Defining Big Data, by listening to customers

Ever since I started working on SAP’s big data initiative , I have been having a hard time defining what exactly is big data . So, my gang and I had many a discussion and figured out that the best way to figure this out is to talk to customers . I called this a “listening tour ” – visiting customers to not sell anything or to solve any problem, but just to listen to them on what they think of big data .

One CIO put it explicitly on my face – the 3V model doesn’t work for him at all . In his eyes – this is just a vendor view of the world . Volume , variety , velocity , etc are all characteristics of data and doesn’t tell him what is the BIG deal . I mentioned this to Doug Laney on twitter and he confirmed that of course it is a data management theme that he meant when he/Gartner came up with it in 2001 .

Other customers also gave me versions of this view – they all are curious about big data , but they can’t see what the big deal is . Doing the same analysis that they do today with just a lot more data didn’t sound like anything worthwhile .

“Real time” and “Right time” access to data resonates with most of them – and they see value in that . This is of course a good degree different from just a few years ago .

As I looked across all the responses – one thing stood out . There is only one big V that matters to customers – BIG VALUE.

Two somewhat related definitions came out in how customers seem to think about the concept of BIG VALUE .

1. insights that cannot be figured out by current options (in techniques, storage , usability etc ) that can some how be made available by BIG DATA

2. “power of one” – if insight can be narrowed down to one customer , one user , one product etc – in the context of a useful transaction , then BIG VALUE is delivered . This is also the aspect where real time and right time comes into play meaningfully for customers .

3. Big value is equally obtained by figuring out what “mistakes” happened it past that no one found so far , and by forward looking predictive and prescriptive insights

So how do customers expect this BIG VALUE to be delivered to them ?

1. Via a Platform – that lets them store and compute without worrying about an upper limit , at an affordable price point . Security and other “enterprisey” things are a given – big data doesn’t get a big pass .

2. Via special skills – as in data science skills .

3. Via applications – apps that give users easy access to insights in an interactive and visual manner , without having to know how data is organized behind the scenes , or learning special technical skills to use the app .

Listening tour will continue , and if there is anything interesting I can share , I will post it here


Published by Vijay Vijayasankar

Son/Husband/Dad/Dog Lover/Engineer. Follow me on twitter @vijayasankarv. These blogs are all my personal views - and not in way related to my employer or past employers

4 thoughts on “Defining Big Data, by listening to customers

  1. Great Analysis.

    Point 1 requires more elaboration. Unstructured data sources – This shall cover Enterprise content repository and Public content in Internet and Social Media relevant to one’s organisation’s scope of analysis and related services to tap external sources without storing them. Not many have explored unstructured data extensively and there is so much scope. This is a bigger add-on to Analytics.



  2. Great post,

    Glad to see someone moving beyond the punditry of “big data” (which in my world is just a marketing term) to focus on the real changes in the past few years: faster data management, “real-time” (actually near-real-time, which is more than adequate) analysis, and fast distribution for implementation.

    I like the way you are going, especially that you are not just focused on the 3V (I heard we were up to 8 V now, does not matter – irrelevant marketing hype) but rather in the value to the organizations.

    we shall discuss this as well when we meet – lots to talk about!



  3. Wonderful post Vijay! I always wondered what big data is all about and what it means to a decision maker. Looking forward to future posts on this subject


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