Data Science Innovation

‘To get off to a flying start, an experienced expert is vital’

Gerard Van Zomeren

Project Manager @ Gemeente Roosendaal

Are we able to recruit the right professionals? With 80,000 residents, do we have sufficient data at our disposal? And is it worth the investment? The municipality of Roosendaal decided to go for it and set up a data science department. Pipple assisted by offering their knowledge.

A team of four existing and two new employees was formed: the datalab. They learned to model with data and to build calculation models. Also, they provided the required tools and inventoried the needs of the business.

Insidiously simple
The experience brought in by Pipple was much welcome, says project manager Gerard van Zomeren. “Data science sometimes seems insidiously simple. As a consequence, our expectations were too high at the beginning. We, for example, thought to be able to realize a model for predicting our WMO (Social Support Act) expenses within a few months. However, there was still so much to learn and to configure.” According to Van Zomeren, you do not have to be 100% ready before starting to practice data science. But having an experienced expert on board is unquestionably vital to realize a flying start.

Predicting
The high expectations were not that surprising. Van Zomeren: “A municipality generates a lot of data which we can use to tackle matters in a smarter way. Of course, taking all the privacy guidelines into account.” Pipple made them recognize their possibilities, think of ideas, and helped them to put these into practice while sparking the enthusiasm of other colleagues.

For example, Pipple set up an image recognition algorithm for detecting solar panels. Also, the Beheer (Management) department proposed a great idea. Van Zomeren: “At this point, we still assess the quality of the roads ourselves on the basis of video footage. This can, however, be done much more effectively when we apply automatic image recognition. By also adding data, such as the age of the road and the degree of usage, we are able to predict which maintenance lies ahead. Or consider smart cities. Using the available data on occupation of parking lots, air quality and air temperature, the municipality is able to proactively adjust the routing of traffic flows when circumstances require so.

Social domain
Something they additionally learned from the early beginning: focus is required. Van Zomeren: “We start in the social domain; our neighborhood-oriented approach. It would be great if the data could teach us about the best approach for each neighborhood to fight poverty and solitude.”

Gerard Van Zomeren

Project Manager @ Gemeente Roosendaal

Are we able to recruit the right professionals? With 80,000 residents, do we have sufficient data at our disposal? And is it worth the investment? The municipality of Roosendaal decided to go for it and set up a data science department. Pipple assisted by offering their knowledge.

A team of four existing and two new employees was formed: the datalab. They learned to model with data and to build calculation models. Also, they provided the required tools and inventoried the needs of the business.

Insidiously simple
The experience brought in by Pipple was much welcome, says project manager Gerard van Zomeren. “Data science sometimes seems insidiously simple. As a consequence, our expectations were too high at the beginning. We, for example, thought to be able to realize a model for predicting our WMO (Social Support Act) expenses within a few months. However, there was still so much to learn and to configure.” According to Van Zomeren, you do not have to be 100% ready before starting to practice data science. But having an experienced expert on board is unquestionably vital to realize a flying start.

Predicting
The high expectations were not that surprising. Van Zomeren: “A municipality generates a lot of data which we can use to tackle matters in a smarter way. Of course, taking all the privacy guidelines into account.” Pipple made them recognize their possibilities, think of ideas, and helped them to put these into practice while sparking the enthusiasm of other colleagues.

For example, Pipple set up an image recognition algorithm for detecting solar panels. Also, the Beheer (Management) department proposed a great idea. Van Zomeren: “At this point, we still assess the quality of the roads ourselves on the basis of video footage. This can, however, be done much more effectively when we apply automatic image recognition. By also adding data, such as the age of the road and the degree of usage, we are able to predict which maintenance lies ahead. Or consider smart cities. Using the available data on occupation of parking lots, air quality and air temperature, the municipality is able to proactively adjust the routing of traffic flows when circumstances require so.

Social domain
Something they additionally learned from the early beginning: focus is required. Van Zomeren: “We start in the social domain; our neighborhood-oriented approach. It would be great if the data could teach us about the best approach for each neighborhood to fight poverty and solitude.”

