Big Data is a phrase a lot of us probably grew up with.
Twenty years ago it was the height of tech, with only the biggest of companies being able to benefit from it but in the world of the modern data stack (and with a little bit of help from DoubleCloud) even start-ups can empower their growth with Big Data.
What is Big Data?
Big Data can be defined as complex data sets that are so large, traditional data processing software can’t handle them.
When used correctly though, those huge volumes of data can be invaluable to businesses, both established and just starting-up, in tackling issues they may not even have been aware of before.
Just imagine an ecommerce website — they need to be able to recommend the right content, products and offers to potential customers based on what they’re looking at on social media, what they’ve viewed on the web, etc… but capturing, categorizing and using that data used to be a mammoth task.
That’s where Big Data steps in…
What can Big Data do?
As part of an efficient Modern Data Stack, designed to process large scale data, there’s not much an organization can’t do with Big Data.
The vital point is of course to create the right data architecture first to support that much raw information, but once created (and it’s a lot easier than you might think with the right help) there’s not a lot Big Data can’t do.
Predictive modeling with Big Data
Predictive Modeling is a great use of Big Data but doesn’t just require a lot of information, it requires a lot of different kinds of information for it to be successful.
In short, predictive modeling is a way of using all that data you’ve stored to run virtual simulations to predict possible outcomes.
When data scientists utilize these models, they can start to manipulate starting variables to better predict and create possible (optimal) solutions.
For instance, doing X followed by Y will likely result in XY, but by changing up the starting variables, you can see that by doing X, Y and XY at the same time, you might achieve XX (if that doesn’t make sense it’s because we’re being vague. What you do and what you change will vary wildly based on your business needs).
Utilizing predictive modeling (sometimes also known as predictive analytics) moves an organization away from reactive processes into a much more proactive stance.
The more comprehensive and inclusive your data modeling can be, the more useful your final model will end up.
That’s why it’s so vital to have the right architecture set up, so you can handle that much data quickly, cheaply and efficiently.
Empowering cyber security with Big Data
Big Data and cyber security may not seem like two items you’d automatically associate but with that much information being collected you need to ensure it’s been stored safely.
“Ah” you say “but you said I could empower my cyber security with Big Data?”
Well actually… that too. You see, the very information that your Big Data is collecting can also be used to bolster your cyber security against possible or future attacks.
If you’re a CISO, you’re likely aware that one of the biggest problems to your security you might face is that you’re never quite sure when a malware or ransomware attack might come.
Has your hardware been compromised? Are you suffering a DOS attack? Are your staff being targeted by phishing or social engineering scams?
How would you even begin to predict or identify those kinds of trends?
With Big Data…
Big Data might be handy to test an organization’s preparedness, but where it really comes into its own is when combined with a bit of ML/AI to do the same function.
The quicker an attack can be identified, the quicker the correct steps can be taken to neutralize it.
Big Data is invaluable in automating those steps, identifying behaviors against historic records and real time data to determine what’s ‘normal’, what isn’t and what needs to be done about it.
Real-time A/B tests
One of the things everyone always seems to forget about Big Data is that, with the right architecture, it’s easier to achieve sub-second speeds with it.
Once that’s done, it becomes very useful for live A/B tests; something that’s always in need for start-ups, testing scenarios and hypotheses, changing messages, adapting to market conditions etc.
With the right amount of data, architecture to sub-second speeds, organizations can easily compare what they think they know about their customers to what’s actually happening in real-time; optimizing landing pages, capture forms and messages for the best results.
Take Amazon’s homepage for example.
At any one time they can have over 200 different versions live, optimized for clicks and sales based on exhaustive A/B tests and personalized preferences.
Big Data led segmentation
Segmenting large swathes of your customer data isn’t a new concept but what we’re talking about here is using Big Data to help segment your Big Data.
Segmentation has to be one of the best known uses of Big Data, using it to create optimized customer journeys to create a better digital experience for your end users (and ultimately increase your ROI). However… it can also be one of the hardest to get right, especially if you’re hoping to stay on the right side of privacy legislation (such as GDPR and CCPA).
Reducing the number of ‘mass emails’ you send, opting instead for a more personalized campaign involves organizing your customers or clients by interest, in formats and frequency that will most likely increase engagement with your communications.
It can also be used in new customer acquisition by identifying your best performing segments and cohorts and then adapting your ongoing marketing activity to target similar demographics.
Keeping an eye on the competition
Keeping an eye on your competitors website and social media is a great way of benchmarking your efforts against theirs.
Somehow breaking down all that data, including 50,000+ followers and tweets etc isn’t so great. But, fortunately, Big Data, capturing and storing that information correctly, can once again help.
At sub-second speeds you can analyze all that data, pulling out positive and negative sentiment reports to pass back to your product and marketing teams, helping to shape the growth of your organization with genuine BI.
Find out what you don’t know
The biggest win with Big Data however is identifying those trends and relationships you had no idea existed.
We’re all guilty of making decisions based on our own opinions and biases, but Big Data removes that, relying solely on the data in front of it.
The trick with Big Data isn’t capturing it anymore. Anyone can do that.
The trick is storing it in such a way that you can derive actionable business intelligence from it without costing a small fortune (or a large one).