data analysis loop

It is impossible to catalog and classify all examples of data analysis, let alone list them on an arbitrary scale of “greatness”. Data analysis isn’t one finite set of events with a beginning and an end, it is a way of looking at the world around us. Indeed, if the definition is taken to extremes, consciousness itself could be seen as data collection and analysis: with further collection and analysis being driven by insights of previous iterations. 

Of course, this conscious loop has many branches. To map them all out would create an infinite recursive fractal, where each step is subject to further inspection, action and change. 

In the simplified, abstracted model of the conscious loop, the greatness of an analysis can only be considered in terms of its effects. The data analysis can be considered successful if:

  1. the data collected is checked for quality
  2. analysis is done with rigor
  3. conclusions flow from the premises
  4. useful actions are taken (or able to be taken)

If any step is missing, the analysis itself falls apart.  

With this in mind, I present my completely arbitrary choice of the prestigious first pick, the most successful example of data analysis ever in world history: the first demographic census.  

1. Sumerian census 

We will never know when or where the first person decided to record data about people or to what end. We do know it was useful, because the idea lasted, and written records of data start with ancient records of trade, recorded on clay tablets about five thousand years ago. The first time (that we know of) this kind of written data analysis had a disproportionate impact on many people at once was when the kingdom of Sumer (sometimes called Babylonia) started to record its population in order to distribute food stocks. Some of the tablets used for this census have survived to the present day, on display at the British museum.  

There is still much we don’t know about Sumer, but we know they achieved success in architecture, engineering, mathematics, warfare, law and administration (and the earliest written language). How much of this can be attributed to their data collection and analysis, we will probably never know.  

In time, census taking took hold in Ancient Egypt, where it was used to calculate and organize the labor force. The book of Numbers in the Jewish Torah records census taking for military purposes, and the Romans started recording demographic data around 600BC. The largest ancient census took place in the Han dynasty China in the 2nd century, recording a population of 59.6 million. Ancient censuses and the analysis of their data were extraordinary social and organizational achievements, probably aiding many ancient rulers in the governance of their lands and peoples.  

2. Florence Nightingale 

Florence Nightingale is probably most well known as a nurse, but she was also one of the first to use infographics to display the results of statistical analysis. According to Nightingale: “Diagrams [are] of great utility for illustrating certain questions of vital statistics by conveying ideas of the subject through the eye, which cannot be so readily grasped when contained in figures.” 

Nightingale used her statistics and connections with the ruling classes of Britain to fundamentally reform and modernize healthcare in the late 19th century, identifying sanitation and hygiene as critically important to health outcomes. She came to these conclusions through data collection and analysis, but her presentation of these imperial data were so iconic, they had the ability to reach those with the powers to enact change – impacting healthcare practice forever.  

3. John Snow 

Another healthcare related story that took place around the same time as Nightingale’s diagrams. John Snow (not the Game of Thrones character) was a British doctor who used data collection and data analysis to trace the source of a cholera outbreak in central London, and to come to the conclusion that cholera was transmitted by “an agent in the water” than by the accepted theory that it was transmitted by “bad air”.  

Snow used data collection to trace the cholera outbreak to two water companies who drew their water from the Thames river, virtually unfiltered. He notes that a huge, double-blind experiment fell into his lap: “No fewer than three hundred thousand people of both sexes, of every age and occupation, and of every rank and station, from gentlefolks down to the very poor, were divided into two groups without their choice, and, in most cases, without their knowledge; one group being supplied water containing the sewage of London, and amongst it, whatever might have come from the cholera patients, the other group having water quite free from such impurity.” 

Snow’s analysis of the subsequent data and his other works led to fundamental changes in water and waste management in London and other cities, saving many lives and contributing significantly to global public health.  

4. Abraham Wald 

Abraham Wald was a Hungarian mathematician who worked for the United States during World War Two.

His contribution to this list of great data analysis? Not falling for what we now call “survivorship bias”.  

When making warplanes, you need to consider armor. But armor is heavy: and heavier planes are slower and less fuel-efficient. No armor is a problem, but too much armor is also a problem, so Wald was assigned to calculate the optimum amount. He was presented with data from engagements all over Europe. The engineers noted that the planes had far more shots on the fuselage and wings – they concluded that these areas were in need of reinforcement.  

But Wald knew that sometimes The most important data is the data you don't have - Abraham Wald Click To Tweet. The most important data was “where do planes that don’t come back get shot?” The planes that returned safely had more shots on the areas that can handle more shots – Wald concluded that the areas with fewer recorded shots, needed the most armor.  

5. Climate Change 

It is hard to write an article on data analysis and not mention climate change. It is also hard, however, to describe the data analysis that has gone on so far as being “successful”. Although there has been a great deal of public awareness on the issues surrounding global climate impact: The facts are still opaque to some, with significant minorities of people not recognizing the severity of the problem.  

The first calculations of the greenhouse effect began in 1896, with Swedish scientist Svante Arrhenius calculating that a doubling of atmospheric CO2 would give a total warming of 5–6 degrees Celsius, and these ideas were developed at length in 1899, by Thomas Chrowder Chamberlin. Most scientists disputed or ignored the greenhouse theory developed by Arrhenius, until the 1950’s, where better spectrography, isotope analysis and understanding of ocean chemistry lead to a larger number of scientists arguing that CO2 could be a problem and that CO2 concentration was in fact rising.  

Throughout the years, the consensus that the atmospheric concentration of human-produced CO2 is increasing, and that this will have a detrimental impact on global climate, has only grown stronger. Subsequent data collection and analysis have birthed entire fields of science dedicated to climate change research.  

