In her latest .rising leaders post, Jodie Hopperton attempts to demystify the industry's infamous buzzword, drawing on personal experience and expert opinion to bring you (almost) everything you need to know about big data.
If you haven’t heard of big data, then you’ve been under a big rock. If you have, then you’re probably sick of it. Before you eject any more big data-infused bile, make sure you understand it.
In her latest .rising leaders post, Jodie Hopperton attempts to demystify the industry buzzword, drawing on personal experience and expert opinion, to bring you (almost) everything you need to know about big data for media.
Big data is a phrase that has been bandied about for the last few years and it’s getting more and more prominence in media and marketing. New roles, or titles at least, are springing up. Companies will now have Data Scientists or Data Analysts, and marketing campaigns are increasingly built and adapted upon insights drawn from data.
So what exactly is big data? Typically it’s the 3 Vs, data that is high in Volume, Velocity and Validity.
It’s a vast field, and one that is relatively new so there are few real experts out there – a few of them got together for the Big Data for Media Conference at Google London and here are some of the things they said:
Start collecting data – analytics tools can tell you a lot quickly, check out your Google analytics as a first step
– it doesn’t have to be huge to be big data. dunnhumby may tell you that if it fits onto a spreadsheet, it’s not big data. But I’ve learnt that we all need to start somewhere.
– don’t forget that what consumers DON’T do, can be as telling as what they do do. For example on a website look at what is not being clicked as well as the most popular content.
– people trust hard facts so use data to back up a point of view
– tie up with third parties to add context to your own data. Partners could include social media platforms such as Facebook, retailers such as Tesco, or finance companies such as AMEX. Rather than asking a consumer directly for additional information about themselves; you may be better off acquiring the information elsewhere.
– try to identify key signals, which often lead to another action. You may be able to use this to increase sales or reduce churn.
– Editorially to visualise a story, or even BE the story;
– Brief colleagues internally to build trust, and perhaps to get them on board with a project;
– or to drive decisions both editorially and commercially
We can also learn a lot from these experts on what not to do. It’s always useful benefiting from other people’s mistakes! Here are a few of them:
1. Be careful with personalisation. When communicating it’s often better to address people as individual, however when devising editorial strategies, we can’t all be Mashable with their data driven home page so think carefully about your core product and be true to it.
2. Data is personal so use it carefully. Most companies I spoke to anonymise data before their teams analyse it. Of course if you are using it for comms, the whole point is that you can see individual information. Data Privacy laws are in place for protection and brands should have some basic rules in place so that users feel comfortable with them owning it. Would you be happy if your company was using your own personal data? That’s a good sanity check.
3. For any change you make based on data, test, test and test some more before you make a full scale roll out. Simple A/B testing can give you useful insights.
4. Never lose sight of what makes your product great. That can take time and resources.
5. Customer is king – always keep them at the center of your strategies, taking precedence over channel led strategies
6. Be open! This field is changing so quickly and possibilities of using big data are seemingly endless, be open to new developments and alternative ways of thinking.
If you own data, lucky you! It’s an asset. A big one. It’s also incredibly personal so use it wisely.
Nice. Forget about so called ‘Big Data’? It’s just data.
I discovered and patented how to structure any data: Language has its own Internal parsing, indexing and statistics. For instance, there are two sentences:
a) ‘Fire!’
b) ‘Dismay and anguish were depicted on every countenance; the males turned pale, and the females fainted; Mr. Snodgrass and Mr. Winkle grasped each other by the hand, and gazed at the spot where their leader had gone down, with frenzied eagerness; while Mr. Tupman, by way of rendering the promptest assistance, and at the same time conveying to any persons who might be within hearing, the clearest possible notion of the catastrophe, ran off across the country at his utmost speed, screaming ‘Fire!’ with all his might.’
Evidently, that the phrase ‘Fire!’ has different importance into both sentences, in regard to extra information in both. This distinction is reflected as the phrase weights: the first has 1, the second – 0.02; the greater weight signifies stronger emotional ‘acuteness’.
First you need to parse obtaining phrases from clauses, for sentences and paragraphs. Next, you calculate Internal statistics, weights; where the weight refers to the frequency that a phrase occurs in relation to other phrases.
After that data is indexed by common dictionary, like Merriam, and annotated by subtexts.
This is a small sample of the structured data:
this – signify – : 333333
both – are – once : 333333
confusion – signify – : 333321
speaking – done – once : 333112
speaking – was – both : 333109
place – is – in : 250000
To see the validity of the technology – pick up any sentence and try yourself. After that try a paragraph?