The data compass makes projects manageable
The areas of application for AI and data science projects are almost inexhaustible and as rich as the industries where they are used.Data analysis provides valuable insights for research and science, product development, sales and logistics, production, human resources, management, banking and many other corporate divisions and individual industries. One of the recurring constants of such projects is to identify patterns and regularities in data and to develop models that allow predictions and form the basis for decisions.
In order to structure all these different aspects, we at [at] have developed the data compass. It serves as orientation and the goal-oriented development of KI and data science projects. The Data Compass is independent of specific technologies or software providers and can be individually adapted to the existing IT solutions of our customers.The data compass divides each data science project into four successive stages:Business Processes, Data Intelligence, Predictive Analytics and Insights Visualisation. The Data Compass represents the sum of our many years of experience and more than 1,000 successfully implemented AI and Data Science Projectsin various sectors.
1. business processes
It all starts when our clients come to us with a more or less specific question or problem for which they are looking for a solution. Together we take care to understand this question comprehensively. To do this, we clarify and evaluate all the background, motivations and interests associated with the question. The more precisely we specify a question, the better it can be answered with the help of appropriate data. We consider it crucial for success to have all the people involved at the table right from the start. The earlier all affected departments and decision-makers are involved in the process, the easier and more effective the implementation will be.
As soon as the concrete question is available, we start planning and clarifying the framework conditions of a project. For this purpose, we use the analysis of the business processes to investigate which technical and analytical challenges need to be overcome on the way to finding a solution. Understanding the interaction of business processes and the analysis concept form the foundation.
2. data intelligence
In the subsequent phase, we translate the business or technically oriented question into a data-driven question. This is about determining exactly which figures, measured values and data are relevant. The data may already be available or concepts for data collection may have to be developed first.
The real challenge is to make very different data comparable with each other. Colloquially, one could say that data must "speak a uniform language". This aspect of a data science project can be enormously intensive in terms of time and not infrequently involves manual processes. That is why Data Intelligence is one of the crucial processes without which no reliable statements can be made. Data science requires "good", i.e. relevant, structured and valid data.
3. predictive analytics
In many cases, companies today are confronted with enormous amounts of data. In order to evaluate this Big Data, special algorithms are used or developed in-house that are able to recognise patterns and regularities in large amounts of data. The analysis of historical data can be used, for example, to develop prediction models. In the process, probabilities are calculated according to which a certain event or scenario will occur. In this way, trends can be recognised at an early stage and a reaction can be made to them. The election campaign of Barack Obama, whose campaign team analysed gigantic amounts of tweets from social media and adjusted their campaign accordingly, became famous. With the help of predictive analytics, we can either evaluate concrete questions and hypotheses or we look for the next (meaningful) question that hidden patterns and regularities in the data point to.
4 Insights Visualisation
For the human brain, data in the form of images is much faster and easier to process than endless rows of numbers in table form. Insights visualisation, the visualisation of data, is therefore not only important for presentations, but above all to be able to understand and interpret the information. Visualisation thus becomes an essential part of any analysis. Especially because data science is not exclusively aimed at IT experts, but also finds its field of application in management and the executive board, the results must be presented clearly and comprehensibly. Usability and information design are the key to making data science a part of everyday business practice.
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