Making Sense of Big Data

Stratigent has been in the analytics industry for over 11.5 years.  During that time, we have seen many "buzzwords" become part of our everyday lingo.  "Web Analytics" quickly became "Digital Analytics" and is now commonly referred to as "Multi-Channel Analytics." We have also seen words such as "Engagement" and "Optimization" which, depending on your audience, can mean about one hundred different things.
I think we can all agree that the hottest topic in our industry today is "big data."  While this topic has really become popular in 2013, I would argue that organizations have been dealing with big data for years. If you walk into a meeting full of IT professionals and try to pass "big data" off as a new concept, you might receive a negative response. The reason being that every enterprise organization has been housing and maintaining a variety of extremely large databases for years. So, what's different now? Organizations are looking to integrate the offline databases with the online digital assets.
The concept of integration is where most organizations encounter a very common mistake:  Thinking that "big data" means just throwing all the data you have into one database for the organization to use. This thinking causes several issues:
  • Internal politics
    • Who owns the data once it has been integrated?
  • Longer runway to value
    • A large-scale integration project could end up taking years.  How can you get any short-term value if you're locked up in an "all or nothing" project?
  • Lack of understanding
    • What data is important to the organization?  Without a full understanding of this it would be next to impossible to create a scalable end-product.  This goes well beyond just the ability to report on the data.  When building a plan you must also be thinking about how to operationalize the data within the organization to improve the user experience.
  • Organizational Pressure
    • Every dollar spent these days is scrutinized within an organization.  An full integration project would cost significant dollars while also having a long runway to value as I previously mentioned.
To get around these common pitfalls, there is a much more efficient way to think about big data: Get the right data to the right people at the right time. Instead of a giant database for all of your data, it now becomes about getting the right combined datasets to the necessary stakeholders in a timely fashion. For example:
The goal for the data that you collect across your channels, both offline and online, has always been for you to take action based on what that data is telling you. In addition, with budgets being tight, taking an iterative approach to big data will allow you to get some quick wins to avoid any additional pressure from your executives. The "holy grail" all organizations strive towards is to build an optimized experience for each individual, or persona, that interacts with your business. That obviously cannot happen overnight, so focus on getting the business stakeholders access to the data that will allow them to take actions to improve the customer experience one step at a time.
The ideal approach to action is to crawl before you try to run. We see so many organizations try to solve simple problems with a complex solution and that again quickly lengthens the time to value and increases pressure on the program.  Not all data needs to be integrated. In fact, there's a good chance that your stakeholders might only need to see the channel data side by side in order to draw some conclusions.  
The challenges in our industry are not going to get any easier. The number of ways customers can interact with your business will continue to evolve and become more complex from a tracking standpoint. However, I want to leave you with a some concepts you could take action on today to enhance your understanding of the customer journey and ultimately begin to optimize your return on investment from those customers:
  • Implement a Voice of Customer tool (i.e. OpinionLab)
    • Tools like this give you extremely valuable insight into the mind of your consumers.  Already have one implemented? Spend about a half hour analyzing the data to see what stands out to you.
  • Campaign Analysis
    • Get access to your analytics data to see how some of your most recent campaigns have performed to look for some low-hanging fruit.  It's always best to focus your analysis time on where the money is being spent for reach and acquisition.
  • A/B and Multi-Variate Testing
    • There are several cost-effective options out there now to get your feet wet in the testing space.  These tools can deliver an incredible ROI.
  • Automated Visualizations
    • Tools to help you with automated visualizations of data from multiple channels continue to pop up in the space.  The key is to automate as many of the manual processes as possible so you can spend your time actually analyzing the data.



By Bill Bruno
About the Author:

Bill Bruno is the CEO - North America, Ebiquity.

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