James M. Connolly, US Correspondent, 5/9/2013 Comment now
Too often people who make a living with technology, whether they build it, manage it, or write about it, lose sight of what should be obvious about the constituency they serve. The people who use tech have limited knowledge or interest in that tech.
Chris Taylor, Director of Marketing, TIBCO Software, 5/2/2013 Comment now
Big data is a polarizing topic whenever it comes up in discussions -- which is often. Like any hot trend, the combination of excitement, ignorance, and opportunism creates noise that leads to a healthy amount of skepticism, cynicism, and a few other isms as well.
Seth Grimes, Analytics Industry Observer, 4/26/2013 Comment now
Old-school BI and analytics are about crunching numbers and only numbers. The old school produces indicator values, which are abstracted away from context but nonetheless used to justify context-sensitive decisions.
Sam Zindel, Data Strategist at iCrossing Digital Marketing, filled us in on how supermarket delivery giant Ocado uses big data to identify and serve specific content to vegetarians once they spot them on their website.
This was part of Sam's talk at The Big Data Show about putting the customer at the heart of digital marketing, as well as making the most of the data you already have.
On the opening day of the Big Data Show, Mike Cornwell, CEO of The IDM, was generous enough to give us some time to discuss his afternoon panel session. He also offered a word of caution on the state of marketing data. We're all getting excited about big data, but it seems most people still can't deal with their small data in the best possible manner.
"Just knowing enough to find some insight from information and using it intelligently for marketing still seems to be beyond a lot of organizations," he said.
Does this resonate with your business? Have you got the small data figured out before you invest time in de-siloing and bringing more information together?
Continuing our series of interviews with businesses leveraging big data, we talk to James Gill, CEO of GoSquared.
GoSquared offers real-time web analytics, using big data technologies to surface the analytical data that counts. Marketing managers and IT departments benefit from GoSquared's ability to pick out the most actionable insights as they happen.
In the first of a series of interviews with business leaders who leverage big data, we talk to James Robinson, CTO and co-founder of OpenSignal.
OpenSignal combines big data technologies and sensor data from mobile phones to give insight to both mobile consumers and telecommunications giants. Robinson is also a contributing writer on Big Data Republic.
Hadoop is the open-source software framework that quickly became almost synonymous with big data. But what does it actually do?
Whereas traditional data queries were run on one server, Hadoop enables you to run data queries across a large number of machines. By spreading the computational load across many servers, Hadoop enables you to deal with big data in a timely fashion.
Tobias runs an online DVD store -- and he wants to increase sales by recommending products to customers as they check out. But he doesn't just want to recommend bestsellers, he wants a smart system that recommends based on the buyer's demographics and taste.
That's where Hadoop helps out. For each customer, Hadoop enables Tobias to spot patterns across all of his customers' data, based on age, sex, genre preference, actor preference, period of production, and many other defining elements. He can access this information quickly, because different elements of the search can be carried out individually and simultaneously, instead of having to take place on a single machine.
Today we're going to take a look at the V that allows big data to be immediate and reactive: Velocity.
As well as having to master the sheer volume and variety of information within big data, organizations also have to be able to contend with the speed at which all of this data is generated. Real benefit can be gained by pouncing on this data in real-time -- affecting outcomes while they are still forming.
What kind of benefit?
Well, as we've already established, data can take many different forms. How working on this stream of real-time big data will benefit you will depend on your industry. For this example I'll focus on the financial services sector.
Andy is in charge of online security for a big bank, trying to make sure his customers' money is safe. When he can detect fraud after the event, it's fairly useless, but if he can spot it as it happens, it can be priceless. If a malicious computerized attack is started on Andy's bank, it will be generating thousands of events every second -- but Andy has put the right system in place to detect these events by comparing them to the way actual, normal customers behave. And it happens in real time, so alarms are going off to let him know.
Many fraudsters will access online banking and go directly to the transfer section of a Website without first checking balances and transactions. That clickstream is foreign and unfamiliar to the complex event processing engine and thus gets flagged.
In this way the bank can stamp down on the illegal activity as it happens, rather than chasing up after the event.
There's plenty of talk about big data's three V's: volume, velocity, and variety. But what exactly do these terms mean?
We're going to take a quick trip through one of these today: Variety.
This exciting concept within big data gives you the opportunity to gain insight by combining a variety of data sets that would not traditionally sit together. By enabling you to link up your traditional analytical data sets with many different types of information, a new world of analytical possibilities is opened.
So what's so exciting about this?
Well, it allows you to collate data sets that don't obviously relate to each other. Data experts can then analyse this collated data, to spot patterns or create new insights you would previously have been blind to. Variety, when tackled well in big data, allows you to see new revelations in the data your organization already produces.
An example: Judith is a brand manager, she loves her job and is very good at it, but knows she would benefit from being able to listen even more closely to the voice of her customer.
Taking traditional financial information, Judith can already see the performance of her brand. It doesn't take a data scientist to see which week did well, and which week did badly. But it won't tell her why.
Harnessing variety in data, Judith's data team can create relations between this data and what's being said on social media about her brand, as well as in text-input fields on customer satisfaction surveys. These disparate sets of data can be brought together, contextualized, and visualized in a way that gives Judith clues as to what her brand has done to influence customer behavior.
Suddenly, Judith now has the vision to generate hypotheses on ways to amplify positive results and mitigate negative trends.