|Quotes| = 56
Isabelle Nuage
(February 24, 2015)
Big Data by itself is of little use.
Eric Jonas Life is too short to not be having fun.
Kira Radinsky Working with data is like an adventure.
William Edwards Deming
In God we trust, all others bring data.
Timothy E. Carone
(January 30, 2015)
Big Data is the oxygen for autonomous systems.
John Foreman I find it tough to find and hire the right people.
Niels Bohr Prediction is very difficult, especially about the future.
Christopher Bishop
Half of what we do at Microsoft Research is Machine Learning.
Daniel Tunkelang Search is the problem at the heart of the information economy.
Isaiah, XXX 8 Now go, write it before them in a table, and note it in a book.
Rishi Shah
(September 24, 2014)
Big data profitability depends on your employee’s data literacy.
All analysis starts with an understandable set of data and algorithms.
Josh Bloom
The first rule of data science is: don’t ask how to define data science.
Caitlin Smallwood You imagine a data set & you salivate at just thinking about that data set.
Jeffrey Fry Having more data does not always give you the power to make better decisions.
Andre Karpistsenko The idea or the initial enthusiasm is just a small part of doing something great.
ATKearney Is Big Data the 21st century equivalent of the Industrial Revolution? We think so.
Foster Provost & Tom Fawcett
Increasingly, business decisions are being made automatically by computer systems.
John Cook
(26 March 2015)
Statistics aims to build accurate models … Machine learning aims to solve problems more directly.
Pierre Simon, Marquis de Laplace The most important questions of life are, for the most part, really only problems of probability.
BI Community What is the most used feature in any business intelligence solution? It is the Export to Excel button.
Milton Friedman The only relevant test of the validity of a hypothesis is comparison of its predictions with experience.
Eran Levy
Mashing up multiple data sources to generate a single source of truth is an integral part of data analysis.
Ivan Vasilev The hidden layer is where the (neural) network stores it’s internal abstract representation of the training data.
P. Dawid
Causal inference is one of the most important, most subtle, and most neglected of all the problems of Statistics.
Thomas Carlyle A judicious man looks on statistics not to get knowledge, but to save himself from having ignorance foisted on him.
n.n. Data does replace heuristics, hard-coded rules, assumptions and beliefs. Machine learning only enables data to do that.
Jake Porway
(October 1, 2015)
Data is not truth, and tech is not an answer in-and-of-itself. Without designing for the humans on the other end, our work is in vain.
Claudia Perlich The conversation is based around how to properly deal with even more sensitive information about where exactly people spend their lives.
Yann LeCun You don’t want to just hire clones of the same person, because then they will all want to explore the same things. You want some diversity.
Andrew Ng Coming up with features is difficult, time-consuming, requires expert knowledge. “Applied machine learning” is basically feature engineering.
John Foreman
If your goal is to positively impact the business, not to build a clustering algorithm that leverages storm and the Twitter API, you’ll be OK.
Michele Nemschoff
(August 30, 2014)
Big data isn’t just for developers and analysts in the technical arena. In today’s digital age, big data has become a powerful tool across industries.
H. James Harrington
If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.
Eric Jonas Graduate students, perhaps because of an adherence to sunk cost fallacy, often write really great surveys of the field at the beginning of their PhD thesis.
Foster Provost, Tom Fawcett
However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz.
William S. Cleveland
Data analysis needs to be part of the blood stream of each department and all should be aware of the workings of subject matter investigations and derive stimulus from them.
Martyn Jones
(March 12, 2015)
Is Big Data really about high volumes, high velocity and high variety, or is it in fact about much noise, too much pomposity and abundant similarity leading to unnecessary high anxiety?
Yann LeCun The idea that somehow you can put a bunch of research scientists together and then put some random manager who’s not a scientist directing them doesn’t work. I’ve never ever seen it work.
Sundar Pichai Machine learning is a core, transformative way by which we’re rethinking everything we’re doing. We’re thoughtfully applying it across all our products, be it search, ads, YouTube or Play.
William S. Cleveland
Model building is complex because it requires combining information from exploring the data and information from sources external to the data such as subject matter theory and other sets of data.
SBS documentary “The Age of Big Data” Data is becoming a powerful and most valuable commodity in 21st century. It is leading to scientific insights and new ways of understanding human behaviour. Data can also make you rich. Very rich.
Lord Kelvin When you can measure what you are speaking
about and express it in numbers, you know
something about it. When you cannot express it in
numbers, your knowledge is of a meagre and
unsatisfactory kind.
European Union’s General Data Protection Regulation (GDPR)
(Dec. 2016)
How could a result be explained, especially a result of a machine learning model, without a versioned record of what data was input to generate the result and what data was output representing the result?
Dr. Olly Downs
(May 18, 2015)
Most of the big data investment focus to date has been on the underlying infrastructure, while development of the applications that make use of that infrastructure – and that deliver actual business value – has lagged.
Jeff Leek
To evaluate a person’s work or their productivity requires three things:
1. To be an expert in what they do
2. To have absolutely no reason to care whether they succeed or not
3. To have time available to evaluate them.
Julia Evans Cleaning up data to the point where you can work with it is a huge amount of work. If you’re trying to reconcile a lot of sources of data that you don’t control like in this flight search example, it can take 80% of your time.
