Archive for ‘Data Integration’ Category

Data in traditional companies vs. digital natives
  • Synergic Partners
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  • Arquitectura Big Data . Big Data . Data Science . Data Warehouse . datos . Procesamiento de Datos .

Once the process of how to obtain value from data is understood, it is worth asking whether if the process varies if we are talking about a traditional company, for example a large telco or a bank, or if we are talking about a start-up based on the new digital economy as is the case of companies such as Uber, Cabify, Airbnb or Netflix among other. Data in a traditional company In a traditional company, especially in large companies, the heart of data processing has always been the data warehouse. In the following diagram we can see an overview of a typical data warehouse (DWH).     Absolutely all the information stored and processed in a DWH is structured information…

Towards the Fourth Industrial Revolution
  • Carlos Lorenzo
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  • Big Data . Digitalización . Eficiencia operativa . Fabricación Aditiva . Industria 4.0 . Inteligencia Artificial . IoT . Realidad Aumentada . Revolución Industrial . Transformación digital .

The customization of mass production is the catalyst for change that, among other things, originates industry 4.0 Lot has passed since the steam engine changed the course of our civilization history with the mechanization of production in the First Industrial Revolution. Scientific advances at the end of the 19th century allowed the Second Industrial Revolution with the discovery of electricity that would be the basis for mass production, followed by a Third Industrial Revolution in the 20th century thanks to the power of information technology and electronics in the automation of production processes. Today, a Fourth Industrial Revolution is brewing, product of the merge of a series of exponential technologies such as Big Data, Artificial Intelligence, the Internet of Things…

From Sensorization to Industry 4.0
  • Maria Muñoz
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  • Advanced Analytics . Analítica Avanzada . Big Data . Industria 4.0 . IoT . Sector Industrial . Sensorización . Transformación digital .

The industrial sector is currently immersed in a process of change that makes evident the need to evolve its business model towards a less traditional business model. In recent years, digital technologies have appeared that can help the industry adapt to market demands. The incorporation of these technologies in industrial processes becomes key to remain relevant within the sector. Thanks to the integration of these technologies, focused on IoT and the analysis and collection of information on the own instrumentation of the machines, the industrial sector will be enriched with the appearance of a huge amount of information not contemplated nor analyzed to date. This analysis will significantly increase the knowledge that companies have on their own processes and their environment,…

Anticipating Customer Churn through Levers of Retention
  • Rubén Granados
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  • Big Data . Churn Prediction . Data Science . Fuga de Clientes . Machine Learning . Palancas de Retencion .

Identification and analysis of signs of customer churn Knowing which customers might not continue as such has been the objective of classic customer churn models, which final output consisted of the probability of loss associated with each customer over a specific temporary time range. However, once we know the customer is going to leave, we must ask ourselves: What can we do to keep them? And taking it even further: What can we do to keep the customer from even considering leaving? Preventive Measures for Customer Churn in the Banking Sector At Synergic Partners, we have carried out a project for one of the largest banking entities in Spain responding to these questions with a methodology based on the combination…

Warnings about normalizing data
  • Santiago Morante
  • Algorithms . Algoritmos . Analítica Avanzada . Big Data . Blog Synergic . Data . Data Normalization . Data Science . datos . Machine Learning . Normalización de Datos .

For many machine learning algorithms, normalizing the data of analysis is a must. A supervised example would be neural networks. It is known that normalizing the input data to the networks improve the results. If you don't believe me it's ok (no offense taken), but you may prefer to believe Yann Le Cunn (Director of AI Research in Facebook and founding father of convolutional networks) by checking section 4.3 of this paper. You can catch up the idea with the first sentence of the section: Convergence [of backprop] is usually faster if the average of each input variable over the training set is close to zero. Among other things, one reason is that when the neural network tries to correct…

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