Ex3 Café

Instant Coffee - Revolutionary?

 

Yesterday, Starbucks formally launched a new product called Via™.  In short, the product is high-end, instant coffee (i.e. poor in "microground" coffee, add water, stir and voila).  What caught my eye is that Starbucks marketed this new product—in part—as "revolutionary."  I thought this was a bit of a stretch, since instant coffee has been around for over 100 years.  Now granted, the instant coffee of "the past" didn't taste all that great, but to market this new instant coffee as "revolutionary" I thought was a little over the top.  I even think to market this product as evolutionary is stretching it a bit, but that's just my opinion.

This product announcement made me stop and think about recent marketing innovations that may have been mischaracterized as "revolutionary" including:

• Inbound Marketing:  Per one of my earlier blog posts.  Inbound marketing focuses on creating and publishing content that "draws people in" to learn more about your company and its offerings.  The primary channels for inbound marketing include, blogs, SEO, social media...etc.  While the term inbound marketing and the communication channels are relatively new, the concept of inbound marketing has been known for years as "pull marketing" (i.e. in push versus pull marketing).
  
  
• Viral Marketing:  This is another marketing concept that has been in existence for years (previously known as referral-based or word of mouth marketing).  Remember the slogan, "You tell two friends, and they tell two friends, and so on, and so on...."  Like inbound marketing though, the communication channels have changed to distribute the message but the underlying concept remains pretty much the same.

• Data Analytics:  Previously referred to as data mining, machine learning, information discovery...etc. the terms and technologies have changed over the time but the underlying concepts and methods have remained virtually the same.

In short, the next time you hear about something marketed as "revolutionary," be suspicious, as it's likely an old idea—with a few minor tweaks—repackaged as something new.  Wait, that's what marketing is isn't it?

P.S.
I actually tried Starbucks Via™ and it was pretty good.  I still wouldn't characterize it as "revolutionary" though. :-)

Our Data Is All F'd Up...

I've heard this phrase, in one form or another, at virtually every company I've consulted for that has a centralized data warehouse.  After recently hearing this—yet again—I thought to myself, "why is the data always f'd up?" 

As I pondered this thought, I looked at the issue from both the IT and business sides of a company.  I've consulted in both areas so I have an unique view across both worlds.

IT Contributions to Messed Up Data

The following are observations I've made, while consulting on the IT side, which are likely key contributors to messed up data:

• Lack of Data Warehouse Modeling Experience:  More times than not, IT departments will put developers (i.e. C++, Java, PHP...etc.) in charge of developing the data model for the data warehouse.  The problem here is while one might be a good "coder," this doesn't mean that the same person has the proper skills and training to develop a robust data model for the data warehouse that's easy for a business/data oriented person to work with.  Some of the worst data models I've seen were developed by great programmers!
  
  
• Overwhelmed with New Data Requests:  Often times, if you manage a corporate data warehouse, you're constantly getting requests from marketers to add new data to the data warehouse.  If you don't have good processes and procedures in place to handle these requests or you're significantly under staffed, you will likely end up with an ineffective (or worse yet) inaccurate data warehouse resulting in a lot of unhappy business customers.
  
  
• Lack of Processes to Manage Source System Data Changes:  Unfortunately, mapping new source system data into a data warehouse is not a "set it and forget it" exercise.  This is because source system data can change overtime.  This can be a disaster on the marketing side, which can lead to inaccurate customer targeting or personalizing communications with the wrong content.  If there are no processes in place for the source system data administrators to alert the data warehouse administrators of data changes it's only a matter of time before a marketing crisis occurs.

Business Contributions to Messed up Data 

The following are observations I've made, while consulting on the Business side, which are also likely key contributors to messed up data:

• Lack of Basic Understanding About IT and Data:  It's vital that at least one business stakeholder have a solid understanding of basic database principles and working with IT departments.  Without this knowledge, business requirements and decisions tend to be driven by the IT side.  This can lead to systems being delivered that are substandard and have low user adoption.
  
  
• Haphazard, Last Minute Data Requests:  I see this all of the time.  Knee jerk requests from the business to add new data to the data warehouse.  Everything is a last minute fire drill.  This type of behavior leads to mistakes and it overwhelms IT business partners, thus causing friction between IT and business.  In some cases, it's unavoidable to make last minute requests.  In most cases though, a little advanced planning could help prevent such fire drills from happening.
  
  
• Not Educating IT on How Business Actually Uses the Data:  I've found that educating the IT department on how the business actually uses an application or database goes a long way.  Without providing this education, the IT department tends to operate in a vacuum and thus delivers substandard solutions to the business.

How to Fix The Problem

I'll leave this to my next blog post so stay tuned...