The Problem with Big Data is Context - Information Architecture Makes the Difference

The story of the blind men and the elephant illustrates how a limited perspective can lead to erroneous conclusions.  Big Data can create the same fractured, incomplete perspective… only faster.  

Big Data Analytics has taken center stage in many companies.   Retailers and manufacturers of consumer products use advanced analytics to segment customers and predict buying behavior.  Vast amounts of digital information also provide feedback on customer experience.  Corporations in regulated industries are using Big Data to ensure compliance with a growing number of regulations.  Ensuring compliance is complex and expensive, unless you can analyze structured and unstructured content (like emails) to identify potential violations.

Here’s the trouble – Big Data engines can analyze a lot of data, but without context linking the analysis to your specific organizational vocabulary--such as product hierarchy and attributes-- the Big Data engine will have you operating as one of the blind men.  The following example illustrates the point.   

You are seeking to understand a customer complaint such as:

“Waited too long on hold. Refund was not issued. Screen resolution was poor and battery did not last on my computer.  Customer service rep did not have knowledge to answer questions.”  

You must:

1. Parse the text
2. Interpret which departments and products are causing the problems.  
3. Classify the information according to process, product, supporting function and department.

That’s where the real problem starts.   A sentiment analysis engine built on Big Data cannot distinguish the names of your products and services.  It may not classify this complaint properly.  You might miss the chance to act… resolve the problem… protect your brand.

So, without an architecture, taxonomy, and domain model, the blunt tool misses the point, requires manual interaction, and slows service.  

Instead, you can improve the results of your Big Data projects by incorporating information science so you can,

• Diagnose gaps in current analytics processes
• Quickly operationalize big data initiatives
• Align customer experience analytics with internal process metrics
• Remove manual processing steps from big data programs
• Reduce the cost of integrating analytics tools

Big Data Advanced Analytics is the “in-thing.”  Without information architecture and other interventions, Big Data can be overwhelming, expensive, and distracting.

We can help put data analytics to work to improve marketing and customer satisfaction and gain a crucial competitive edge. Give us a shout.