Take your first step into the world of Data Science

We offer predictive decision models using complex rule based algorithms for customers with advanced business data maturity

What's your data-driven management maturity?

Stage 1 - Operational

  • If you constantly rework information
  • Your ERP/CRM database is not the single source of true.
  • Your reporting is standalone, reactive and/or focused on legal compliance
  • Data driven management is not a common practice in your organization and is not alligned with strategy

Stage 2 - Strategic

  • If your are confident to take strategic decisions with information straight from your datawarehouse
  • Your reporting has an aceptable degree of automatization, is proactive, use multidimensional analysis and dashboards.
  • Data driven management is the common practice. Your performance indicators are alligned with strategy and shared with stakeholders.

Stage 3 - Transformational

  • If your are confident to take operational and strategic decisions based on prediciting modeling, scenario planning, and risk mitigation
  • Your reporting delivers actionable insights based on Data Science and Machine Learning
  • Data driven management is the core of your competitive advantage. Data value influences investments.
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Data Science - 1. Time Series Modelling

There are numerous data sources that change over periods of time, known as time series.

We are able to build models that understand trends in the past to predict the future.

For example, if you have data on a user's previous online shopping history, we can predict what items they are likely to buy on their next visit to your website.

Data Science - 2. Regression and Predicting Modelling

Data Science - 3.  Advanced Data Algorithms

Take advantage of the data that your business collects everyday

CLUSTERING

Clustering helps your business to identify data patterns and similarities for business decisions

  • In retail businesses, clustering discovers hidden patterns in customer shopping behavior, sales campaigns and customer retention.
  • In the insurance industry, helps fraud detection, risk factor identification and customer retention efforts.
  • Financial services applications include:  customer segmentation, credit scoring and customer profitability.
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PROJECT DEPLOYMENT: Clustering Data for Customer Segmentation Strategy

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Principal Component Analysis

PCA is a methodology to reduce the high dimensionality/variables of a complex problem.  Here we try to reduce the number of variables that describe a business outcome to make predictions and actionable insights

The most famous and powerful internet search engine, Google, uses PCA algorithm in order to display the most relevant results when you search somothing. Facebook uses PCA for face recognition.

Our applications of PCA are in the field of quantitative finance, helping our customers to:

  • Analyzing the shape of the yield curve
  • Hedging fixed income portfolios
  • Forecasting portfolio returns and analyzing the risk of large portfolios
  • Developing asset allocation algorithms for equity portfolios
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