Statistics is Dead – Long Live Statistics

To be an expert in a thematic field!

Lee Baker wrote an article that will please the whole community of official statistics where specialists of many thematic fields (and not alone statisticians or mathematicians or … data scientists) are collecting, analysing, interpreting, explaining and publishing data.
It’s this core message that counts:
“… if you want to be an expert Data Scientist in Business, Medicine or Engineering”  (or vice versa: An expert statistician in a field of official statistics like demography, economy, etc.)  “then the biggest skill you’ll need will be in Business, Medicine or Engineering…. In other words, …. you really do need to be an expert in your field as well as having some of the other listed skills”

Here is his chain of arguments:

“Statistics is Dead – Long Live Data Science…

by Lee Barker

I keep hearing Data Scientists say that ‘Statistics is Dead’, and they even have big debates about it attended by the good and great of Data Science. Interestingly, there seem to be very few actual statisticians at these debates.

So why do Data Scientists think that stats is dead? Where does the notion that there is no longer any need for statistical analysis come from? And are they right?

Is statistics dead or is it just pining for the fjords?

I guess that really we should start at the beginning by asking the question ‘What Is Statistics?’.
Briefly, what makes statistics unique and a distinct branch of mathematics is that statistics is the study of the uncertainty of data.
So let’s look at this logically. If Data Scientists are correct (well, at least some of them) and statistics is dead, then either (1) we don’t need to quantify the uncertainty or (2) we have better tools than statistics to measure it.

Quantifying the Uncertainty in Data

Why would we no longer have any need to measure and control the uncertainty in our data?
Have we discovered some amazing new way of observing, collecting, collating and analysing our data that we no longer have uncertainty?
I don’t believe so and, as far as I can tell, with the explosion of data that we’re experiencing – the amount of data that currently exists doubles every 18 months – the level of uncertainty in data is on the increase.

So we must have better tools than statistics to quantify the uncertainty, then?
Well, no. It may be true that most statistical measures were developed decades ago when ‘Big Data’ just didn’t exist, and that the ‘old’ statistical tests often creak at the hinges when faced with enormous volumes of data, but there simply isn’t a better way of measuring uncertainty than with statistics – at least not yet, anyway.

So why is it that many Data Scientists are insistent that there is no place for statistics in the 21st Century?

Well, I guess if it’s not statistics that’s the problem, there must be something wrong with Data Science.

So let’s have a heated debate…

What is Data Science?

Nobody seems to be able to come up with a firm definition of what Data Science is.
Some believe that Data Science is just a sexed-up term for statistics, whilst others suggest that it is an alternative name for ‘Business Intelligence’. Some claim that Data Science is all about the creation of data products to be able to analyse the incredible amounts of data that we’re faced with.
I don’t disagree with any of these, but suggest that maybe all these definitions are a small part of a much bigger beast.

To get a better understanding of Data Science it might be easier to look at what Data Scientists do rather than what they are.

Data Science is all about extracting knowledge from data (I think just about everyone agrees with this very vague description), and it incorporates many diverse skills, such as mathematics, statistics, artificial intelligence, computer programming, visualisation, image analysis, and much more.

It is in the last bit, the ‘much more’ that I think defines a Data Scientist more than the previous bits. In my view, if you want to be an expert Data Scientist in Business, Medicine or Engineering then the biggest skill you’ll need will be in Business, Medicine or Engineering. Ally that with a combination of some/all of the other skills and you’ll be well on your way to being in great demand by the top dogs in your field.

In other words, if you want to call yourself a Data Scientist you really do need to be an expert in your field as well as having some of the other listed skills.

Are Computer Programmers Data Scientists?

On the other hand – as seems to be happening in Universities here in the UK and over the pond in the good old US of A – there are Data Science courses full of computer programmers that are learning how to handle data, use Hadoop and R, program in Python and plug their data into Artificial Neural Networks.

It seems that we’re creating a generation of Computer Programmers that, with the addition of a few extra tools on their CV, claim to be expert Data Scientists.