Gerard Van Zomeren

Project Manager @ Gemeente Roosendaal

Are we able to recruit the right professionals? With 80,000 residents, do we have sufficient data at our disposal? And is it worth the investment? The municipality of Roosendaal decided to go for it and set up a data science department. Pipple assisted by offering their knowledge.

A team of four existing and two new employees was formed: the datalab. They learned to model with data and to build calculation models. Also, they provided the required tools and inventoried the needs of the business.

Insidiously simple
The experience brought in by Pipple was much welcome, says project manager Gerard van Zomeren. “Data science sometimes seems insidiously simple. As a consequence, our expectations were too high at the beginning. We, for example, thought to be able to realize a model for predicting our WMO (Social Support Act) expenses within a few months. However, there was still so much to learn and to configure.” According to Van Zomeren, you do not have to be 100% ready before starting to practice data science. But having an experienced expert on board is unquestionably vital to realize a flying start.

Predicting
The high expectations were not that surprising. Van Zomeren: “A municipality generates a lot of data which we can use to tackle matters in a smarter way. Of course, taking all the privacy guidelines into account.” Pipple made them recognize their possibilities, think of ideas, and helped them to put these into practice while sparking the enthusiasm of other colleagues.

For example, Pipple set up an image recognition algorithm for detecting solar panels. Also, the Beheer (Management) department proposed a great idea. Van Zomeren: “At this point, we still assess the quality of the roads ourselves on the basis of video footage. This can, however, be done much more effectively when we apply automatic image recognition. By also adding data, such as the age of the road and the degree of usage, we are able to predict which maintenance lies ahead. Or consider smart cities. Using the available data on occupation of parking lots, air quality and air temperature, the municipality is able to proactively adjust the routing of traffic flows when circumstances require so.

Social domain
Something they additionally learned from the early beginning: focus is required. Van Zomeren: “We start in the social domain; our neighborhood-oriented approach. It would be great if the data could teach us about the best approach for each neighborhood to fight poverty and solitude.”

Are we able to recruit the right professionals? With 80,000 residents, do we have sufficient data at our disposal? And is it worth the investment? The municipality of Roosendaal decided to go for it and set up a data science department. Pipple assisted by offering their knowledge.

A team of four existing and two new employees was formed: the datalab. They learned to model with data and to build calculation models. Also, they provided the required tools and inventoried the needs of the business.

Insidiously simple
The experience brought in by Pipple was much welcome, says project manager Gerard van Zomeren. “Data science sometimes seems insidiously simple. As a consequence, our expectations were too high at the beginning. We, for example, thought to be able to realize a model for predicting our WMO (Social Support Act) expenses within a few months. However, there was still so much to learn and to configure.” According to Van Zomeren, you do not have to be 100% ready before starting to practice data science. But having an experienced expert on board is unquestionably vital to realize a flying start.

Predicting
The high expectations were not that surprising. Van Zomeren: “A municipality generates a lot of data which we can use to tackle matters in a smarter way. Of course, taking all the privacy guidelines into account.” Pipple made them recognize their possibilities, think of ideas, and helped them to put these into practice while sparking the enthusiasm of other colleagues.

For example, Pipple set up an image recognition algorithm for detecting solar panels. Also, the Beheer (Management) department proposed a great idea. Van Zomeren: “At this point, we still assess the quality of the roads ourselves on the basis of video footage. This can, however, be done much more effectively when we apply automatic image recognition. By also adding data, such as the age of the road and the degree of usage, we are able to predict which maintenance lies ahead. Or consider smart cities. Using the available data on occupation of parking lots, air quality and air temperature, the municipality is able to proactively adjust the routing of traffic flows when circumstances require so.

Social domain
Something they additionally learned from the early beginning: focus is required. Van Zomeren: “We start in the social domain; our neighborhood-oriented approach. It would be great if the data could teach us about the best approach for each neighborhood to fight poverty and solitude.”

Gerard Van Zomeren

Project Manager @ Gemeente Roosendaal