I like to think and write about foundational mega-trends such as The Cloud Changes Everything and share ideas that change how WTE helps customers create responsive websites, digital marketing, custom software, and increasingly efficient information architecture to feed all of the above. That’s why when my LinkedIn advice to c-level techies and marketers to embrace big data and hedge their Google Analytics with other tools made my phone blow up, I got writing.
I love big data. So much so that I took classes from UC San Diego Supercomputer Center (SDSC) to get officially Certified in big data. Taking college classes at 48 sounds strange to some, but not for someone who loves technology. Today I’ll be sharing insights that may make the term “big data” less intimidating, along with ways we’ve learned to use the insights data can bring for your business:
Oracle defines “big data” as “data that contains greater variety, arriving in increasing volumes and with more velocity.” So, variety, volume, and velocity are big data’s three Vs, and we add “veracity” because garbage in equals garbage out. BigData is a bit subjective. Many people focus on PB (petabytes), or Rows, or Nodes. BigData is the amount of data you need to store to get actionable results, now or in the future... that’s my definition. WTE works with companies from food trucks you love to Fortune 100 brands you’d recognize. Every customer has big data’s four Vs these days because data is a monsoon with a single direction: more.
Think your website and online marketing are too small to worry about big data? It’s not. We created a back-of-the-envelope spreadsheet (in resources) for a small fictional website with 6,000 customers and 300 visitors, generating 1,400 page views daily. This modest fictional 200-page site would generate close to 700,000 data points yearly.
Extend our model five years, and our fictional site would generate close to five million data points because data growth isn’t one-to-one. Growing our fictional website’s customers by 10% yearly would increase data by thirty to fifty percent. Compound that level of data growth over five years, and our fictional web marketing team would look back on less than a million data points as the good old days. Everyone’s data is too big to manage effectively without understanding big data terms and using automated tools.
As you can probably tell, big data is seductive, fun, and challenging. Solving big data's Rubik's cube is thrilling and difficult and can become an end unto itself. Unfortunately, it's easy to get lost in the data weeds, and remember you're trying to use complex logic, programming, and pattern recognition to understand and predict human passion, love, and behavior. Resisting big data's seduction to remember data = people may be the most difficult challenge for any development or marketing team. Here's how Forbes’ Aarron Spinley describes the Problem with Big Data (linked in Resources):
Today, many businesses talk about data rather than about customers. They announce data-first strategies, not customer-first ones. But I sense a comeback. It is true that new advanced analytics, artificial intelligence, and machine learning capabilities, when built for the behavioral paradigm, can underpin customer orientation. I work around it every day. But please understand that this is about the correct data, not big data. In a highly fragmented and hyper-connected culture, the best advertising is not in the data; it's in the creative. It's not found in the dictated buying models or segments of data platforms. Instead, it's lurking within the behavioral, the psychological, and the individual. Our marketing mission is to drag our profession out of the industrial era. The Problem with Big Data - Forbes
Today, many businesses talk about data rather than about customers. They announce data-first strategies, not customer-first ones. But I sense a comeback.
It is true that new advanced analytics, artificial intelligence, and machine learning capabilities, when built for the behavioral paradigm, can underpin customer orientation. I work around it every day. But please understand that this is about the correct data, not big data.
In a highly fragmented and hyper-connected culture, the best advertising is not in the data; it's in the creative. It's not found in the dictated buying models or segments of data platforms. Instead, it's lurking within the behavioral, the psychological, and the individual. Our marketing mission is to drag our profession out of the industrial era.
Aaron's focus on customers and using big data ai and machine learning to "underpin customer orientation" creates the proper framework for any technology, tool, and innovation. But, unfortunately, Aaron's MarTech criticism points out the same dangerous seduction.
MarTech is short for a range of software and tools known as a "marketing technology stack" designed to achieve marketing goals. MarTech's primary seduction is a not-so-hidden belief in a 360-customer view. Everything customers may do, think, buy, or share is predictable and so knowable if your MarTech is tuned, accurate, and intuitive.
