Rafael Ivan Garcia

Computer Engineer by University of São Paulo, entrepreneur and cofounder of Infosimples. Studies and works with artificial intelligence, loves gaming and left the sedentary life behind.

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Clients satisfaction survey results of 2015
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Running a Company

Here at Infosimples, every year we conduct a satisfaction survey to collect the opinions of clients about our services and to understand the strengths and weaknesses of our services in the eyes of those who use it.

The survey results enable us to evolve and deliver a better service year after year.

On december 2015, we sent our most recent survey to 20 companies who use a data automation service we provide, and in this article we will share a compilation of the results.

Infosimples solutions evaluation

The first part of the survey was about how much the solutions meet the clients' needs and how easy it is to integrate existing systems with the Infosimples APIs.

Infosimples solutions evalutation

Infosimples client support evaluation

The client support evaluation worried us because of the perception of agility with a level lower than we would like, even though the professionals were considered to have excellent/good qualification.

We are already taking actions to improve support agility. We expect to see an expressive improvement on the next satisfaction survey.

Infosimples client support evaluation

Net Promoter Score (NPS)

This was our first Net Promoter Score (NPS) evaluation.

"How likely is it that you would recommend Infosimples to a friend or colleague?"

The answer to this question refleclects the client's loyalty and willingness to recommend the solution to others.

This metric usually ranges from -100 to + 100.

Net Promoter Score Scale

The result was a +60 NPS. We consider this a good result because:

  • The software industry NPS in 2015 was +19*
  • There were no detractors (scores 0 through 6) - 0% detractors
  • Promoters (scores 9 and 10) represented 60% of respondents
  • Passives (scores 7 and 8) represented 40% of respondents

We believe this metric will be more helpful from the next survey on, when it will be possible to analyse our NPS evolution.



We would like to thank all respondents for having dedicated some minutes to answer the questions. The results are being very important on our plan for the starting year.

We wish a great 2016 for everyone!

*Source: Satmetrix 2015 Consumer Net Promoter Benchmarks

Amazon Web Services launches Frankfurt (Germany) region
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Amazon Web Services (AWS) launched today their second region in Europe, this time in Frankfurt, Germany. The new region already supports many services offered by AWS worldwide, including the traditional Elastic Compute Cloud (EC2)Elastic Block Storage (EBS)Auto ScalingElastic Load Balancing and Relational Database Service (RDS). Moreover, three new edge locations were included in Frankfurt for Route 53 and CloudFront.

Ireland and Frankfurt regions can now be used together to build multi-region applications with the assurance that all data are hosted in data centers within the European Union. The launch of a region in German soil is probably related to the restrictive data protection laws that companies in that country have to deal with, which limits what can be stored in data centers outside the country.


What is Data Matching?
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Data Science

Data matching example

Data Matching is the task of finding records that refer to the same entity. Normally, these records come from multiple data sets and have no common entity identifiers, but data matching techniques can also be used to detect duplicate records within a single database.

Data Matching is also known as Record Linkage, Object Identification or Entity Resolution.

Identifying and matching records across multiple data sets is a very challenging task for many reasons. First of all, the records usually have no attribute that makes it straightforward to identify the ones that refer to the same entity, so it is necessary to analyze attributes that provide partial identification, such as names and dates of birth (for people) or title and brands (for products). Because of that, data matching algorithms are very sensitive to data quality, which makes it necessary to pre-process the data being linked in order to ensure a minimal quality standard, at least for the key identifier attributes.


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