“Know your data, where it comes from, what’s in it, what it means. It all starts from there.” Randy Au ( 15.02.2019 19:27 )

“It takes a lot of will power, in our data obsessed world to say ‘too much!’ However, there are many ways where too much information is destroying productivity, and actually causing bad decision making, not good. But it is hard to avoid the world of opportunities that has been opened in data collection and analysis. So how do you balance the two? The first step is to understand there is a big difference between data collection, and it’s utilization. While it seems subtle, the difference is key, and utilization is where many make mistakes.” Chris Riley

“In Web 1.0, businesses busied themselves with getting the basic internet technologies in place so that they could establish a web presence, build electronic commerce capability, and improve operating efficiency. We can think of ourselves as being in the era of Big Data 1.0, with firms engaged in building capabilities to process large data. These primarily support their current operations – for example, to make themselves more efficient. With Web 1.0, once firms had incorporated basic technologies thoroughly (and in the process had driven down prices) they started to look further. They began to ask what the web could do for them, and how it could improve upon what they’d always done. This ushered in the era of Web 2.0, in which new systems and companies started to exploit the interactive nature of the web. The changes brought on by this shift in thinking are extensive and pervasive; the most obvious are the incorporation of social-networking components and the rise of the ‘‘voice’’ of the individual consumer (and citizen). Similarly, we should expect a Big Data 2.0 phase to follow Big Data 1.0. Once firms have become capable of processing massive data in a flexible fashion, they should begin asking: What can I now do that I couldn’t do before, or do better than I could do before? This is likely to usher in the golden era of data science. The principles and techniques of data science will be applied far more broadly and far more deeply than they are today.” Foster Provost & Tom Fawcett ( 2014 )

“One of the big challenges of being a data scientist that people might not usually think about – is that the results or the insights you come up with have to make sense and be convincing. The more intelligible you can make them, the more likely it is that your recommendations will be put into effect.” Victor Hu

“Given enough time in computing, you can do just about anything, but only recently have people been able to apply these machine learning models in real time to critical business processes.” Gary Orenstein ( 2017 )

“Big Data is just a tool.” Kamil Bartocha ( 26. Apr 2015 )

“Solving data quality problems requires investment, and it’s an investment no business can avoid. Poor data quality can lead to compliance issues, legal challenges and increased effort from all sides. Over time, as data (or the information derived from it) decays, inefficiency and inaccuracy becomes a serious issue that can hamper progress throughout the organisation. Customers also have higher expectations and feel more empowered to complain if they don’t feel their data is being handled correctly.” Martin Doyle ( October 23, 2014 )

“Over the last decade, the primary consumer of log data shifted from humans to machines.” Kiyoto Tamura ( April 21, 2015 )

“I wish I could use data analytics more, but I don’t have Big Data.” n.n. ( 2015 )

“Data are the heat engine for invention in data science.” William S. Cleveland ( {2000-2014} )