Page 5 - IOTA Good Practice Guide

1. Data management

Many tax administrations experience data management as their biggest challenge, as they try to
transform into a data-driven organization. Having access to the right data will enable organizations to
draw the right conclusions and build valuable solutions. But what is data management? Asking this
question to ten different people will give ten different answers. Data management includes topics like
data preparation, data quality, data governance, data storage, data access and data security. One can
easily drown in the numerous frameworks and approaches for managing data. Therefore, this chapter
only focuses on some specific topics around data management, which altogether are far from
exhaustive for data management as a whole. Nevertheless, the given insights are worth sharing and
discussing.

Data quadrant model (by Ronald Damhof)
Level: basic
Contact person: Bastiaan Veldkamp, The Netherlands

Problem:

A common pitfall in many organisations
nowadays is that a lot of focus is put on
creating innovative information
products, and less on requirements for
putting these in production. The strong
focus on innovative information
products is caused by a lack of insight of
the different kinds of data preparation
that exist and their different
requirements.

Solution:

The Netherlands Tax and Customs Administration (NTCA) however strives to optimise how ad-hoc
models and data that are created by their data scientists find their way into the structured production
process. In an interview with Ronald Damhof we will show how his Data Quadrant Model helped the
NTCA to organise its thinking around different types of data preparation. It starts with a basic
assumption that data deployment starts with raw materials and ends up in some sort of product. And
in the process of getting raw materials to end products, logistics and manufacturing is required. It also
starts with the basic assumption that reliability and flexibility are both wanted, but are mutual
exclusive. And lastly, it starts with the basic notion of the 'push pull point', which stems from the
logistic- and manufacturing literature we were taught in high school about push systems and pull
systems.

Data layer architecture
Level: basic
Contact person: Bastiaan Veldkamp, The Netherlands

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