European digital strategy foresees ever increasing use of data in the coming years. It predicts a 530% increase of global data volume between 2018 and 2025. The European Data Act is in the proposal form and initiatives for further use of data are planned.
The NAIADES project is on course to showcase the benefits of digitalization of the water industry. Right now, the data has been collected on all pilot sites and continues to be collected and pushed to the platform where various models and predictions are available based on the data. All the collected data is signed and KSI Verifier checks all the data that enters the NAIADES platform. You can read more about Guardtime’s KSI Signatures in our previous blog post.
As data is being used and reused, the lifecycle is extended to months, years, and even decades. Using older data that has been moved and stored brings new challenges for ensuring the data is unchanged during the previous processing. Long-term integrity guarantees for water data allow us to observe patterns and changes that occur over months or even years, or to reuse the data for a different purpose much later. KSI Signatures offer a way to check integrity of data even years after signing.
For example, let’s imagine a municipality that has been collecting data about water consumption from all public buildings for years and now it has chosen to conduct a full analysis of the data to find places to reduce water consumption. The water consumption data has been stored in a database that was built when the data collection first started in 2014 and has not been upgraded or checked.
Table 1 Consumption data collected and recorded in 2014 for a building
The database was configured for integers but the water data has been all sent in decimal format. The database rounded all the values to the nearest integer. This introduced a significant error and lost a lot of valuable information. See how the weekend consumption is now indifferentiable from the weekdays! If the data would have been signed at collection, this flaw would not have occurred. Looking at the actual measurements, an analyst would easily see the baseline consumption at weekends, when the building is nearly empty, is significant and could recommend checking for leaking taps or pipes in the building.
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