Knowledge is not only vital for the enterprise profitability but integral part
of its core business process no matter the sector of its operability.
Business Intelligence as entity that process information and transforms
information into knowledge, must be in the centre of Business and
Technology unified conceptual and operational processes.
DIRECTING INTELLIGENCE bridge the gap between business and
intelligence by creating the foundations for an Intelligent, Operational,
Evolutionary Enterprise Ecosystem.
Aligned to each enterprise Ecosystem, DIRECTING INTELLIGENCE
designs and creates adaptive, collaborative Intelligent Open Architecture
Platforms based on machine learning methodology and algorithms (neural
network, fuzzy systems, genetic algorithms, SVM, etc…), platforms that
processes transactional data from operating systems and data from the
Web, numerical as well as unstructured text data, allowing real time and
on line, substantive assessment of holistic knowledge for each enterprise.
DATACTIF Athena Big Data Analytics is an open
architecture system, based on Artificial Intelligence (neural
networks, fuzzy logic, support vector machine, genetic
algorithms, etc...) that processes aggregated data from :
1. Reservation Systems,
2. Specialized in Tourism Sites
(Trip Advisor, Booking, Trivago, Expedia, etc ...)
3. Google Analytics,
4. And Social Media.
DATACTIF Athena designed specially for the Tourism
Industry combining Social Network Analysis and Text Mining,
performs the following tasks : Data Collection from the web.
Communities Detection. Influence Measurement.
Sentiment Analysis Clustering. Recommender System.
Polarization Analysis. Term analysis & Fact extraction.
DIRECTING Intelligence in Tourism
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 be able to interact with each another.
A new paradigm of doing business is necessary, the creation of an
Enterprise Ecosystem that will offer an operational and profitable
symbiotic relationship between an enterprise and its environment. In
this context a modern Enterprise must create a business strategy
development (linear model) being sensitive in same time to internal
and external changes (non linear model)
Business Intelligence aligned to the Enterprise Ecosystem
Knowledge is not only vital for the enterprise profitability but integral
part of its core business process no matter the sector of its
operability. Business Intelligence as entity that process information,
transforms information into knowledge, must be in the centre of
Business and Technology unified conceptual and operational
DIRECTING since 1999 has for mission the design and creation of
Big Data Analytics aligned with the business engineering of each
sector and within aligned with each enterprise strategy
From federation of systems to Enterprise Ecosystem
Tourism products and services attract travelers to a country. Cultural
and natural attractions, beaches and resorts, sports events are all
products that appeal to travelers. Within this country attributes and
strategy there is destination identity and within destination every single
enterprise's identity and offer.
The best way to achieve a sustainable growth or retention of profitability
for each enterprise is through a three-step process: analyze and
understand the evolution of the tourism sector ecosystem; analyze the
relation between destination and tourism sector ecosystem and develop
strategic positioning and value proposition of the given enterprise
aligned with the above framework.
In this context Big Data Analytics has to understand each traveler
needs and wishes, transform information into actions to be taken and
design a business and communication guide of actions that an
enterprise must undertake.
For the success of this task, data coming from all sources (specialized
in tourism sites, social media, booking systems) must be processed in a
unified platform and analyzed with machine learning techniques as we
have both transactional (numerical) data and data expressing thoughts
and feelings, structured and unstructured data, data concerning the
whole tourism ecosystem and data concerning the traveler's social
Reputation. Rating Index
Rating Index of each Hotel, is based on ratings
made by travelers in specialized sites such as
Trip Advisor, Booking, etc…
Terms extraction discovers attributes from
comments that travelers wrote about an hotel in
the specialized sites.
In the case of Santorini, we observe that
travelers expect “sunset”, “honey moon
atmosphere”, “perfect moments” and feel a
From their hotel a view from their room, etc…
But also BREAKFAST !
