What does Big Data mean to Database Marketing?

What does “Big Data” mean to Database Marketing?

Over the last year the term “Big Data” has been used progressively and is now part of the discussion in the budget battles taking place in many industries and organizations.  For me, it all started with hearing the word “Hadoop” back in 2010.  Who forgets a word like that?  You won’t.  The second time I heard it, I Googled it and then had a big smile on my face.  “Hadoop” and “Big Data” both mean more value for data, data integration and analytics.  It takes the value of data and data monetization to a new level.  Someday you will thank whoever coined the term “Big Data” and will hear the word Hadoop and not laugh.  There’s nothing worse than sitting in a meeting with IT and being asked “What are your thoughts on Big Data” and having no opinion.   Why should it matter to you- because it is all in the path to a bigger and now validated budget.  It also does not hurt to sound like you know what you are talking about.

“Big Data” budget for everyone

The buzzword in the budget wars right now is “Big Data”.  Everyone wants a piece of the “Big Data” budget – Marketing, HR, Compliance, Finance, and IT.  I also read that there is $200M earmarked for “Big Data” in the U.S. government budget this year.  Now that the word is out, the possibility for the application of “Big Data” is being discovered everywhere.  You could not ask for more exposure around data and funding for data integration and analytics.  For years it was exceedingly hard to justify the expense for data, data integration, data warehouses and analysis tools during budget reviews.  It was often too technical for Marketing Executives to fully appreciate and there was little understanding.

A faster path to direct answers with data

A decade ago, there was little interest in “Customer Intelligence” – and no one had coined the term back then.  We thought customer information was cutting edge but couldn’t find anyone to look at the reports because they were about “customers” not “prospects”.   In the 90’s data integration and matching consisted of homegrown processes that did
variations of exact name, phone number and address match strings, and IT would shut us down when we tried to add more business rules into the matching algorithms.  Employees spent hour’s manually matching records by looking the businesses up on the Internet, which we thought was a revolutionary data matching research tool.   There was the thought that data gave direct answers and there was no concept of what was between having data and getting results.  CDI, data governance, data stewards, and analytics were not part of our vocabulary back then.  We did those jobs but there were no cool words coined yet to describe our technical jobs.  We were in marketing, dealing with marketing executives and an IT organization that also thought we were “marketing” and were not technical like they were.

Feel comfortable saying “Big Data”

Here is a cheat sheet for mastering “Big Data” and feeling comfortable saying Hadoop.  Big Data is all kinds of data.  Examples of types of Big Data: POS and billing transactions, trade, customer interactions via sales, customer service, chat sessions, email, social media, events, and CRM records.  Customer preferences and behavior, customer usage, news, financial reports, hardware, networks and systems, product sensors, system level logs, phone records, employee results and most importantly, third party data.  The list is endless and really, any kind of data will work.

Getting back to Hadoop.  It is one of the many new types of technology that solves the problem of Big Data for businesses.  Think of it as a 3D Data Warehouse, where data does not have to be flat and structured.  Instead of a data refresh and the limitations of storage, data can just be added showing changes in time.  Imagine being able to track a sale made in 2007 to what the customer looks like in 2012 and all of the behavioral data that is in between.  There is finally an application that will give us a multi-dimentional view of data.  In simplest terms, Hadoop[i] is a high-performance distributed data storage and processing system accessible through open source software.  The system stores data on multiple servers and runs parallel processes against the data across servers and then combines the information to provide results.

Initiate the discussion in your next meeting

Still wondering why “Big Data” matters to you?  For database marketing professionals, who has more experience as business users of data, data integration and analytics?  Your experience and skills are now in demand across the enterprise.  The term “Big Data” is relevant right now, sounds impressive, is technical and is new (not really).  It has been around for years to anyone in the data world, but new to those (that approve the budgets).  So, go, grab the words and in your next meeting ask “what does “Big Data” mean to your department, organization or company”.   You could be really impressive and follow it up with “any thoughts on deploying “Hadoop” across the Enterprise”?

[i] Cloudera.  (2011).  Cloudera Ten Common Hadoopable Problems Whitepaper.  Retrieved from http://info.cloudera.com/TenCommonHadoopableProblemsWhitePaper.html

This article is by Rebecca Croucher from inside-data.com.