Tag: Big Data Analytics

Klaus Fabits 0
Big Data? Ich kann es schon nicht mehr hören!

 „Wieder ein Berater, wieder ein Systemintegrator, wieder ein Outsourcer und wieder ein Software- oder Hardware-Hersteller, der mir erzählt, dass Big Data der Trend der Zukunft ist. Jeder erzählt mir, wie wichtig „Big Data“ für mein Unternehmen sind. Und wenn ich hier nicht zustimme, bin ich gleich ein Innovationsverweigerer. Die Nachfrage

Andreas Gödde 0
Konrad Adenauer gegen Big Data?

Laut aktuellem Gartner Hype Cycle ist der Hype um Big Data verglüht und die Phase der Ernüchterung ist eingetreten. Da höre ich schon viele in den Unternehmen sagen: "Hab ich ja gleich gesagt", "Das war total überbewertet", "Wir haben bei uns im Unternehmen keine Big Data", "Wir dürfen aufgrund des Datenschutzes

Dirk Mahnkopf 0
Nationaler IT-Gipfel: Big Politics, Big Decisions, Big Data – unser Standpunkt

Schritt für Schritt: Big Data wandert langsam in den Zeitungen nach vorne. Gestartet ist das Thema im Wirtschaftsteil und kommt jetzt über den naturgemäß technikkritischen Feuilleton im Politikteil an. Investitionsmittel stehen bereit Die Europäische Union will den Aufbau der datengesteuerten Wirtschaft vorantreiben: 2,5 Mrd. Euro stehen bereit für die Investition

Jim Harris 0
As the butter churns in Bangladesh

“Correlation does not imply causation” is a saying commonly heard in science and statistics emphasizing that a correlation between two variables does not necessarily imply that one variable causes the other. One example of this is the relationship between rain and umbrellas. People buy more umbrellas when it rains. This

Jim Harris 0
Errors, lies, and big data

My previous post pondered the term disestimation, coined by Charles Seife in his book Proofiness: How You’re Being Fooled by the Numbers to warn us about understating or ignoring the uncertainties surrounding a number, mistaking it for a fact instead of the error-prone estimate that it really is. Sometimes this fact appears to

Jim Harris 0
The Chicken Man versus the Data Scientist

In my previous post Sisyphus didn’t need a fitness tracker, I recommended that you only collect, measure and analyze big data if it helps you make a better decision or change your actions. Unfortunately, it’s difficult to know ahead of time which data will meet that criteria. We often, therefore, collect, measure and analyze

Jim Harris 0
Sisyphus didn’t need a fitness tracker

In his pithy style, Seth Godin’s recent blog post Analytics without action said more in 32 words than most posts say in 320 words or most white papers say in 3200 words. (For those counting along, my opening sentence alone used 32 words). Godin’s blog post, in its entirety, stated: “Don’t measure

Jim Harris 0
Bring the noise, boost the signal

Many people, myself included, occasionally complain about how noisy big data has made our world. While it is true that big data does broadcast more signal, not just more noise, we are not always able to tell the difference. Sometimes what sounds like meaningless background static is actually a big insight. Other times

Jim Harris 0
Being data-driven means being question-driven

At the Journalism Interactive 2014 conference, Derek Willis spoke about interviewing data, his advice for becoming a data-driven journalist. “The bulk of the skills involved in interviewing people and interviewing data are actually pretty similar,” Willis explained. “We want to get to know it a little bit. We want to figure

Gastbeitrag 0
Gastbeitrag: "Being Data Driven with Hadoop"

We asked Lars George, EMEA Chief Architect at Cloudera, to share his opinion about Hadoop, Big Data and future market trends in Business Analytics. For all those who want to know more about Hadoop we recomment this TDWI whitepaper and how to apply Big Data Analytics.  The last few years

Guido Oswald 0
Data Day Zürich - wir laden herzlich ein!

