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The methods and solutions are methodologies that have been tested and they produce very good results: IQEvaluator, IQProjector, IQClassifier, IQExtractor, IQRisk Manager, IQ Process Analyst, IQSelector.

The concept of Analytics

Analytics is a strategic capacity that the organization can have and develop for competing. Analytics work requires information systems support, modeling process for data transformation into knowledge. Analytics refers to the use of statistical tools, operations research and information systems to develop enterprise capacity.Companies are competing to optimize their performance on analytical capabilities, which represents getting access to quantitative expertise, capable technology environment and appropriate data.

Using analytics for:

  • segmentation
  • ,
  • forecast
  • risk analysis
  • processes analysis and evaluation
  • product design and prototyping
  • performance evaluation
  • service quality measurement
  • direct marketing evaluation
  • strategic scenarios construction
  • Web analytics
  • IQAnalytics: Project Examples

    Siempre piense ne los que puede pasar en el futuro a su propiedad, es un negocio de largo plazo.

  • Flowers growth factors
  • This example is applicable to different plantations. The purpose was to identify the best option for plastic cover for the farmīs greenhouses (critical variable) based on factors such as growth, area,luminosity, temperature and so on.

  • Service Evaluation for ATM's
  • Evaluation of service in new banking is crucial. Customerīs behavior changes according to the needs. The demand for services have different segments and the solutions need to be organized according to these segments. ATMīs service evaluation includes variables that are related to technogy, peopleīs knowledge of using, adaptation, localization, security etc.

  • Bank Branch Office service
  • This refers to the creation of an information system to feed a service dashboard. In some cases the look of the offece is very important and in other cases the most importnat could be the waiting lanes.

  • Bank risk classification
  • Risk classificatuion is applicable to almost every risk in the organization. The aim of the classification is to identify factors and the likelihood of understanding of how these factors can affect the organization performance. The consecuencies of risk classification are related to marketing strategies and operational tactics.

  • Maximum Probable Loss
  • This is a method to identify what is the effect of risk exposure and loss distribution analyisis in order to know how risks can change the developemnt of the organization.

  • Service queue analysis?
  • What is best way to provide access to services. Human, by phone, by e'mail etc. How to define waiting times and service solutions?

  • Inventory Meat and Eggs Industrial overview
  • This is an aggregate exercise of inventory by industry. This is the simulation, based on statical bases, of the production. Some variables are the most important such as the type of animals and the process of production. There are many interesting businesses associated with aggregate inventory analysis.

  • Pension and benefits transition
  • This is a package of benefits analysis, pesion, work compensation, health, subsidies, maternity leave etc.

  • Disability risk and catastrophic diseases
  • This is the analysis of health insurance for illness that have consequences in the work capacity and the time of treatment and cost is very high

  • Insurance Portfolio Simulation
  • This is a silulation of loss given default function. The methodology is based on the number of claims, defaults and the value of these claims.

  • Training in Analytics capacity
  • Analytics progarma of eight courses for analytics capacity development, including specialized software, statistics, strategy and risk analysis.

  • Forecast in multiple dimensions
  • This is the use of analytical tools to find the forecast of demand, costs, and risk simultaneously.

    Knowledge Tools & Data Management are a priority

    The knowledge tools include technological tools and techniques. There are statistical, modeling and design tools supported by technology. Portals technology, data warehouses, collaboration tools etc. There are techniques such as storytelling, communities of practice that provide environement for knowledge transfer.

    Enterprise Risk Management, Knowledge and Governance

    Management complexity starts in the problem of the holistic view of the organization. Governance requires support from many disiciplines and governance has to addopt models of application. IQAnalytics beliefs in the governance model that identify management as management of knwledge worker and problem solvers under the umbrella of enterprise reisk management for reducing the variation of expected results. There is a clear focus on new management for keeping trust of the employees and to reduce risks associated with strategic development.


    Risk & Knowledge

    First, risk modeling knowledge can provide meaning to information “Risk Management is frequently not a problem of a lack of information, but rather a lack of knowledge with which to interpret its meaning” (Marshal and Prusak 1996).Second, risk modeling knowledge is based on the measure of variability.RM is important because (Oldfield and Santomero 1997) of the search of the maximization of the expected profits, which are exposed to potential variability which can be transformed into losses. The causes of the variability can come from different sources: market, investments, operation, strategy and so on. The organizations are looking for solutions to reduce, to control and to mitigate the risks in order to achieve their goals.more


    Modeling Process

    The risk modelling process is a mathematical and conceptual process. The mathematical model (Caldwell and Ram 1999) starts from a problem and variables definition, introducing observations, data, description of the relationships among variables (generally equations and basic data models), assumptions (experience-knowledge) and with people’s knowledge produces solutions-outcomes (required knowledge for solving the model) that can be used for the specific problem solution or with additional knowledge applicable to several problems.more



    This is the discipline to think about the chain od data, models, standards, processes, technology and strategies. This is a development of the organization to transform organizatuons to compete in worlds with new players, new economical strcutres, managing supply chains, international financial services etc. This is a new view of the world based on integral, holistic and methodic view of management. more



    There are many common points with positivism in the KM and ERM disciplines. The research in several cases in areas of risk analysis is quantitative and based on data analysis. In KM there are many tools from data base structures, data-mining and statistical modeling that have been implemented based on a positivist research approach. In KM there are different points of view, based on computing or human approaches or a combination of both. In any case the positivism and realism paradigms are influencing the research. more

    Mantaining your data

    Data is an asset and the capacity of using it is directly associated with the data quality

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