In the previous blog post, we've introduced 100WEEKS, an inspiring Dutch non-profit organization helping women in Africa to get out of extreme poverty. Since 2015 they have already helped 240 women and are successfully proceeding with next projects. We (Nebu) are supporting this amazing social responsible initiative by providing 100WEEKS with Nebu Dub InterViewer to facilitate the data collection processes. It is great to see how they have been able to use Nebu's tool to the reality of the challenging African environment!

100WEEKS together with the local fieldwork team have set-up a tablet-based Pop-Up call center and are proceeding with the first interviews! The local program manager Fabrice (on the right) together with four interviewers (from left to right: Maureen, Lucie, Benign, Maliza) are calling women regularly to hear how they are doing and to collect data thereon. 

Data collection is an important part of the project as Johannes (in the middle of the picture in the front of Jeroen, 100WEEKS' co-founder) needs to measure the impact of the program on the lives of 100 women taking part in the project.

In the previous post, we explained what the main new roles introduced by GDPR are and what the impact of the new legislation is. Now, let's dive into more details.

Who is concerned?

Everyone!

If you process EU citizens data as part of your activity, regardless whether that processing occurs in or out of the EU, then the GDPR applies to you. Bear in mind that employee data and customer data ARE personal data. And the simple fact of storing that data is considered a processing activity.

Six principles of the GDPR 

The GDPR is not simply a ticking boxes process to avoid a big fine. It is principles driven and aim to change the way we perceive and treat personal data. There are six principles, listed below:

R has gained massive popularity in the past decade as the tool of choice for a wide variety of data analysts. The language is used as part of the data analysis toolchain in some of the biggest companies in the world. The former Revolution Analytics listed up some of the companies that work with R. That list looked impressive already in 2014 including Facebook, Google, Twitter, Monsanto, the FDA, Lloyds, Credit Suisse. Airbnb joined the club in 2016.  

R first appeared in the 1990s in New Zealand, as an implementation of the S statistical programming language. R was written by statisticians, with statistics and data in mind. It is a perfect choice for data analysis, statistical modeling, simulation and graphics. Even though these are key distinguishing features of R, the language provides some other powerful features we will mention below.

Statistics and data in the DNA

R allows you to manipulate (e.g., subset, recode, merge) data quickly.  Some R packages have been designed specifically for these purposes, e.g., dplyr. Typically, a majority of the time spent on an analysis project is spent on the analysis—preparing the data.  R is much adept and efficient in data preparation. Collected data often requires many steps in data processing to be ready for analysis, so R is ideal.

Most likely, you have heard about upcoming new legislation in relation to the processing of personal data. This General Data Protection Regulation (GDPR) will be in effect per May 25th, 2018, and has a big impact on Market Research, as well as other industries.

In many market research projects, personal data is being collected, which means you have to have a basic understanding at least, of what GDPR entails. In this article we want to inform you about steps you can take to ensure you comply with the new legislation, to avoid potentially high penalties.

What is the GDPR?

The General Data Protection Regulation (GDPR) is a regulation by which the European Parliament, the Council of the European Union and the European Commission, intended to strengthen and unify data protection for all individuals within the European Union (EU). The GDPR aims primarily to give control

Often, the information that can help answer certain specific questions (churn, cross &-upselling and employee flow) already exist within the company, hiding in plain sight.

It is often a challenge leveraging  data about your  customers or employees for insights to answer questions. If companies could improve their data practices at five important stages, they could become much more effective in solving some of the most pressing problems they face. We think the following steps are important.