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Is Your Big Data Strategy Unsinkable?

Acxiom Last Updated June 18th, 2020
Is Your Big Data Strategy Unsinkable?

There’s a wonderful exhibit about the Titanic making its way around the United States right now.  It amazing to realize that one hundred years have passed and we still rush to learn more about ‘the unsinkable ship.’  Shouldn’t it all be old hat by now?  The trials are all long past, the ship was found, fueling a boom in artifacts and an Oscar nominated movie, and yet we still want to know more about the most luxurious ship in the world, and the great tragedy that befell her on April 15, 1912.

What’s fascinating about this specific exhibit, and why I call it out, is the way it breaks down Titanic’s history in three distinct sections:  before, during, and after her fateful voyage.  For all that we know, the most important part is of often the overlooked part of the story, and is the perfect allegory for constructing a big data strategy.

Yes, you read that right, the creation of the Titanic, or more specifically, the failure in her design, is a great way to think about applying big data to your business.

See, the major supposition to the exhibit is that while the iceberg ripped a hole in the Titanic, she was actually doomed to sink before she ever left dry dock in Belfast because she was built on a vision.  Smaller, faster ships were cutting hours, even days off the Trans-Atlantic voyage, and eroding The White Star Lines revenues.  In response, they created the largest, most luxurious experience a traveler could want.  No expense was spared in designing a wondrous experience, luring customers back and narrowing the competitive gap.  Unfortunately, the focus on experience neglected the functional necessities of designing for the environment, specifically the rough Atlantic sea.  Combine poor structural design that couldn’t handle more than the slightest of tilts and poor metal quality, and the Titanic was doomed from the start.

The Titanic is a great lesson in hubris, but what does it have to do with big data strategy?  Very simply, it starts with the translation of the vision, specifically when the vision went into execution.  Simply expressing a desire to leverage big data isn’t enough.  Between articulating a desire and making the leap to execution, there are three critical steps necessary to map out a plan that will keep the vision afloat:

1. What is the business problem that you are solving for?

Note the emphasis on business.  Is what you’re building going to drive sales, curb customer retention, or expand your market share?  These tangibles enable you to define what you will need to build out a plan to support the vision, and anticipate any rough seas that could inhibit the plan.  There needs to be a reason why you are undertaking this journey.  Without that, there is no way to translate the vision into a logical plan for execution.

2. What are the appropriate, necessary data assets?

When talking to customers about big data, I find the term ‘boil the ocean’ thrown around a lot.  It’s easy to get caught up in the swirl, to believe that every single data variable is necessary, that there can never be too much, but that’s not the case.  If the solution to your business problem is to acquire new customers, then focus in on the necessary attributes and supporting functions to make the solution happen.  Build to the plan and keep the focus tight, so as not to lose perspective.

3. What is the end goal for the customer?

This is a huge challenge, because it requires us as marketers and business people to step back and think differently.  We have a goal, but our goal doesn’t always translate to the way a customer thinks, and that can be the unknown iceberg waiting out there to spoil the experience.  Building a strategy and leveraging big data brilliantly can only go so far if the infrastructure necessary to act on isn’t there.

The key is that all three points build on each other.  Looking back at the Titanic, competitive pressure helped craft a vision, but the translation from the vision to the solution was fatally flawed.  Why?  Simply, the focus was on the wrong details, ultimately compromising the customers end goal.  It’s a harsh example, but appropriate when painting the big picture.

One hundred and two years from now, what will your big data legacy be?  Will you be remembered for transforming an industry, for bringing innovation and driving growth?  If so, start by constructing your strategy with a series of simple steps, and you very well could be unsinkable.