DIRECTING INTELLIGENCE, a high technology company specialized in big data analytics, active in knowledge creation and management since 2000, designs and creates adaptive, collaborative, Open Architecture Platforms like DATACTIF suite of BIG DATA ANALYTICS, based on machine learning methodology and algorithms. DIRECTING's Intelligent Solutions process transactional data from all sources regardless systems (DB2, Oracle, SQL Server, etc..) and data from the Web, structured as well as unstructured data, allowing real time and on line, substantive assessment of enterprise corporate knowledge.

1. DATACTIF DATAMININGSUITE
www.directingintelligence.com
info@directingintelligence.com
®
Enterprise Corporate Intelligence Platform

2. ARTIFICIAL INTELLIGENCE
Business environment is more complex than ever and its complexity even
if cannot be controlled, it is nevertheless possible to live intelligently with
it. The best actually solution is to integrate techniques based on artificial
intelligence to decision support systems, especially knowledge-based
tools such as neural networks, fuzzy logic and genetic algorithms.
Get inspired from nature, where Adaptation and Evolution of spaces
exists, scientists created machine intelligence able to operate under
uncertainty, approximation, partial information and fault tolerance and
solve “cognitive” complex tasks such as : Classification, Clustering,
Prediction and Association.
For the study of specific phenomena called complex adaptive systems
such as business activities or human activities in general, Intelligent
Systems are more effective because they :
extract knowledge directly from data presenting it, either as logical
expressions, like rules and correlations, or as visual representations
can handle both qualitative and quantitative data simultaneously
can analyze discrete and continuous variables
even though they learn from data, they can also embed existing
knowledge acquired from the business environment
can resolve complicated tasks with a high level of confidence and
can work on data sets with missing or erroneous values
DIRECTING INTELLIGENCE, a high technology company specialized in
big data analytics, active in knowledge creation and management since
2000, designs and creates adaptive, collaborative, Open Architecture
Platforms based on machine learning methodology and algorithms.
DIRECTING's Intelligent Solutions process transactional data from all
sources regardless systems (DB2, Oracle, SQL Server, etc..) and data
from the Web, structured as well as unstructured data, allowing real time
and on line, substantive assessment of enterprise corporate knowledge.
VISUALIZATION IN HUMAN LEVEL
Knowledge visualization in accordance to human abilities is the most
important step in data modeling. DIRECTING's Intelligent Solutions offers
a user friendly interface, with a scalable GUI that, contrary to the high
level of complexity of used algorithms, requires neither computer science
nor statistics knowledge.
DIRECTING INTELLIGENCE

3. Machine learning, a subfield of artificial intelligence, provides computers
with the ability to learn without being explicitly programmed. There are two
machine learning methods and both are used by DATACTIF® :
SUPERVISED LEARNING: is the machine learning task of inferring a
function from supervised training data. The training data consist of
a set of training examples decided by the analyst.
UNSUPERVISED, SELF-ORGANIZED LEARNING : is the machine learning
task of inferring a function to describe hidden structure from
unlabeled data.
DATACTIF®, available as software as a Service (SaaS) and enterprise
hosting solutions, consists of the following modules:
MACHINE LEARNING AUTOMATION. Performs training of existing algorithms,
for every new data set decided by end user. This module creates new
entities in DATACTIF’S data warehouse as well as metadata and updates
all related applications.
DATACTIF DATA MINING SUITE

4. CLUSTERING. Allows to discover group (clusters) of procedures or clients
with common characteristics. Method used, the self-organizing map (SOM),
trained with unsupervised learning from input data that produces a two-
dimensional discretized representation (i.e. 25 neurons, picture bellow).
CLUSTERING HISTORY. Clustering History performs clusters comparison
between two time periods.
Customers’ Segmentation observed through time, offers a macroscopic
point of view of customers’ evolution in a given social and economic
context, measuring in same time the efficiency of the Enterprise's
business and marketing strategy.
DATACTIF DATA MINING SUITE

5. CLASSIFICATION AND PREDICTION. Classifies business procedures, clients’
transactions, etc… as the most optimum for a company and at the same
time predicts future clients ‘behavior and business scenarios effectiveness.
The intelligent systems used, are trained with historical data predicting
Churn, Life Time Cycle and Life Time Value as well as Effectiveness
of Business and Marketing Activities.
Prediction results can be connected with other important economic factors,
such as market share, sales, net profit, growth evolution, etc…. In addition,
this module assists the user in decision making by suggesting optimum
actions to be taken in difficult or unknown market conditions.
ASSOCIATION RULES. In the context of a Customer Centric knowledge
model, association rules allows to relate clusters with any kind of
information provided from both internal operational systems or external
sources (i.e. data from social media).
REPORTING TOOL can combine findings from DATACTIF® and data from
other systems (ERP, CRM, etc…) and presents statistics in ad hoc forms
offering a holistic view of an enterprise’s available information.
DATACTIF DATA MINING SUITE

6. DISTRIBUTION NETWORK EVALUATION-PREDICTION. Based on historical data
of existing stores (profitability, surface, employees, facilities, etc), area
data such as social, demographic, economic, data concerning competition
and customers’ behavior, Distribution Network Evaluator performs:
FOR NEW STORES : Evaluation of new site location and proposal for the
best emplacement. Prediction of future profitability for each option.
FOR EXISTING STORES : Potential evaluation and Profitability's Prediction.
SOCIAL NETWORK ANALYSIS. Combining Social Network Analysis and Text
Mining, this module performs the following tasks :
Data Collection from the web ÷ Communities Detection ÷ Influence
Measurement ÷ Sentiment Analysis ÷ Clustering ÷ Recommender System
÷ Polarization Analysis ÷ Term analysis ÷ Fact extraction
DATACTIF DATA MINING SUITE

7. Contact UsOUR PARTNERS

8. www.directingintelligence.com
info@directingintelligence.com