IDC: Tons of Customer Data Going to Waste

If you've taken a look at the latest A2 infographic, Listening to the Voice of the Customer, you know of the disconnect between marketing's desire to become customer-centric and its ability to get there. Heck, even if you're not in marketing, you can well imagine the challenge.

Marketing has to be in charge of the customer's digital experience, yet digital life is changing at a rapid pace. While the customer voice grows ever stronger, it's coming from many directions and in many forms. Marketing's view remains largely channelized, and rather myopic as a result.

The answer is simple -- on paper, that is. "We need modern marketing and modern selling, both of which put the buyer front and center. To achieve this, you're going to need an aggressive focus on data, on content, and on the competencies of using these new digital channels," says Kathleen Schaub, vice president of IDC's CMO Advisory Service.

Ah, if only real life were so simple.

As much as companies understand the need for data and analytics and are evolving their relationships with both, they're really not moving quickly enough, Schaub suggested during an IDC webinar earlier this week about the firm's top 10 predictions for CMOs in 2014. "The aspiration is know that customer, and know what the customer wants at every single touch point. This is going to be impossible in today's siloed, channel orientation."

Companies must use analytics to help take today's multichannel reality and recreate "the intimacy of the corner store," she added.

Yes, great idea. But as IDC pointed out in the prediction I found most disturbing -- especially with how much we hear about customer analytics -- gobs of data go unused. In 2014, IDC predicted, "80% of customer data will be wasted due to immature enterprise data 'value chains.' " That has to set CMOs to shivering, and certainly IDC found it surprising, according to Schaub.

"Clearly, the attention to the data within companies is growing, but given what we think is the impact of it, there wasn't enough attention" in IDC's technology marketing benchmark, she said. Preventing such waste will take initiative within marketing as well as in IT -- and she pointed to four categories:

  1. Missing data. "Marketers need to think about what it means to know everything about customers, then identify the most valuable pieces and figure out how to get them," said Schaub. She used the sales dialogue as an example, with the idea being that phone calls and meeting notes get transcribed and used with semantic analytics. "It's a future view, but at least companies need to be thinking about this."

  2. Unavailable data. This is somewhat similar to missing data, I think, but IDC's point is that this is data that's locked and not available for marketing's use. "Data comes in a lot of forms and formats, and it has to be aggregated quickly and efficiently" for it to be useful.

  3. Junk data. You might have a situation where the data is being acquired and made available but, frankly, it's garbage. IDC's data group researchers say that some 80% of data collected has no meaning whatsoever, Schaub said. "Being smart enough and having a tool to be able to differentiate between the signal and the noise is an important part of data management and making this all work."

  4. Late data. "Between every click on your website or click on one of your emails or a response to an offer is an opportunity for data to be used and used better. If you have data but it's not available to your systems in real-time in terms of the digital dialogue, then it's pretty much useless."

So think about your own company. Would you say that 80% of customer data is wasted? Do you have any ideas on how to shrink that percentage? I'd love to hear your ideas, so share below.

— Beth Schultz, Circle me on Google+ Follow me on TwitterVisit my LinkedIn pageFriend me on Facebook, Editor in Chief,

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Beth Schultz, Editor in Chief

Beth Schultz has more than two decades of experience as an IT writer and editor.  Most recently, she brought her expertise to bear writing thought-provoking editorial and marketing materials on a variety of technology topics for leading IT publications and industry players.  Previously, she oversaw multimedia content development, writing and editing for special feature packages at Network World. In particular, she focused on advanced IT technology and its impact on business users and in so doing became a thought leader on the revolutionary changes remaking the corporate datacenter and enterprise IT architecture. Beth has a keen ability to identify business and technology trends, developing expertise through in-depth analysis and early adopter case studies. Over the years, she has earned more than a dozen national and regional editorial excellence awards for special issues from American Business Media, American Society of Business Press Editors,, and others.

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Re: Industry experts above data science
  • 12/23/2013 9:14:47 AM

Musa, right -- domain expertise is not sufficient. But I do believe as analysts and data scientists mature in their careers and find their niche, that becoming a domain expert is a wise move.

Re: Industry experts above data science
  • 12/22/2013 2:04:55 PM

I think becoming domain experts for a particular industry gives direction to data science. It would nulify some assumptions made earlier by the data analysts and provide them with good direction for their techniques and research. This way they can minimize wastage of time and effort on invalid direction and be more productive. However i believe domain expertise alone is definately not sufficient.

Re: Industry experts above data science
  • 12/21/2013 1:43:07 PM

That's an interesting point. Do you think analysts/data scientists need to become domain experts so they can provide this view or does better partnership suffice?


Re: Visualizations and Data Science makes it easy!
  • 12/21/2013 1:41:28 PM

Well said, Musa.

Re: Data from multiple sources
  • 12/20/2013 10:00:56 AM

I agree. It costs money to do something with the data, so you would really like to concentrate on the more useful or valuable data. Not knowing how good or valuable the data is can lead to doing nothing.

Visualizations and Data Science makes it easy!
  • 12/20/2013 12:25:42 AM

When dealing with multi-dimensional data sets, having many attributes coming from different data sources; there are many problems that we deal with. A few of them are integrating the multiple data sets from different data sources, missing data due to numerous issues and sometimes inconsistent data. These issues put the industry experts who are responsible for making the analysis into great problems. It is very difficult for them to percieve the trends within the high dimensional data sets. Efficient Visualizations and techniques of data science are the only thing that can make this tedious task easy,efficient and very accurate. 

Industry experts above data science
  • 12/20/2013 12:13:20 AM

What i have observed with business intelligence organisations that deal with international clients and big data sets is that they base their decisions of wheather an attribute is important or garbage solely on the perception of an industry expert instead of exploring the attributes through mature statistical and data science practices. It would be a very good practice if the industry expert would rest his views and build up a story around what the data actually shows. Maybe sometimes the industry expert would come to know of an attribute that he thought was not important but later shows to have a great impact on the target attribute through some linear or non-linear relationship.

Re: Data from multiple sources
  • 12/19/2013 8:18:45 PM

... or look at it on your laptop and then go to the store and purchase. It's a tall order retailers are facing, that's for sure. They'll get the multichannel view eventually, though -- and then won't we all be up in arms over how much control they have over us as shoppers?!

Data from multiple sources
  • 12/19/2013 6:06:30 PM

I think determining which data is important is the difficult task.  Also, as mentioned, customer data is coming from so many different directions. 

For example, I might shop for something on my smart phone, later look at it again and then make the actual purchase on my laptop.  Marketers are looking at three distinct data sets, without probably realizing that it is coming from the same person.