Hubris and overconfidence online can be dangerous because it pushes customers away and leads to "Black Swan" events. I enjoyed reading Taleb's Black Swan: The Impact of the Highly Improbable book because the downside of our human tendency to overestimate our control, expertise, and power may predict a fall.
We would extend Aaron's "underpinning" idea to your marketing technology stack and highlight how seductive learning your known unknowns via technology becomes. It’s so easy to fall into the end-unto-itself seduction instead of remembering MarTech, Big Data, and AI are meant for greater ends: learning about people who love what you do while you create the next thing your supporters love, share, join, and buy. So, caveat fully stated, let's learn about AI and get seduced by some big data and AI tools.
I like IBM’s AI definition:
Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.
At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.
Chances are you've already “spoken to” or used AI tools. For example, most customer service systems, such as Zendesk, use AI to answer as many questions as possible because talking to people costs more than algorithms and machines. Have you tuned your writing with Grammarly, Wordtune, or ProWritingAid? Then you've used AI.
See how big data is AI's left hand? Recognizing patterns in any data makes creating responsive systems more accessible and more rewarding for customers. When thousands of customers ask about order status (big data), using ai to design systems to communicate and share data trapped in your systems turns customer anxiety into reassurance, confidence, and joy.
The Domino's Pizza Tracker is an excellent example of using algorithms to create, manage, and shape customer expectations. The tracker feels personal, but you know Dominos used AI and machine learning to share mountains of data about prep, bake, and delivery. By sharing large amounts of internal data sets in the form of a "personalized" pizza tracker, questions about your pizza are answered and shared.
Domino's use of AI isn't perfect, as their strange coupon system proves, but we'd bet they'll work out the kinks in their front end and mostly online order process soon if their tracker is a good predictor.
Google Analytics is one of my favorite analytical tools. We aren't crazy about GA4, but Google will turn off GA3 next summer. So while we are reluctantly learning to use GA4, there are things we like about Google's coming upgrade. In addition, as I mentioned in my "free advice" post, we've incorporated other website analysis tools, including:
Since only some people reading this post have their own developer teams, here are my top three no-coding-needed data analysis tools:
Understanding customers better is always a good idea. Tools, coding, big data, and ai can help, notably when you keep Aaron's "underpinning" and “it's about customers stupid” ideas in mind. Aaron correctly identifies knowing your customers better as any big data, ai tool, or coding framework's "underpinning." I'd add two somewhat radical, perhaps crazy ideas: asking for help and sharing as much of your great and powerful OZ stuff as possible.
When you ask customers what they think, value those responses, and incorporate full public credit for their ideas, suggestions, and recommendations, you're doing the most intelligent thing the Internet makes easy: crowdsourcing your challenges, problems, and solutions. We're all smarter together than alone, so when in doubt, ask for help.
Every online business is like a play. We create a stage, set the scene, and watch how our actors and audience interact and relate. Instead of wrapping your process in mystery and obfuscation, we recommend pulling back your curtain. Let your audience see how you create, develop, analyze, and think. As we shared in Start with Why (LINK to blog post), people don't join or buy what you sell; they join who you are, so being authentic and sharing your "secrets" builds trust online.
Your customers know there are stage designers (graphic designers), actors (content writers), and directors creating your online world. So have the confidence to share your behind-the-scenes, and you'll encourage comments and shares and get the help you need to win customer hearts and minds.
And, yes, there are tools for creating an online community tool – one we're working on now. I'll leave discussing our Community in a Box tool for another time, but if you have favorite tools I forgot, please share them, and we'll add them and send a thank you link your way.
Technology changes fast and sometimes in confusing ways. While our language and terminology can tend to make simple things more complex, we techies have a sense of humor, too. So here are a handful of crucial big data terms that are good to know (but still could make your head spin if you’re not a full-on techie):
What are your favorite big data and data analytics tools? Email me (like to eg@wte.net) and we’ll add your favorites and send a link your way. Thanks!
How even small websites generate big data: