Big data or Thick data: two faces of a coin

Big data or Thick data: two faces of a coin

May 24th, 2017

Big data or Thick data: two faces of a coin which can be defined as follows.

  • Big data is a term for large or complex data sets that traditional software has difficulties to process (capture, storage, analysis, curation, searching, sharing, transferring, visualizing, querying, updating, etc.). However, the term often refers to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime and so on.”
  • Thick data is generated by ethnographers and anthropologists adept at observing human behavior and its underlying motivations. Thick data is qualitative information that provides insights into the everyday emotional lives of a considered population.

Big data or Thick data: two faces of a coin

To date, thick data and big data have been independently supported and used by rather different people: organizations grounded in the social sciences vs. corporate IT functions.
A perfect example of silo culture. Big data or Thick data: two faces of a coin that should “talk to each other”, but most of the time do not because of silo culture.

Big data or Thick data: two faces of a coin

  • Big Data relies on machine learning, isolates variables to identify patterns, reveals insight. Big data gains insight from scale of data points, but loses resolution details. It does not tell you why those patterns exist.
  • Thick data relies on human learning, accepts irreducible complexity, reveals social context of connections between data. Thick data gains insight from anecdotal, small sample stories, but loses scales. It tells you why, but misses identifying complex patterns.

Focusing solely on Big Data can reduce the ability to imagine how the world might be evolving. Big Data only is not sufficient for risk assessment, and in particular Hazard Identification, and can create a distorted view of the risk landscape surrounding an entity.

Known knowns and unknown knowns

If one is seeking a map of an unknown risk territory (risk landscape) Thick data is the tool one should use if data are scarce. As data availability grows, on its way to become “big data”, both techniques can and should be integrated.
When performing risk assessments of dams, mining operations, etc. We always collect and analyze “stories”, anecdotes, loss reports to gain insights of “pre-existing” states of the system.

  • In the case of innovative companies that insight can be highly inspirational.
  • In the case of dams, that insight can tell us that a dam that “looks wonderful” actually has a “congenital defect” that raises the probability of failure.

Big data would not be capable of shoring that, but could probably reveal a pattern between third party observations and meteorology, or any other group of variables, that could raise an alert on “shorter terms” emergent hazards.

Integrating Big data or Thick data: two faces of a coin

Working successfully with integrated Thick and Big Data, certainly enhances any risk assessment. Big data or Thick data: two faces of a coin that should “talk to each other”, breaking up the silo culture.

Over the years we have found ways to integrate data from multiple sources and nature in our risk assessments.
Incomplete Thick data sets are routinely used in conjunction with expert opinion and literature to generate first a prior estimates of the probability of occurrence of hazards and failures. This immensely increases the value of the first cut risk assessment which can then be updated using big data and Bayesian approaches.
That approach also makes it possible to enhance the value of Big data, avoid squandering the capital and running cost necessary to get Big Data which, recent studies have shown, oftentimes remain virtually “unused”.


Integrating Big data or Thick data: two faces of a coin brings value and should be fostered. Using Big Data in isolation can be problematic, thus it is crucial to explore how Big Data and Thick Data can supplement each other. This demand the integration of qualitative evaluation, expert based judgments with “hard” quantitative data.

While we recognize that melding big and thick data together isn’t easy, at Riskope we do it on a daily basis.

Contact us to learn more and to see how this could bring more value to your projects!

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