Reputation. Terms Extraction
Sentiment Analysis retrieves user reviews in the
specialized sites (Trip Advisor, Booking, Expedia,
etc..) and lists them in Positive, Negative or
Using Term analysis and Fact extraction we can
identify and understand the reasons (some times
unknown to an enterprise) for clients’ satisfaction
In the case of “Luxury Hotel” in Santorini we
observe that “cuisine” and “view” makes disappear
other attributes, as the excessive price. But the
feeling of something missing from an ideal holiday
creates a high neutral and negative sentiment that
must be taken under consideration from the
direction of the hotel
Reputation. Sentiment Analysis Index
Data from Social Media
In the case of Social Media, we analyze not only each enterprise official FB, Twitter or Instagram
but also every channel, concerning the destination. Social Media of communities, of individuals,
of local enterprises, in order to understand the Destination Ecosystem and realize the positioning
of each enterprise in the national and local context.
Why Face Book is more important than Trip
Advisors and other sites.
Because travelers express their sentiment about
their personal moments and feelings, in a more
free way, sharing it with other travelers and
We collected data from any Face Book page
concerning Santorini (communities, individuals,
professional, etc…) and as we can see terms
that appears are not only “staff”, “service”, “view’
but also psychological expectation such as
“romantic”, “need to love this place and love in
general”, “need to live a special moment”. And
this is the Unique Selling Proposition of the
destination as well as of each Hotel
Face Book. Destination. Terms Extraction
Sentiment Analysis that retrieves user reviews
on Face Book reveals a gap between Face Book
evaluation and Trip Advisor one. The deep
meaning is that each hotel in Santorini must find
the balance between professionalism and
personalized “amateur” behavior
Face Book. Destination. Sentiment Analysis
Influencers identification is the number one objective
in every social media as specific users exercise
influence over an organization and its potential
Influencers are activists, well-connected, have
impact, have active minds, and are trendsetters,
though this set of attributes is aligned specifically to
Targeting influencers, is seen as a means of
amplifying marketing messages in order to
counteract the growing tendency of prospective
customers to ignore traditional marketing efforts.
Example of INFLUENCE ANALYSIS
& RECOMMENDER SYSTEM for Santorini based
on Face Book. We see that despite of the thousands
of photos with “sunset” that are everywhere,
Influencers and mostly Asian-Chinese travelers
propose culture !
Face Book. Destination. Influence Analysis
Face Book. Luxury Hotel. Influence Analysis
Influencers identification in the Face Book page of
the “Luxury Hotel” shows that its target group
contains more French people that Santorini average.
Nationalities in general are very important to
business strategy of an hotel and as we will see in
the following pages, combined with booking system
information helps to increase profitability
Clustering allows to discover, groups (clusters)
of users with common characteristics. Data used
are consumer-tourist attitudes, preferences and
life style data from sources such as booking
systems and Face Book.
There are 4 distinct Hyper Clusters :
2. Experience Destination,
4. Time Relatives
Those Groups constitute the GRAF model. Why
they are important. Because each group has
specific requirements, standards, expectations
(from the composition of breakfast, up activities
based on which choice hotel) and definitions as
to what is "romantic", "luxury", "original" etc. ...
Understanding the composition of travelers
concerning a destination first and then for a
specific hotel and being able to predict the
composition for next season is of a highly
importance in the efficient design of the
marketing mix and business plan of each
enterprise focusing on next season.
Booking System. Clustering
Booking System. Prediction
Based on booking systems historical data and
Face book trends, we can predict arrivals by
nationality and booking month for a destination
and within a destination for a given Hotel
Booking System. Prediction
Prediction is updated each month based on real
bookings and arrivals, but also comments on
Face book, mostly on those comments making
reference to future plans of holidays (destination,
month, etc…) allowing this way to an hotel
business and marketing strategy efficient
implementation (SEO optimization, price and
promotion policy, etc…)
Corporate and social media data
Correlation between analysis created by
corporate data and Social Network analysis is
the holly grail for all Big Data Analytics.
DATACTIF® Big Data Suite of Analytics and
DATACTIF®Soneta, creates the bridge between
those two worlds.
As result, we have a full profile for clients and
prospects increasing this way business strategy
effectiveness. We obtain also the enrichment of
transactional data with the necessary qualitative
information, that no other research can offer.
Through historical holistic information on
customers evolution, we can measure the
efficiency of each enterprise strategy and predict
with high accuracy results of future actions.