DATA SCIENCE
Extracting value from data
Every day, organizations produce huge amounts of data. But much of it is not used to its fullest effect, or even discarded. That’s because the information is, in many cases, a by-product of regular business processes. However, ‘useless’ information usually is anything but. Businesses simply don’t have the knowledge or capacity to process the information properly and put it to use in improving or even transforming the business. Without processing, the true value of data remains firmly locked away.
Approach
To extract the knowledge needed for decision making, the information must be broken down and thoroughly analyzed. This calls for advanced and structured techniques and insights in the way data is generated in the first place. For that, the first step is to fully understand the business itself, followed by understanding the data. The data needs to be prepared, and after that it can be made the subject of data science.
Examples
- Combatting identify fraud: analysis of streaming data to identify misuse of offered services.
- Failure prevention: By analyzing data generated by maintenance, inspections and faults, patterns can be identified and leveraged to prevent future system failures.
- Association mining: A special form of pattern mining in categorical data. Association mining uncovers failure patterns and root-cause indications.
Unleash the full potential of data within your organization?
UREASON assists you in tackling the business challenges within your organization. Our one week intake workshop is the first step in our collaboration proposal, during which we work with you to analyze your challenges, what knowledge needs to be extracted, data availability and data quality. Together we decide what level of support fits your needs best.
