It is about time
/“Why do you limit yourselves to the time dimension?” That is something we often hear when we talk about our analysis framework for time-oriented data. We immediately reply that it is not a limitation. The longer answer that usually follows consists of three key messages: time is everywhere (1), time is the key to understanding reality (2) and time is unique from an analysis point of view (3).
1.
Time is the most common dimension. We live in the world of time-oriented data. When we think about time in the context of data analysis we usually imagine a chart with univariate time series like stock prices. But the time component is much more common - it is present in most models created for phenomena we are interested in. We can talk about time-oriented data even if a single variable of such a model is associated with the time dimension. This applies to structured and unstructured data, for example to a collection of pictures with timestamps in their EXIF metadata. And with data stored digitally, even if there is no specific time variable in a model, we usually get information stating when this record was created or updated.
2.
We conduct data analysis because we are interested in reality. We want to learn from the past and use historical data to better understand consequences of events and actions. We also look into the future to learn about threats and opportunities and evaluate available decision options. Surprisingly, time can also be very relevant while explaining the present – in the case of non-trivial phenomena, it may be difficult to distinguish between strong and weak elements of a model while completely ignoring temporal context. The time component in data enables us to notice, analyze and predict changes. It creates opportunities to go beyond states, to understand how they are affected by events and to provide insight is the nature of underlying processes.
3.
We are better at dealing with states than processes. In the context of data analysis time can seem both familiar and confusing. Representation of time can be based on time points or can use an interval-based model. Time values can be expressed using different levels of granularity (e.g. months and weeks) with mapping between them not always straightforward (e.g. days and months). And there are practical issues with autocorrelation, seasonality, outliers, but also time zones, daylight savings or holidays (e.g. workweeks). There are many techniques, especially around time series, that can help address specific analysis challenges. Unfortunately, definition of an analysis problem and selection of appropriate algorithms usually require in-depth expertise.
We believe in making analysis of time-oriented data more usable and available to individuals who need to solve their problems rather than become data analysis experts. We don’t see focusing on the time dimension as a limitation but rather a unique opportunity, since time can become the shared dimension connecting data streams from different sources in an easy and natural way.
We simply change the way of looking at data – to always keep the time dimension in mind.
