A sign that Big Data is Big can be seen in the fact that the term Big Data has found its way all the way to the average Danish newspaper reader. Politiken, the biggest newspaper in Denmark, had it as a theme and wrote 6 pages about it. [among others this one 1] not long ago.
The newspaper article refers to hungry.dk – a Danish company, to illustrate the use of Big Data in a business.
hungry.dk provides a portal for take-away restaurants and is highlighted because they are using data from one of the new data interfaces delivered by the Danish state as part of the digitaliser.dk initiative. They retrieve smiley data for restaurants and merge it with their data on restaurants, enabling them to remove places that do not comply with the Danish food regulations. This, the article says, is one example which describes how companies can use Big Data, which is something that we are lacking behind in Denmark, and thereby loosing workplaces and competition edge, by not taking advantage of it [ 3 ].
I think its great that hungry.dk merges its list of takeout places with the smiley database, and its a great example of how businesses should take advantage of the data provided by the state. But for me Big Data is about analysing large dataset, to find patterns or to use it to predict the future, not so much about merging data from different sources. Merging data from different sources could very well be a step in a Big Data problem, but is not a Big Data problem in it self.
Either way, Big Data is something that is on a lot of peoples minds, and a tool that most companies should be consider using. The Data that the Danish state provides are there to be used, and even if you could ask why the state should pay for supporting high usages of the databases to enable businesses to bloom.
But where to start? Most books and websites makes it sounds like its about asking for the key at the Big Data engineer, and then the river of knowledge will magically start flowing out of your databases, and make exactly your company special. But is it that easy?
First point of order is the consider what data you got, or how to get it. Do you save backups? Ensure that all customer information is not a critical item, or actually save it in a way that makes it retrievable?. The collection of data can also be from an public API, or get data from Twitter to find trends and moods of things, or make a smart Facebook app so people completely voluntarily tells you all their secrets about everything from their dishwasher breaking to where they want to go for holiday next year ( imagine if a dishwasher seller could get info like that, or easyjet knew where people dreamt about going)(by the way are you sure that your facebook birthday app doesnt already collect data like that?)
When you got the data, and got enough to have a statistical relevancy (se 5) you can introduce the data to a statistician and her machine to enable them to learn something from the data. Actually you should probably include her when collecting the data too.
With success you can optimise your sale, your campaigns or who you build bridges better. There is virtually no limits to what you can make a machine help you with, if only you know how to teach it. An example where many businesses claims to gain on using machine learning is with recommendation systems, were it is said to add up to 10% more sale. [The classic examples netflix.com and Amazon.co.uk].
It sounds easy, but like most investigations, a single hitch can end in false conclusions, which will be hard to spot and resolve.
“The way to turn data into insight is to squash the notion that big data is a silver bullet. We preach that data and analytics is important but then we empower people to be curious and ask questions and get involved in big data analytics.” [ 6 ]