Most businesses have a lot of data stored on their systems – But what happens with this data after it is filed away? A society without data has become inconceivable. Data are continuously generated and stored, in such vast amounts that it is often too much for people to process. This is where smart machines and self-learning algorithms come in: they are not only extremely useful but often also necessary to provide us with insights that were hitherto invisible.
Sometimes some interesting and complex relationships are discovered after linking various data sources. Many businesses purposefully collect data to get to know their clients better so that they can offer them a better service. Other organizations manage data that is necessary (or even mandatory) to provide their services.
How do you make the step from data to data-driven research? What are the required steps and what sort of things should you bear in mind? Are there any particular issues? You may be faced with a lot of questions when you first start to consider the use of data-driven research.
We can sometimes forget important aspects of data-driven research or overlook critical details, even in the case of advanced analyses. That’s why a guide with a clear overview of the various aspects, customized for a specific project, research project or topic, can be very handy. You may find that your data suddenly plays a key role in policy-related decisions.
Centerdata provides customized AI guides. The guide comes complete with a step-by-step plan to make data-driven research even more accessible for your particular issue or organization. We also offer feedback opportunities for explanations and consultation.
AI guide and step-by-step plan for the City of The Hague
We developed a comprehensive AI guide for the City of The Hague to facilitate helping people on benefits back into work. Through data-driven research, the number of people getting off benefits can be predicted using predictive and clustering techniques, which information then serves as policy input. By providing the City of The Hague with specific recommendations and by elaborating a five-step plan, advanced analyses and predictions can be made for policy-related solutions.
Algorithms and techniques that enable autonomous learning for computers
Our world is full of devices and applications that can rapidly generate, store and transmit large amounts of data. This means that huge amounts of data are available for a wide range of analyses. Consider for example social media data, self-driving vehicles full of sensors, intelligent home and office equipment, internet and browser behavior, digital camera images, smartphone apps and other wearables.
These fast growing quantities and different types of available data combined with cheaper and more powerful processing of large quantities of data and affordable data storage have recently generated a lot of interest in machine learning and deep learning. These techniques are based on the recognition of patterns for complex and multispectral data.
By creating autonomous software or using iterative feedback to discover the links between the various data, patterns are found and anomalies identified. This results in a system for finding clusters and classifications.
The deep learning technique can learn to identify so-called discriminative features in large data sets that are both systematic and automatic. This heralds the switch from data sorting by individuals to so-called unsupervised learning.
Deep learning plays an important role in the analysis of large quantities of multidimensional and complex data, such as data collected through sensors. We have experience with the application of deep neural networks on terabytes of triaxial motion measurement system data to recognize activity at both algorithm and infrastructure level.