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This is the elephant in the Big Data room….How much “Bad Data” is out there?Wait a minute, I hear you say, do you mean to say that not all of this data is accurate? What is the point of salivating over terabytes of Big Data if we don’t understand which bits are inaccurate, duplicated, incorrect or incomplete?That is the right question to be asking.The consequences of cyber attacks are well documented, but actually, in a world that is increasingly reliant on our data, the costs of Bad Data could be even higher. The U.S. economy is estimated to lose $3 trillion pe
Recent rail accidents in Philadelphia and all over the world lead many a commenter to wonder why there wasn't a more sophisticated monitoring system aboard these trains. Well, the industry saw that problem and is beginning to release some solutions.
In the last six months of so there has been much commentary about Tesco’s difficulties, including a number of posts by me. One of the questions I have been asked many times since is “Why didn’t ClubCard (Tesco’s loyalty card scheme) data warn them? Why didn’t big data help? Surely a retailer as sophisticated as Tesco would be able to read its big data to notice that there are some worrying trends that need addressing? The fact that apparently vast quantities of big data were not able to keep Tesco from a truly alarming fall from grace, contains lessons for us all
We all have watched movies such as Terminator, Minority report, iRobot.., at some point in time and wondered if life would get that futuristic. Living alongside robots to do our choirs or sensing a fluctuation in your heartbeat to send out a signal for an ambulance? Can the right hardware and software work together to save a dying person? Deep inside, all of us imagine a life with sophistication, comforts and luxury at all stages of life. In this era of continuous technological evolution and advancements, we see a total restructuring of how we live our daily lives.A decade back we would have
"The current economic climate is putting companies under more stress than they have experienced in quite some time. The economic downturn is forcing executives to sharpen their pencils, look for profit and eradicate losses in every nook and cranny of the organisation, and make hard decisions about where to allocate scarce resources. Geopolitical uncertainty makes planning and modelling at once harder and more important than ever before. And new reporting rules have companies scrambling for faster, yet more detailed, information about results and performance so that they can report their
Enjoy 6 video courses. Data project are among the most exciting ones. This session covers all the key elements to engage with confidence in your first steps with data. Get on the way to become a data “CEO”.
Alas, poor Yorick! I knew him, Horatio; a fellow of infinite jest, of most excellent fancy; he hath borne me on his back a thousand times; and now, how abhorred in my imagination it is!From the play Hamlet by William Shakespeare Big Data is dead! Long live Information Management.Of course I am overstating. It isn’t tangibly dead, because outside of PR and Marketing, and the gullible imaginations of a few punters with more dollars than sense, it was never actually alive.I don’t blame the industry for ‘bigging up’ Big Data, it was a ‘common sense’ continuatio
Google has added another set of nuts and bolts to the mechanics of the Internet of Things (IoT) with the launch of its Eddystone technology. Named after the Eddystone Lighthouse off of the English coast in the county of Cornwall, Google’s Eddystone is an open source cross-platform Bluetooth LE (BLE) [...]
On a hot sweltering day in Lincoln, Nebraska in July of 1990 I delivered my first predictive model.Working on a contract basis for Metromail (acquired by Experian), I analyzed the performance of about half dozen recent mailings for a non-profit to predict the characteristics of best donors.I used demographics like age, income, home value, history of donations and created a linear regression model. The top decile (we group data into equal ten percent, each is called a decile) was yielding a theoretical 7% response rate, and the bottom about 0.5%. Everything looked good, and I presented the res
Gary Bamford's insight:
Ah the good old days - tapes - yes I remember them along with the JCL to load them!
IntroIn order to bring some semblance of simplicity, coherence and integrity to the Big Data debate I am sharing an evolving model for pervasive information architecture and management.This is an overview of the realignment and placement of Big Data into a more generalized architectural framework, an architecture that integrates data warehousing (DW 2.0), business intelligence and statistical analysis.The model is currently referred to as the DW 3.0 Information Supply Framework, or DW 3.0 for short.A recapIn a previous piece with the name of Data Made Simple - Even 'Big Data'(available here o
Here is my top 7 list of daft things that some people say about Big Data.I think that Big Data does play a role in some businesses. I also think that some of the basic distributed file store and text search technologies can be usefully employed, in non-traditional indexing, counting and correlation. However, there is an awful lot of nonsense said about Big Data.So, onwards and upwards.Big Data is like currencyIf Big Data is currency, and for most of us, it isn't, then it's more like the hyperinflationary money of the Weimar Republic, rather than something you would take to the bank or try and
We have been told that our data is a gold mine, and many have invested accordingly. – And still, many executives feel empty-handed (and empty-pocketed as well). But why?Well, it shouldn’t come as a surprise as man throughout history has coveted the ability to turn things into gold – the Midas touch. Medieval alchemists were convinced they could turn lead into gold. Even in modern times there are examples of serious believers (such as the German leadership during WWII which seriously tried to turn sand into gold).Today’s business alchemy challenge is the ability to turn
In this post I outline the five essential skills any data scientist needs. The post was first published in my column for Data Science Central. In one of my previous posts, I explained the difference between what I consider the two core types of data scientist – strategic and operational.Broadly speaking, they require many of the same skillsets – but the distribution of your expertise and experience within these skillsets will vary, depending on whether your role is more strategic, or operational
An outlook report from Institute for the Future that describes the current state of global business, identifies important directional shifts and anticipates the impact these will have on our world by 2025.
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