and Knowledge. Shared knowledge makes

people from different departments to work together, using the same

information. ÷ Building innovative, interconnected, collaborative and

evolutionary analytics that share and exchange data, content and services

within the ecosystem.

Enterprise Open to World. Interconnection of an enterprise with the world

through all devices ÷ Provision of the connectivity infrastructure and

integration of individual applications and devices into the Enterprise or

Community Ecosystem, based on interoperability standards.


1.6. DIRECTING INTELLIGENCE IN PRACTICE. RETAIL CASE

European Retail is today characterized as very mature with declining growth figures,

constantly high unemployment and stagnation of inflation-adjusted income.

These characteristics, together with an altered demographic structure in almost all

countries, are changing the consumer demands. Retail industry is facing a magnitude

of challenges that could be categorized as follow:

Mondialisation. Supply chain and logistics systems enable retailers to

produce, purchase and sell products worldwide.

Demographic shifts. Demographic shifts (aging population, increase flow of

immigrants, increased urbanization, etc…) determine essential aspects of retail

as they influence or change consumers’ needs and demands. Demographic

shifts open up new niche markets and can require retailers to start new brands,

widen or deepen their product assortment, adapt their pricing philosophy and

service policy and change the design and layout of their shops and commercial

signage.

Health and wellbeing. Health, safety and wellbeing will likely become the

most important factors in near future due to cultural reasons but also due to the

increase of ‘lifestyle diseases’.

Internet of Things. Technology adoption requires new service models,

offered via the internet and moving beyond selling individual products.

Even if consumers are the ultimate arbiters, retail actors that must be taken also under

consideration are Suppliers, Producers, EU regulations, etc..

In order to achieve an Intelligent Enterprise Ecosystem framework, I created an

architectural plan that analyzes the interferences (input) of all external factors on

customers and the consequences on their final purchase decision (output).


Based on this architectural plan I designed a flexible, virtual staging data warehouse

treating all data from all sources and an open architecture platform of big data

analytics based on artificial intelligence, that process information.

1.6.1 WHY ARTIFICIAL INTELLIGENCE :

Incorporating the contribution of game theory and decision-making processes,

gradually an autonomous body of research was created : Artificial Intelligence, which

exceeds computer science, as it is the only one that allows the approach and study of

complex adaptive systems, such as human behavior.

At the borders of economics, computer science, psychology, sociology, semantics and

logic, artificial intelligence was based on heuristic search, the selective trial and error

research.

The development of this science has made possible to have approximate

representations of real situations, more accurate than those generated by operations

research algorithms. Being able to face any situation that could be represented

symbolically (verbally or mathematically through diagrams),


Artificial Intelligence allows to extend the use of computers to more complex

problems and less structured, including the most sophisticated forms of reasoning,

unique privilege to human crisis till this moment.

Complex problems and unstructured data as the chaotic information flow provided by

the web (among other things).

But there is another "because" for artificial intelligence usage apart their ability to

make sense of the mess of available data. Their ability to be trained, be “educated” by

each enterprise to select, analyze and present useful information to serve business

strategy. Their ability to evolve in parallel with each enterprise.

1.7. DIRECTING INTELLIGENCE IN PRACTICE. BANKING

Most banks have never created a close relationship with their retail customers and

understand little of their actual needs. Tailored products and services are rare. Instead,

complaints about inadequate advice, disproportionately high interest rates for

overdrafts or call center issues surface in the news with depressing regularity.

Customer resentment was already running high before the financial crisis. Since then,

trust in banks has plummeted even further. At the same time customers have found a

new, loud mouthpiece in online forums and consumer portals. News about negative

experiences can spread through these media like wildfire. Initiatives like the “Occupy

Wall Street” movement have attracted media coverage as never before.


Examples from other industries suggest that marketing strategy and selective

improvements at individual customer interaction points will not suffice.

Banks need to radically change their perspective, aligning their entire business model

towards the nexus of their success: the customer.

I created an alignment strategy based on a progressive transition (customers loyalty-

acquisition and information consolidation) from the most profitable and loyal clients,

who in parallel offer complete and high quality information, to prospects. Strategy

with the clear objective to implement business objectives (new clients acquisition and

existing clients loyalty), to maximize existing knowledge usage and to allow a parallel

evolution of business and knowledge. Strategy that is represented by the following

diagram.

Based on this strategy a platform of big data analytics was created using machine

learning, trained to increase business objectives efficiency.

more papers and case studies : www.directingintelligence.com

Data & Analytics

towards an Intelligent enterprise ecosystem