6. Bernard Widrow and Marcian Hoff 

From the global and the complex to a smaller, but still very influential breakthrough: the first neural network to be implemented to solve a real world problem. Widrow and Hoff built an analog neural network: a machine that could learn. You could say that this doesn’t seem like an example of data analysis, but it is, and a very important one at that. Widrow and Hoff invented a machine and an algorithm that could perform the conscious loop. It could collect data, analyze it, act (or make a prediction) and learn from its actions. This machine was called ADALINE. 

ADALINE could be used for anything, from balancing a broom on a moving rail, to predicting tomorrow’s weather better than a human forecaster. It was eventually used in signal processing, where it was used to filter echoes in telephone signals. 

Although Widrow’s prediction – that within 10 years of invention, adaptive computers would be just as widespread as digital computers – was incorrect, it is proving to be prophetic. 65 years after their initial conception, neural networks – with their ability to complete the conscious loop inside computers – are becoming widespread in almost every industry. Cars use them to navigate, search engines use them to provide results, doctors use them to analyze patient data, supermarkets use them to stock shelves – their importance in data analysis will only grow from here.  

For some entertaining 1950’s TV click here.

7. Moneyball 

This section takes its name from the 2003 book and 2011 film of the same name. I can recommend watching the movie if you haven’t already, as it is an excellent example of how to use data analysis for a competitive advantage. For those of you who don’t plan on seeing, haven’t seen or don’t care about spoilers: here is a short summary.  

The narrative follow’s Oakland Athletics baseball team manager Billy Beane’s attempts to field a competitive team with a very limited budget. Beane finds undervalued, talented players by using baseball statistics to evaluate performance instead of the more favored “intuitive” techniques of the past. Beane’s team go on to win 20 consecutive games, a record breaking streak that solidified data analysis as a force to be reckoned with in the world of baseball.  

8. Michael Burry 

Another example from Hollywood: Michael Burry’s analysis of – and bet against – subprime mortgages made him rich in the aftermath of the 2008 financial crisis and inspired the award winning film: The Big Short (2015).  

Michael Burry – a hedge fund manager – analyzed data on mortgage lending practices and correctly predicted that the United States housing market would collapse as early as 2007.  

The crisis was already at a point of no return and Burry knew this. Instead of trying to prevent the crisis, he managed to profit from it. His hedge fund, Scion Capital, ultimately recorded returns of 489.34%. 

9. AlexNet 

AlexNet is a convolutional neural network or CNN. CNN’s are extremely complicated in how they work, but quite simple in what they do. They usually work on image data. Given a data set of labeled images they can try to predict the label of an unlabeled image.  

You might already be familiar with them, but in 2012, they were a relatively novel concept. Even more novel was this particular CNN’s use of an as yet niche piece of hardware. The Graphics Processing Unit, or GPU.  

GPU’s are very good at processing image data, as they are very good at calculating matrix operations in parallel. All this means is that instead of doing calculations (like 2*2) in sequence, very quickly, they can do: 

All in one, giant computational step. GPU’s were only really used for gaming until then, but with AlexNet, they could be used to greatly accelerate CNN’s and other big data analysis applications, leading to the machine learning revolution of the present day.  

10. Audit 

One of the largest professional services firms in the world (250,000 employees in over 700 offices around 150 countries) provides assurance (including financial audit), tax, consulting and advisory services to companies. They deliver external audit services to their clients, examining financial records and operational procedures to assess for regulatory compliance – If this organization does the job right, their Customers can do their job right – avoiding millions in government fines 

Audits are traditionally highly manual, labor-intensive and lengthy activities involving in-person interviews, data sampling and the manual documentation of processes and risks. A feat of data analysis if ever there was one, but it doesn’t stop there!  

As businesses are supported by an ever-growing number of complex systems and big data sets, traditional audit measures are insufficient in capturing all audit risks and breaches 

Along comes ProcessGold! (okay, shameless plug but bear with me). ProcessGold’s Process Mining platform combined with the audit industry knowledge and expertise in external audit resulted in the development of the audit market’s most comprehensive process analytics and audit detection platform. 

The solution combines Process Mining algorithms and analytic platform to automatically reconstruct processes based on factual client data and controls (automatic data collection and processing). ProcessGold provides auditors with transparency and full audit coverage by delivering a holistic view of their client’s business processes. Using its proprietary technology ProcessGold allows the global organization to deliver multidimensional process maps in real-time to identify all audit risks and compliance breaches. 

Using ProcessGold, the auditors were able to complete the conscious loop for its clients. Any of their customers can now collect, process, analyze and act on data in near real time, allowing for rapid iteration and fact based, goal-oriented business transformation. 


As the wheel of time has turned, increasingly complex conscious loops have come into being, and their iteration has accelerated. From expensive, incomplete and time-consuming censuses in the ancient world, to the real-time analysis of minutely detailed business process information. In the future, as a part of the UiPath family, we at ProcessGold expect this iterative loop to turn faster and faster, incorporating machine learning, automized task analysis and big-picture strategic overviews to accelerate business evolution with hyperautomation. 

The impact of “big data” analytics is often manifested by thousands—or more—of incrementally small improvements. If an organization can atomize a single process into its smallest parts and implement advances where possible, the payoffs can be profound. And if an organization can systematically combine small improvements across bigger, multiple processes, the payoff can be exponential.” – McKinsey & Company  

jorim-theunsJorim Theuns, Marketing Trainee @ProcessGold