Da war doch mal was, Sie erinnern sich, Hoodiejournalismus?! In dieser Diskussion über Digital gegen Print, jung gegen alteingessen, #hoodie vs. #schlipsy, über was ist Premium oder was ist hautnah dabei, über was erscheint modern, zeitgemäß und innovativ oder was bezahlt die Miete am Ende des Monats, ist ein Punkt

Jim Harris 0
A double take on sampling

My previous post made the point that it’s not a matter of whether it is good for you to use samples, but how good the sample you are using is. The comments on that post raised two different, and valid, perspectives about sampling. These viewpoints reflected two different use cases for data,

Jim Harris 0
Survey says sampling still sensible

In my previous post, I discussed sampling error (i.e., when a randomly chosen sample doesn’t reflect the underlying population, aka margin of error) and sampling bias (i.e., when the sample isn’t randomly chosen at all), both of which big data advocates often claim can, and should, be overcome by using all the data. In this

Jim Harris 0
What we find in found data

In his recent Financial Times article, Tim Harford explained the big data that interests many companies is what we might call found data – the digital exhaust from our web searches, our status updates on social networks, our credit card purchases and our mobile devices pinging the nearest cellular or WiFi network.

Thomas Keil 0
Interview zur re:publica mit Professor Mayer-Schönberger: „Es muss sichergestellt werden, dass ich mich gegen die Maschine entscheiden kann!“

Viktor Mayer-Schönberger ist Oxford-Professor, Berater internationaler Organisationen sowie einer der Keynote-Speaker der re:publica 2014. In seinem Bestseller „Big Data: Eine Revolution, die unser Leben verändern wird" wirft er ein kritisches Licht auf Big Data Analytics – zeichnet aber auch positive Szenarien auf. Wir haben ihn gefragt zu seinen Forderungen an

Jim Harris 0
The dark side of the mood

As an unabashed lover of data, I am thrilled to be living and working in our increasingly data-constructed world. One new type of data analysis eliciting strong emotional reactions these days is the sentiment analysis of the directly digitized feedback from customers provided via their online reviews, emails, voicemails, text messages and social networking

Jim Harris 0
Lean against bias for accurate analytics

We sometimes describe the potential of big data analytics as letting the data tell its story, casting the data scientist as storyteller. While the journalist has long been a newscaster, in recent years the term data-driven journalism has been adopted to describe the process of using big data analytics to

Jim Harris 0
Big data hubris

While big data is rife with potential, as Larry Greenemeier explained in his recent Scientific American blog post Why Big Data Isn’t Necessarily Better Data, context is often lacking when data is pulled from disparate sources, leading to questionable conclusions. His blog post examined the difficulties that Google Flu Trends

Jim Harris 0
What magic teaches us about data science

Teller, the normally silent half of the magician duo Penn & Teller, revealed some of magic’s secrets in a Smithsonian Magazine article about how magicians manipulate the human mind. Given the big data-fueled potential of data science to manipulate our decision-making, we should listen to what Teller has to tell

Jim Harris 0
What Mozart for Babies teaches us about data science

Were you a mother who listened to classical music during your pregnancy, or a parent who played classical music in your newborn baby’s nursery because you heard it stimulates creativity and improves intelligence? If so, do you know where this “classical music makes you smarter” idea came from? In 1993, a

Thomas Keil 0
Big Data Analytics auf dem Vormarsch

"30 Prozent der Unternehmen haben bereits Big-Data-Initiativen" - so eines der Ergebnisse der gerade vorgestellten Studie "Big Data Analytics" des Analystenhauses BARC. Die Experten von BARC haben Unternehmen in der gesamten DACH-Region befragt und zeichnen ein aktuelles Bild des Status quo: Fast ein Drittel aller Unternehmen gibt wenig auf Pauschalurteile

Gerhard Svolba 0
„Der eifrige Statistiker“ – oder: Warum das Verlangen nach Daten manchmal so besonders groß ist.

Kurz vor Ende des „Jahres der Statistik“ soll dieser Blog-Beitrag eine Lanze für die vielen Statistiker und Statistikerinnen brechen, die tagein tagaus, die ihnen gestellten Analyseaufgaben sorgfältig erfüllen. Und sich dabei häufig den Ruf einhandeln "detailverliebt“ zu sein, wenn es um die nötige Datenbasis geht. Wie kommen wir zu unserem