(March 4th, 2015)
Data Science has its own language. So, if you want to have at least a slight chance of surviving in the enterprise world of tomorrow -with its obsessive focus on collecting and analyzing data- you better have started yesterday with learning this terminol.
Rao Naveen
There’s been a lot of talk about trying to make AI work on existing infrastructure. But the sad reality is that you’re always going to end up with something that’s far less than state-of-the-art. And I don’t mean it will be 30 or 40 percent slower. It’s more likely to be a thousand times slower
Jeffrey Heer, Michael Bostock, Vadim Ogievetsky
Graphical Perception Experiments find that spatial position (as in a scatter plot or bar chart) leads to the most accurate decoding of numerical data and is generally preferable to visual variables such as angle, one-dimensional length, two-dimensional area, three-dimensional volume, and color saturation.
Mark van Rijmenam
(October 16, 2014)
Although such Business Intelligence is still quite common and does give you at least some insights, the fast-changing world of today requires a different approach. Organisations today should strive for a holistic overview of their internal and external data that is analysed on the spot and returned graphically via live storylines.
Foster Provost & Tom Fawcett
On a scale less grand, but probably more common, data-analytics projects reach into all business units. Employees throughout these units must interact with the data-science team. If these employees do not have a fundamental grounding in the principles of data-analytic thinking, they will not really understand what is happening in the business.
Some decisions you need to make are big enough to change the course for your business. And your past experiences may not be good predictors of the future. More data are within your reach to understand what was previously unknown. Sophisticated analytical tools are available to you to ‘see’ a wider range of possibilities and evaluate them quickly. Now is a good time for an upgrade in your decision making capabilities.
Avi Kalderon
(JAN 27, 2015)
Without effective data governance and data management, big data can mean big problems for many organizations already struggling with more data than they can handle. That “lake” they are building can very easily become a “cesspool” without appropriate data management practices that are adapted to this new platform. The solution? Firms need to actively adapt their data governance and data management capabilities – from implementing to ongoing maintenance.
Strategy& There is no general rule dictating how organizations should navigate the stages of big data maturity. They must each decide for themselves, based on their own situation – the competitive environment they are operating in, their business model, and their existing internal capabilities. In less-advanced sectors, with executives still grappling with existing data, making intelligent use of what they already possess may have a substantial impact on decision making.
The main priorities for executives are to:
• develop a clear (big) data strategy;
• prove the value of data in pilot schemes;
• identify the owner for “big data” in the organization and formally establish a “Chief Data Scientist” position (where applicable);
• recruit/train talent to ask the right questions and technical personnel to provide the systems and tools to allow data scientists to answer those questions;
• position big data as an integral element of the operating model; and establish a data-driven decision culture and launch a communication campaign around it.
Alice Zheng
There’s structure in it, but it’s kind of a different form. … It’s spit out by machines and programs. There’s structure, but that structure is difficult to understand for humans. … So, you can’t just throw all of it into an algorithm and expect the algorithm to be able to make sense of it. You really have to process the features, do a lot of pre-processing, and first do things like extract out the frequent sequences, maybe, or figure out what’s the right way to represent IP addresses, for instance. Maybe you don’t want to represent latency by the actual latency number, which could have a very skewed distribution, with lots and lots of large numbers. You might want to assign them into bins or something. There are a lot of things that you need to do to get the data into a format that’s friendly to the model, and then you want to choose the right model. Maybe after you choose the model, you realize this model really is suitable for numeric data and not categorical data. Then you need to go back to the feature engineering part and figure out the best way to represent the data. … I hesitate to say anything critical because half of my friends are in machine learning, which is all about algorithms. I think we already have enough algorithms. It’s not that we don’t need more and better algorithms. I think a much, much bigger challenge is data itself, features, and feature engineering.

5 thoughts on “Quotes”

  1. Dear Michael !
    I liked your Quotes really. You can see my work at Also you can 2 video here. It’s original for kdnuggets post 😉
    my best regards


    • Hello Andy, thank you very much for your hint. I had a look at your list and found 40 which were not in my list right now. My list now contains >700 from which I publish one a day. So at least another 2 Years …. There are some typos in your list, e.g “better plac”. You might have a look. Thank you very much, Michael


      • Hello Michael !
        Thanks a lot for your attention to my humble work. I have fixed typo “better place” and hope for best. How did you find videos for #1, #2 interviews quotes ?
        I hope you enjoy it too :)) I saw your web site and found it very useful for me.
        So thanks again for your attention.


  2. Very nice post. I simply stumbled upon your weblog and wanted to say that
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  3. hatemgkotb said:

    This is simply AMAZING!


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