I think we’re in dangerous territory here.

It’s easy to learn how to use a few tools, but much much harder to use those tools intelligently to extract valuable, actionable information in a specialised field.

If you have little/no medical knowledge, how do you know which data outcomes are valuable?
If you’re not an expert in business, then how do you know which insights should be acted upon to make sound business decisions, and which should be ignored?

Plug-And-Play Data Analysis

This, to me, is the crux of the problem. Many of the current crop of Data Scientists – talented computer programmers though they may be – see Data Science as an exercise in plug-and-play.

Plug your dataset into tool A and you get some descriptions of your data. Plug it into tool B and you get a visualisation. Want predictions? Great – just use tool C.

Statistics, though, seems to be lagging behind in the Data Science revolution. There aren’t nearly as many automated statistical tools as there are visualisation tools or predictive tools, so the Data Scientists have to actually do the statistics themselves.

And statistics is hard.
So they ask if it’s really, really necessary.
I mean, we’ve already got the answer, so why do we need to waste our time with stats?


So statistics gets relegated to such an extent that Data Scientists declare it dead.”

The original article and discussion –>here

About the Author

Lee Baker is an award-winning software creator with a passion for turning data into a story.
A proud Yorkshireman, he now lives by the sparkling shores of the East Coast of Scotland. Physicist, statistician and programmer, child of the flower-power psychedelic ‘60s, it’s amazing he turned out so normal!
Turning his back on a promising academic career to do something more satisfying, as the CEO and co-founder of Chi-Squared Innovations he now works double the hours for half the pay and 10 times the stress – but 100 times the fun!”

This post is taken from datascience.central and has been published previously in Innovation Enterprise and LinkedIn Pulse

Big Data and Official Statistics



Big Data is THE topic of the freshly published Statistical Journal of the IAOS – Volume 31, issue 2.


Five articles deal with Big-Data topics:

In the editorial Fride Eeg-Henriksen and Peter Hackl give an overview of the Big-Data discussions hold in Official Statistics. Here some remarks taken from this editorial:

‘In spite of the wide interest in and the great popularity of Big Data, no clear and commonly accepted definition of the notion Big Data could be established so far [3]. Modern technological, social and economic developments including the growth of smart devices and infrastructure, the growing availability and efficiency of the internet, the appeal of social networking sites and the prevalence and ubiquity of IT systems are resulting in the generation of huge streams of digital data. The complexities of the structure and dynamic of corresponding datasets, the challenges in developing the suitable software tools for data analytics, generally the diversity of potentials in making use of the masses of available data make it difficult to find a suitable and generally applicable definition. The often mentioned characterization of Big Data by 3 – or more – Vs (volume, velocity, variety – as well as veracity and value), does not capture the enormous scope of the corresponding data sets and the extensive potentials of making use of these data. A highly relevant aspect is that Big Data are so large and complex that traditional database management tools and data processing applications are not feasible and efficient means. This is illustrated by a look at the categories of data sources which typically are seen in the context of Big Data: Such data sources may be
– Administrative, e.g., medical records, insurance records, bank records.
– Commercial transactions, e.g., credit card transactions, scanners in supermarkets.
– Sensors, e.g., satellite imaging, environmental sensors, road sensors.
– Tracking devices, e.g., tracking data from mobile telephones, GPS
– Tracks of human behaviour, e.g., online searches, online page viewing.
– Documentation of opinion, e.g., comments posted in social media.


‘A general conclusion from the set of articles in this Special Section can be drawn as follows: The feasibility and the potentials of using Big Data in official statistics have to be assessed from case to case. In some areas the use of Big Data sources has already proved to be feasible. The choice of the appropriate IT technology and statistical methods must be specific for each situation. Also issues like the representativity and the quality of the resulting statistics, or the confidentiality and the risk of disclosure of personal data need to be assessed individually for each case. There is no doubt that Big Data will have a place in the future of official statistics, helping to reduce costs and burden on respondents. However, major efforts will be necessary to establish the routine wise use of Big Data, and new approaches will be needed for assessing all aspects of quality.’

