A day at the office: Configuring Identity in the cloud

The past half year I did not blog that much on TestingSaaS.
With good reason!
I started a new job as a technical consultant at iWelcome and I was quite busy with relocating too.

Why iWelcome?
It is Europe's Identity Platform in the cloud.
iWelcome provides Identity & Access Management as a Service (IDAAS) for organizations, so they can manage the identity lifecycle of their consumers, employees, business customers, partners and suppliers in a secure, simple and efficient manner.

How cool is that?
Since 2010 I have been studying IDAAS (thanks UMA :-) ) and now with the hype of data science (event logging !!) and data privacy (GDPR) I can all combine these disciplines in one job. Who dares wins!

Mind you, I already had some IAM experience at Essent and Onegini, but this was software related, now it's implementation, a complete other ball game with other stakes and rules.

Just one step at a time.

So stay tuned for my further adventures in IAM told on TestingSaaS, eForensics Magazine, Fixate and the iWelcome blog.

Big Data, deep learning, neural networks. Blimey, now I'm confused!

Yesterday I went to the BI-Podium Event 'De achterkant van Big Data' in Amersfoort.
A great event with fascinating presentations about big data, technology and even ethics.

Many Dutch companies in data science like Big Data Lab, Xomnia and Many2More were present.
Also a great endnote by Donna Burbank, which was a great boost for starting data scientists.
Awesome to see there are a lot of Dutch Big Data enthousiasts and there was enough time for networking.
Thank you Visser & Van baars Recruitment for this opportunity.

As a data scientist with a bio-informatics and software testing (also lots of analysis) background I was able to follow the presentations.
A lot of terms were not new for me, but is that also true for my fellow SaaS enthusiasts from my TestingSaaS-community?
I  already wondered why big data terms like deep learning, neural networks, machine learning etc.are mostly explained from a marketing (too easy) or development (too technical) viewpoint?
Luckiliy, this was not the case at this BI-Podium Event.

So, I got the idea for a blog series on explaining these big data terms in a straight forward way without the marketing and technical phrases.
This way I want to help new big data enthusiasts not too get scared of all these terms, but give them a starting point to explore this new sexy discipline data science.

That's why I founded TestingSaaS, to explain the world of SaaS in a straightforward way.

Stay tuned for my blog series on Big Data as-it-is!

When curiosity gets noticed: an interview about my journey in big data

Last year I started my deepdive in big data.
Blogging, Tweeting and following Coursera data science modules gave me a good start.

Well, that was noticed by my Twitter followers.
One guy, Matt Ritter saw my enthusiasm and wanted an interview.
We talked about my curiosity for big data, my journey and the problems I face when dealing with big data.

Matt, thank you for interviewing me and sharing my journey.
A great guy to follow.

Join also my social network for sharing adventures in big data, software testing, SaaS and computer security and forensics.

Enough adventures for a lifetime!

Data science and software testing, it's all about the question

Introduction

When I started my career in software testing I was a biologist without business experience, but I knew how to crunch data through statistics, python and machine learning.
In the last 11 years software testing was my main profession and still is.
But, more and more companies are into Big Data (as a part of data science) and as a biologist, trained in crunching lots of data (genetics, bioinformatics), I got curious.
Is there a way to combine my knowledge of statistics and crunching big data and software testing in today's business?
Sure there is: a lot of methods (statistics, data mining, web scraping) and programming language (R, python) used in data science can also be used in software testing.
Both software testing and data science are empirical studies trying to answer a specific question. The answer to this question can be derived by using tools or methods.
Mind you, don't let the tool or method determine how the answering process proceeds, let the question be the determinant.
Be open minded! Remember a fool with a tool is just a fool.

Data science and software testing

Data science is not just statistics, it is an interdisciplinary field like bioinformatics, combining mathematics, statistics, computer science, information science etc.
Just like Big Data, it's a buzz word, but a data scientist, according to Coursera, has one goal:

Ask the right questions, manipulate data sets, and create visualizations to communicate results.

Well, that's the same in software testing.
Without the correct question, dataset and visualization (report) a software tester can't inform the stakeholder about the state of quality of the object under test.

Now I know testers have tools like Jira, Microsoft Excel and Selenium to help them.
Why should we know about data science then?
Well, as I said before, a fool with a tool is just a fool.
You maybe know how to use many testtools, but the most important thing a tester does is asking the right questions. This triggers the other stakeholders to answer these and this way possible issues are found.
Data science is all about asking the right questions. It can help the tester with creating the question and deriving the testset, even when the testset has missing data. It also learns the tester how to visualize its findings.
Testtools can also do these things, but, in my opinion, a tester should be able to do it himself.
Knowing data science can help the tester to stay critical. 
There are a lot of data science courses online like Coursera or Udacity.
Try a course, it won't be easy, but that's part of the learning.

Conclusion

Software testers can learn from data science to help them in their daily work: ask open minded critical questions, testdata development and processing, testtool selection and visualizing the quality of the object under test.

For me, data science increased my ability to ask the right questions and diminished the fear of going too deep into the data. 
A software tester never should be afraid to ask the right questions to different (!) people, go deep if neccesary and report his/her findings
You have a job to do: Visualize the quality of the object under test, as critical as possible!

So what's your hobby? Resurrection!

2015 is a busy year for me: new job, more responsibilities, work abroad and some explorations in data science using R.

I noticed I did not post anything on my TestingSaaS blog for a while now.
Well, it's that time again.
Too much is going on in software testing,cloud computing, forensics and information security to let unnoticed.
Questions to be answered like:

• Is the software tester a dying breed?
• How can we test the Internet of Things?
• Can we use data science when doing software tests?
• Isn't test automation just checking, not testing?
• What's a RAT in information security, and why should you know about it?

Just a few questions, and the next few months I am going to answer these through my blog and my articles for Eforensics Magazine.

TestingSaaS is not a dying breed.

Why not?

Because his hobby is: Resurrection!