Alternative Data Analysis is a cutting-edge approach that harnesses non-traditional, diverse datasets to uncover valuable insights and drive informed decision-making. By analyzing unconventional sources such as reviews data, geo data, and text data, this method enables businesses to gain a competitive advantage, identify trends, and predict market movements. It offers a fresh perspective on data analysis, revolutionizing the way organizations make strategic choices.
How can I predict trends early, identify new opportunities, and get a deeper understanding of my market?
With our Alternative Data Analysis approach, clients acquire a solid data-driven decision-making foundation. We gather and clean vast datasets, analyze them, and present them in an understandable manner, enabling customers to rely on informed choices and get a more complete picture. Analyses can be based on historical, real-time, or future data (forecasts). However, we go beyond mere data aggregation and integration. We can recognize and uncover hidden insights.
Furthermore, we support our customers proactively. Even if they lack internal data, we can access thousands of public datasets to gain relevant insights for clients. Our Alternative Data Analysis approach revolutionizes decision-making processes, ensuring a competitive advantage in today’s dynamic business landscape.
With our Alternative Data Analysis approach, clients acquire a solid data-driven decision-making foundation. We gather and clean vast datasets, analyze them, and present them in an understandable manner, enabling customers to rely on informed choices and get a more complete picture. Analyses can be based on historical, real-time, or future data (forecasts). However, we go beyond mere data aggregation and integration. We can recognize and uncover hidden insights.
Furthermore, we support our customers proactively. Even if they lack internal data, we can access thousands of public datasets to gain relevant insights for clients. Our Alternative Data Analysis approach revolutionizes decision-making processes, ensuring a competitive advantage in today’s dynamic business landscape.
This webinar is about the potential of advanced analytics for alternative data sources to enhance the understanding of consumer behaviors, competitor strategies, market trends and many more use cases.
This webinar is about the potential of advanced analytics for alternative data sources to enhance the understanding of consumer behaviors, competitor strategies, market trends and many more use cases.
We create consistent data sets also from unconventional sources such as reviews data, geo data, and text data. Moreover as part of Statista, we have access to a wide range of exclusive data and information for our projects.
For the evaluation we work with all statistical methods including multivariate procedures, statistical modelling, distribution curves and macro-environmental analysis (e.g. PESTEL analysis).
The most important prerequisite for a successful analysis is a valid database. At the beginning of the project, we therefore create a consistent data set from individual data points – often from different sources and systems. To optimize the quality of data, it is cleaned, categorized and transferred into a clear structure during consolidation.
In addition to standard statistical methods, we also use in-house developed AI tools for data analysis. These AI tools can leverage various alternative data sources with AI driven analytics to support Statista Q’s clients. One example of these AI Tools ist ReQiew – our automated tool for semantic text analysis. Focused on consumer reviews and related ratings.
Our digital dashboards guarantee the availability of the data insights on an up-to-date level for a wide circle of recipients whilst also showing important indicators in a graphical interface or enable the performance of individual analyses and deep dives.
Based on the statistical results, our data experts carry out further analyses that are more qualitative in nature. In this project phase, we examine whether there is actual causality or mere correlation in the previously identified relationships. We also validate the results via external factors and industry developments. We find explanatory patterns for the analysis findings and check the plausibility of the results. Based on this in-depth data analysis, our analysts finally derive concrete recommendations for action and provide a crucial foundation for decision-making.
