Business, Technology and Intelligence alignment model for collaborative, networked enterprises

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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.

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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

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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.

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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

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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.

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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.

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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).

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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),

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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.

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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.
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