Associate Professor, Department of Political Science and International Relations, School of Social and Political Sciences, University of the Peloponnese, Greece
Submission: April 15, 2022; Published: April 26, 2022
*Corresponding author: Nikitas-Spiros Koutsoukis, Associate Professor, Department of Political Science and International Relations, School of Social and Political Sciences, University of the Peloponnese, Greece
How to cite this article:Nikitas-Spiros K. The need for “Mundane Analytics”. Ann Soc Sci Manage Stud. 2022; 7(2): 555710. DOI: 10.19080/ASM.2022.07.555710
Success stories of data analysis are a daily occurrence in a very wide range of applications from ‘pure’ to applied sciences from medical to business applications and beyond. However, there are other important uses for the data analysis software toolset, hardly of interest scientifically, that can make a great impact on the work lives of people dealing with plenty of data, on daily basis, just not in a data analysis context, omnipresent in administrative corridors where the venerable spreadsheet still rules. In this opinion we argue that there is a true need for ‘Mundane Analytics’ and we suggest to data analysts to spend some of their altruism on these; simply to help out colleagues that are unaware of the power and the impact analytics can make.
Analytics is one of the most prominent trends in the business and the academic world, often in synergy. Daily, each news media is, most likely, going to have a piece that is relevant to data analysis. In fact, it is hard, if not impossible, to not find a data-analysis relevant and newsworthy piece.
With a data analysis core, at one end of a (very wide) spectrum, analytics, data science and business intelligence may seem more related to statistics and ‘number crunching’. At the other end, machine learning and artificial intelligence are used to fuse new knowledge in tangible (e.g. autonomous driving, managing smart cities) or less tangible outputs (e.g. predicting the stock market).
In a few words, there is a real need for sophisticated data analysis in all types of applications from ‘pure’  to applied sciences , from medical  to business applications , from ‘fun’ projects  to the gig-economy  for data scientists. For example, in the US, some groups took this application level to, you could say, ‘crusade’ level. Even though this is not a strict ‘data analysis’ effort, it is certain that most, if not all, of the implemented projects involve data analysis .
From another perspective, in our postgraduate program “Global Risks and Analytics” we venture between the extremes of tangible and intangible. The program focuses only on three subjects over two semesters and a master’s article: International Politics, Risk Management and Analytics. The learning goal is to provide the theory, tools, and skills necessary to fuse the three subjects into a novel competence integrating quantitative and qualitative methods in factual analysis, for foresight or comprehension, of the world dynamics (as opposed to only argumentative as is often the case when international politics experts analyze events with world-wide impact sifts and events). International Politics, to gain an insight on the driving forces and the international dimensions of events around the world and then use, accordingly or in tandem, Risk Management to address them before hand or Analytics to elicit a more factual and scientifically valid comprehension of the issues at large. From our part, we set a baseline  and followed up with some collaborative work that evolved the principle further empirically [9,10]. We are proud that some our students had the motive and tenacity to take on this principle to achieve publication status, some in high quality journals [11-14].
All very promising, indeed. But is there a real need for less sophisticated, mundane even, uses of analytics tools? A need to use analytics tools to make work life easier, simpler in some situations, even if there is no science or medical or other higher purpose? I think so. One can use the analytics toolset to simplify data-intensive chores and workflows for those who need it. Hint: there’s plenty of them but they are almost invisible in data science radars. Still, they just work with bucketloads of data, daily, repeatedly, but rather mundanely for any type of higher order data science. Enterprise information systems are still lacking in some fronts.
We are a public-state university and textbooks are provided,
free of cost, to each undergraduate student for each of their
semester courses. However, students cannot get a second copy nor
the textbooks for a course that they are not registered to, nor more
textbooks than the number of courses they need to complete to
finish their degree. Textbook dissemination is centrally managed
by the Ministry of Education via a single platform, for all the
undergraduate students, for all the (public) universities in the
country (26 in total these days). Each student, each semester,
uses single-sign-on (SSO) to login to the ministry’s platform and
selects their textbooks from a recommended list for each course
they registered to in the semester. The student then simply waits
to get or pick up the textbooks as and when available. The process
repeats every until graduation. But there is a catch at the backoffice.
Only the university has the information to verify, per course,
which students are (not) eligible to receive the textbook. So, the
distributed textbooks list from the state’s system, needs to be
cross checked by the corresponding department of study for each
student’s eligibility to receive a specific textbook. Statistically, for
every 1000 students probably no more than 2 to 5 are not eligible.
A few needles, but still in a haystack.
I discovered all this by chance, due to my appointment as a
vice rector at the time, when I received a huge excel file to pass
on to the individual departments for the cross check. Apparently,
ex officio, the specific VC’s seat is institutionally responsible to
oversee the process; thus, receive and disseminate the file for the
cross check and so I dutifully did. Being curious, however, when I
passed by my department, I asked the registry how they do carry
out this chore. Apparently, it took two people two dedicated days
(each semester). That is one person working with the master list
while the other fetches the per-course list or, if you will, cross
matching the master 2,500 records to each of the student lists in
41 courses and each course ranging between 15-300 registered
students in our case. We are a relatively ‘small’ department in
terms of student numbers’ both university-wide and nationwide;
the so called ‘large’ departments may have up to three times the
number of students and some departments (smaller or larger in
student numbers) may offer up to 60 courses or more. You get
the picture what these numbers imply for the administrative staff
assigned to do the validation. I am not jealous.
So, I asked the registry staff to give me the lists, guide me as to
what I am looking for and I gave it a go in my preferred analytics
software, KNIME . Analytics software have powerful data
handling tools. In our case the ‘mission-critical’ ability to read files
at bulk, to aggregate their records and filter any mismatches. Once
the workflow was setup properly (it took about half an hour with
some validity checks), the whole process took a no more than a few
seconds on a 15-year-old PC. So, from two days to a few seconds,
in half an hour, simply because I was aware that there is a software
tool that can do ‘that’. Easily.
Of course, comparing spreadsheets in not the holy grail of
analytics. But the joy being able to help my colleagues save those
two days, is just as satisfying. I am very pleased that I was part of
such a ‘mundane’ solution.
My argument is that, what may seem, at least initially,
unchallenging to a career-oriented data scientist may truly be a
‘blessing’ or a ‘holy grail’ even, to someone who is unaware of the
toolset and capabilities of analytics and its capacity to improve
their real-life work experience, daily and immediately. Knowing
their true value, looking down on “mundane analytics” hinders
some of the altruism data analysts may use to help others with
their expertise. I am not suggesting to build a career on mundane
analytics; just to remain aware that their analytics skills can make
an immediate positive impact to a someone else’s work life.
So, in my opinion, the need for “Mundane Analytics” exists
in the ‘real world’, not too far from the data analysis main stage,
though certainly not within it. And while data scientists and
analysts may be looking, understandably, in another direction,
I doubt that the need for “Mundane Analytics” will ever, ever go
Daskalopoulou M, Koutsoukis NS, Fakiolas ET (2018) Government effectiveness in the Eurozone during the world financial crisis 2007-2016. Social Sciences Tribune 18(70): 136-180.
Gionis Ch, Koutsoukis NS, Fakiolas ΕΤ (2019) The EU integration process: mapping convergence and divergence of the member states during the debt crisis. In Klapsis Α (Ed.), Special topics in Diplomacy and International Organization, Papazisis publishing.