[3] C. Reimsbach-Kounatze, (2015), The Proliferation of “Big Data” and Implications for Official Statistics and Statistical Agencies: A Preliminary Analysis”, OECD Digital Economy Papers, No. 245, OECD Publishing.


See also: Big Data in Action May 2015


Big Data in Action

Not long ago in Official Statistics the topic ‘Big Data’ was mostly discussed in a theoretical manner.


However, now more and more real, and solid examples appear and demonstrate how Big Data work and what their outcome could be.

Some of these examples come from (Official) Statistics. These institutions use Big Data as a source and start applying a new analytical paradigm.


Example 1: Global Pulse (UN)

Global Pulse is a flagship innovation initiative of the United Nations Secretary-General on big data. Its vision is a future in which big data is harnessed safely and responsibly as a public good. Its mission is to accelerate discovery, development and scaled adoption of big data innovation for sustainable development and humanitarian action. … Big data represents a new, renewable natural resource with the potential to revolutionize sustainable development and humanitarian practice.’ –>

See some examples of using Big Data below:

  • analyse social media data for perceptions related to sanitation, in order to baseline public engagement
  • use of mobile phone data as a proxy for food security and poverty indicators
  • how risk factors (e.g., tobacco, alcohol, diet and physical activity) of non-communicable diseases (e.g., cancer, diabetes, depression) could be inferred from big data sources as social media and online internet searches


‘This paper outlines the opportunities and challenges, which have guided the United Nations Global Pulse initiative since its inception in 2009. The paper builds on some of the most recent findings in the field of data science, and findings from our own collaborative research projects. It does not aim to cover the entire spectrum of challenges nor to offer definitive answers to those it addresses, but to serve as a reference for further reflection and discussion. The rest of this document is organised as follows: section one lays out the vision that underpins Big Data for Development; section two discusses the main challenges it raises; section three discusses its application. The concluding section examines options and priorities for the future.’


Example 2: CBS

In Statistics Netherlands (CBS) Big Data is an important research topic.




Several examples were studied:

  • road sensors for traffic and transport statistics
  • mobile phone data for travel behaviour (of active phones) or tourism (new phones that register to network)
  • social media data for a sentiment analysis tracking words with their associated sentiment in Twitter, Facebook, Google+, Linkedin, etc.



Example 3: Report of the Global Working Group on Big Data for Official Statistics

In March 2015, the forty-sixth session of the UN Statistical Commission received a report about Big Data in Official Statistics:

‘The report presents the highlights of the International Conference on Big Data for Official Statistics, the outcome of the first meeting of the Global Working Group and the results of a survey on the use of big data for official statistics.’ …

‘The potential of big data sources resides in the timely — and sometimes real‑time — availability of large amounts of data, which are usually generated at minimal cost.  …. before introducing big data into official statistics …. it needs to adequately address issues pertaining to methodology, quality, technology, data access, legislation, privacy, management and finance, and provide adequate cost-benefit analyses.’

UN Statistical Commission Forty-sixth session 3-6 March 2015,
The full report (


Example 4: UNECE Statistics Wiki on Big Data in OfficialStatistics

A dedicated wiki offers an overview of the ever growing activities in the field of Official Statistics and Big Data. It’s managed by the Geneva Office of UNECE.2015-05-23_BIGData-UNECE-wiki

The wiki provides an interesting Big Data Inventory



Which is the working model helping to get the best results from data? It’s not a specific qualification alone, it’s melting together multiple skills around data: data strategy, best methods, analytical and statistical skills. ‘The ability to work together quickly and flexibly is critical.’

‘Matt Ariker, Peter Breuer, and Tim McGuire from McKinsey give some hints in their article ‘How to get the most from big data?‘. And this could also be of interest for Statistical Offices, traditional specialists in working with Big Data.


