Astronomical data is and has always been “big data”. Once that was only true metaphorically, now it is true in all senses. We acquire it far more rapidly than the rate at which we can process, analyse and exploit it. This means we are creating a vast global repository that may already hold answers to some of the fundamental questions of the Universe we are seeking.

Does this mean we should cancel our up-coming missions and telescopes – after all why continue to order food when the table is replete? Of course not. What it means is that, while we continue our inevitable yet budget limited advancement into the future, so we must also simultaneously do justice to the data we have already acquired.

In a small way we already doing this. Consider citizen science, where public participation in the analysis of archived data increases the possibility of real scientific discovery. It’s a natural evolution, giving those with spare time on their hands the chance to advance scientific knowledge.

However, soon this will not be sufficient. What we need is a new breed of professional astronomy data-miners eager to get their hands dirty with “old” data, with the capacity to exploit more readily the results and findings.

Thus far, human ingenuity, and current technology have ensured that data storage capabilities have kept pace with the massive output of the electronic stargazers. The real struggle is now figuring out how to search and synthesize that output.

The greatest challenges for tackling large astronomical data sets are:

Visualisation of astronomical datasets



Creation and utilisation of efficient algorithms for processing large datasets.



The efficient development of, and interaction with, large databases.



The use of “machine learning” methodologies



The challenges unique to astronomical data are borne out of the characteristics of big data. The three Vs: volume – amount of data, variety – complexity of data and the sources that it is gathered from and velocity – rate of data and information flow. It is a problem that is getting worse.

In 2004, the data I used for my Masters had been acquired in the mid-1990s by the United Kingdom Infra-Red Telescope (UKIRT), Hawaii. In total it amounted a few 10s of Gigabytes.

Moving onward just a matter of months to my PhD, I was studying data taken from one the most successful ground based surveys in the history of astronomy, the Sloan Digital Sky Survey (SDSS). The volume of data I was having to cope with was orders of magnitude more.

SDSS entered routine operations in 2000. At the time of Data Release 12 (DR12) in July 2014 the total volume of that release was 116TB. Even this pales next to the Large Synoptic Survey Telescope (LSST). Planned to enter operation in 2022, it is aiming to gather 30TB a night.

To make progress with this massive data set, astronomy must embrace a new era of data-mining techniques and technologies. These include the application of artificial intelligence, machine learning, statistics, and database systems, to extract information from a data set and transform it into an understandable structure for further use.

Now while many scientists find themselves focused on solving these issues, let’s just pull back a moment and ask the tough questions. For what purpose are we gathering all this new data? What value do we gain from just collecting it? For that matter, have we learned all that we can from the data that we have?



It seems that the original science of data, astronomy, has a lot to learn from the new kid on the block, data science. Think about it. What if, as we strive to acquire and process more photons from across the farther reaches of the universe, from ever more exotic sources with even more complex instrumentation, that somewhere in a dusty server on Earth, the answers are already here, if we would just only pick up that dataset and look at it … possibly for the first time.

Dr Maya Dillon is the community manager for Pivigo. The company supports analytical PhDs making the transition into the world of Data Science and also runs S2DS: Europe’s largest data science boot-camp.