Data has always been required for knowledge production. Philosophers have already discussed data as being central and empirical fodder for knowledge claims. We used inductive reasoning to unravel what data is revealing about our world and even tried to use different models to interpret it through statistical tools. This view that data is fixed and context-depended body of evidence ready to be plugged into models and explanations has also become part of the discourse around Big Data . This plug and play model of data as a source of knowledge does not asks what is data in the first place as well as it does not deal with the capacity of data to produce insight. Finding a good interpretive framework is not all that is required for data to speak for itself. Hence the road from raw data to knowledge remains complex.
Sabina Leonelli and Niccolo Tempini ( data Philosophers) tell us that we need to raise two fundamental questions: What are the fundamental conditions that are required to make data useable/ reusable? And what implications that data processing carry not just for the content of knowledge that is being processed but for the extent to which this knowledge can ground interventions in the world and inform political , scientific, social and economic debate? In this context, a focus on moving data or data journeys becomes very important. We have the challenge to follow data from its material production through human interaction with the world and among themselves, to dissemination through various forms of aggregations ( data sets , data series , indicators) and vehicles ( data bases , publications, archives) and ultimately to their use as knowledge claims. During these journeys data experiences many different types of encounters—with other data , diverse groups of users, specific infrastructures and technologies, and political, cultural, and economic expectations —which affect and shape data itself and its prospective useability.
Viewing data in travel, we see data in its dynamism and are led to decode how travel changes its properties in response to the environment and relations that come on the way. Data travel is often choreographed and regulated to achieve variety of goals. What comes to be seen as datum at any given time is itself is a result of a journey. These journeys far from being linear are often full of detours and unexpected and unpredictable changes largely due to the complex and diverse social networks and contexts responsible to make data move. This means data is not an unmoved mover that is used to move us. It is a moving mover that is employed to move us. This consideration of data in motion offers us a wealth of insights that often remain forgotten when we consider data as inert and unmoving.
Data journeys transcend and contest disciplinary boundaries both in methods used to track and analyse it as well as in domains in which data comes to be seen as valuable. This means epistemic cultures and context -specific norms shape the journeys and the use of data. Thus, data that was collected for a particular purposes can undertake new journeys towards new settings and unforeseen uses. We have to accompany data from the time of its extraction where data is already influenced by the kind of theories and instrumentation that we use. Next is the vehicle that we use for data travel. It will also impact data and influence its interpretation. Next comes the journey of data which is its coming together for analysis. Here we concentrate on data cleansing, clustering , and visualising. This coming together of data from different sources undergoes transformation and is render usable to achieve specific goals. After this comes the stage of data sharing . Here a special attention is given to the circumstance and implication of data sharing looking carefully at the tight intersections between the decision about who can access data and the criteria used to evaluate and regulate its quality and reliability. Data then travels to the interpretation point. Here we critically view the conditions under which data is interpreted. We have the challenge to carefully highlight the ways in which the commitments to analytic techniques , instruments and concepts as well as consider decisions around which some information is deemed as data and meta data which then may need to be transformed in order to fruitfully use or reuse the consequent data for specific purposes.
This means data journeys are very important to be studied to understand how we can make the best use of data. Hence, we have to focus our attention on the procedures that are used to render data actionable., credible, and accountable to various publics and goals involved in data journeys ranging from clinical settings to public health , related policies. We will also need to critically question the very narrative of authentication and discovery that often underscores the use of data as evidence. While we walk the full arch of data journeys, we do not take our eye away from the specific stages of data travel and unearth the deep interrelations and intersection across those stages. This dynamic approach reveals that data is deeply complex and its meaning and usefulness emerges dynamically over its journeys. This attention to the journeys of data and it transformation over its travel is important to understand what comes to be counted as data and how it is used and reused as evidence to produce knowledge. Data mobility and interoperability ( use in different context) are the twin properties that makes data a new power house. We ,therefore, can ask ambitious and innovative question to data using machine learning and algorithms. This short study tells us that it does not depend on the size of data but the way data is made moveable and interoperable will tell us how we can use data fruitfully. This study also reveal us that when it comes to data practices one size does not fit all. We have diversity of data type and instrumentation of data gathering and processing affecting the meaning and the use of data as it travels from its harvesting to its application or use .
