New Avenues with Web Intelligence: Gaining value from cash register data

Welcome to the European Statistical System Collaborative Network (ESSnet) Web Intelligence Network project, which began in April 2020 and will run until March 2025. This is a continuation of the previous ESSnet Big Data and ESSnet on Big Data II projects, which commenced in 2016. The aim of the network is to contribute to National Statistical Institutes (NSI) understanding the need to change and advance the production of national statistics. New technologies and data sources have tremendous potential to improve statistical production. They offer a way to generate statistics in an effective, accurate, and cost-effective manner.

ESSnet Web Intelligence Network Project

Gaining additional value from cash register data by combining different data sources

Trusted Smart Statistics – Web Intelligence NetworkBackgroundThe Web Intelligence Network (WIN) project is creating an environment where an array of non-traditional data sources can be accessed by members of the European Statistical System (ESS) and beyond. This project works hand in hand with the Web Intelligence Hub (WIH), a platform where data is accessed. The Web Intelligence Hub (WIH) is designed to provide National Statistical Institutes (NSIs) exposure to new data sets, enabling data sharing within a trusted, secure environment. New data sets collated from modern technologies can help produce statistics more cost-effectively by harnessing the power of the digital era. The WIH will harness technology and, in turn, allow collaboration to benefit the modernisation of official statistics. The WIH will create a community of collaboration to: <ul><li>Learn together </li><li>Share knowledge, data, and methods </li><li>Build a partnership between NSIs </li><li>Enable cooperation and sharing of statistical solutions for NSIs</li></ul>Webinar ContentThe use of modern data sources in official statistics is a reality and has been well formulated in the foundations of Trusted Smart Statistics. One of the cases is to acquire additional knowledge based on the content of the websites. Extracting data from enterprise websites to get characteristics is a part of the project currently done by the Web Intelligence Network.The webinar will explore Statistics Sweden’s experiences and the challenges faced when using Consumer Price Index (CPI) production as an illustration. It will emphasise the prospect of increasing the accuracy of measurements in the CPI production process. The course will cover the following phases:1. Data gathering techniques and software:Web scraping using HTML and Application Programme Interface (API) scrapers and will touch on technical solutions that can be used to gather data. Combining in-house made software with third-party external solutions to provide users with a robust and steady stream of data.The webinar will also examine the challenges of using these techniques without looking at specific codes. It will look at how Statistics Sweden utilised in-house and third-party solutions to make the dataflow traceable and describes what is being gathered.2. Data suppliers for cash register dataThe webinar will review the contacting strategies used for cash register data, how this has been done at Statistics Sweden, and how they are now working to refine the process to get more data directly from suppliers&#x27; and companies&#x27; databases. The intent here is to emphasise the value of decreasing the supplier&#x27;s manual labour to answer surveys, such as data for the CPI and Turnover in the service sector.3. Combining sourcesThe webinar will discuss how this work stream of the WIN Project will combine sources with being able to create official statistics from the CPI, by using what was learnt from company A onto company B within the same coop groups.4. MethodologyThe baseline arguments will review the current way of implementing web scraping the CPI. Challenges will be reviewed and look at how these are resolved to improve the results. 5. ResultsThe webinar will talk about the journey so far, lessons learned, and how they were and will be implemented into the project&#x27;s work.Learning Objectives and OutcomesOur main aim is to share the lessons we are learning when exploring these modern data sources regarding all the stages of creating official statistics. These lessons stretch from the initial decisions on what to focus our efforts on to how we implement and gain the most from whatever data we gather.Target AudienceSince the ambition is to cover most of the statistical processes and implementation, we hope everyone involved in one of the phases related to the process of official statistics production can benefit from the webinar. Everyone else interested in new technologies in web data collection and processing for official statistics might benefit from the webinar too.Pre-requisites<ul><li>Basic knowledge of how to acquire data; process data; and analyse data</li><li>General knowledge of statistical methodological issues </li><li>Basic knowledge of cash register data and the coding regarding web scraping is optional. </li></ul>Duration: Two hours Presenters:Remy Kamali Petrus Munter

Presenters Biographies:Remy Kamali studied economics, econometrics and applied statistics at the University of Stockholm and Uppsala and holds master&#x27;s degrees in economics and Statistics. He had a short trial in academic research before joining State agencies in various positions. He recently joined Statistics Sweden as a senior project manager after 10 years at the Swedish Council for Crime prevention where he served as head of the unit for development of new crime statistics.
Petrus Munter studied statistics and economics at the University of Örebro and holds a bachelor’s degree in Statistics. He started working at statistics Sweden while he was a student at the university and joined the agency permanently shortly after he graduated. Petrus is a one of a few pioneers at SCB who have been exploring right from beginning new prospects of using new data sources in this ever-growing digital world. He has mainly focused on the Consumer Price index (CPI) combining with modern data sources such as Web scrapping and cash register data.
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New Avenues with Web Intelligence: Gaining value from cash register data

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