Next Step after OGD: Government’s Big Data Scientist

Open Government Data (OGD) Initiatives have been important steps helping to give broader access to administrative data.

But there was some disappointment because OGD didn’t bring up the mass of apps many hoped. And meanwhile big discussions about using Big Data emerged.

Now the US make a step forward going for a Big Data Initiative: President Obama just nominated a Chief Data Scientist in his Office, DJ Patil.

‘Patil’s new role will involve the application of big data to all government areas, but particularly healthcare policy.’ (Source)


Data are from the Past

There’s a lot of discussion and also big hope about what is called Big Data and the role of Data Scientists. Will Data Scientists help us to create a better future?

Yes and no. ‘Making predictions about unprecedented futures requires more than data, it requires theory-driven models that envision futures that do not exist in data. Fortunately, digital tools also assist us in envisioning futures that have never been.’

This talk by Martin Hilbert published 13.01.2015 explains why Data are from the past and are not enough.

‘During his 15 years at the United Nations Secretariat, Martin Hilbert assisted governments to take advantage of the digital revolution. When the ‘big data’ age arrived, his research was the first to quantify the historical growth of how much technologically mediated information there actually is in the world.’

‘After joining the faculty of the University of California, Davis, he had more time to think more deeply about the theoretical underpinning and fundamental limitations of the ‘big data’ revolution.’

‘Martin Hilbert holds doctorates in Economics and Social Sciences, and in Communication, and has provided hands-on technical assistance to Presidents, government experts, legislators, diplomats, NGOs, and companies in over 20 countries.
At the University of California, Davis, Martin thinks about the fundamental theories of how digitization affects society.’ More

[Source: youtube]

Big Analytics

Technology Predictions for 2015

For 2015, Bing predicts the same three technologies for all continents to be in the first places: Wearables, Digital Personal Assistants and Home Automation.
All these technologies are part of the Internet of Things (IoT). They have many sensors delivering data, they are connected to several clouds and they provide information about processes or behaviours of people and environments.
Devices communicate with devices, devices with humans and humans with devices. So IoT is not another Internet or an Internet besides the already known. It’s an expansion of the Internet and it produces more massive data accessible for known and unknown parties: telecom and cloud providers, enterprises, governments ……
Finally also humans become part of IoT

IoT Infographic

These aspects are explained in a good infographic made by Postscapes in collaboration with Harbor Research.
The complete infographic can be found here:

IoT, Big Data and then?

Data from classical statistical surveys as well as from connected devices must be analyzed in order to be of use.
Analytics of things (of data produced by sensors of connected things including humans) are the new kind of statistical information every organisation, private person or government can use for their purposes, for well informed decisions and steering activities.  The usages are numberless and under no control.
Examples of such statistics and applications are already numberless:
  • Singapur itraffic
  • Some more example applications of IoT in Postscapes IoT toolkit
  • And some things are intelligent even without being connected, like thermostats.


Big Analytics, Data Science and Official Statistics

The quasi-monopoly of national statistical agencies having the resources to undertake huge surveys vanishes. A  new kind of statistics emerges – collected, controlled and used by new agents.
To get the  full potential of these data, special qualifications are needed. Classical statistical analysis expands to Data Science. (Big) Data need (Big) Analytics and this explains why many predict that statistician will be the sexiest profession.
To get an idea of this business in expansion or even explosion Diego Kuonen’s frequent tweets and presentations are an ideal source.
As new sources of data appear and broader analyzing techniques become necessary, Statistical Agencies are challenged. Attacking the paradigm change is on the agenda.

 Who owns the data

Who owns the data emerging from my devices (smartphone, wearables, home automation …)? What data come from sensors out of my personal reach?
And what are they doing with these data?
Huge data protection issues wait for answers. Reality seems to be faster than rules …
'Estimates vary, but by 2020 there could be over 30 billion devices connected to the Internet. Once dumb, they will have smartened up thanks to sensors and other technologies embedded in them and, thanks to your machines, your life will quite literally have gone online.'