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Big data – should you start with applications or infrastructure?


I’ve been reading Erik Lundquist’s article 10 Big Data Trends From the GigaOM Structure Data Conference, and I see that he says big data projects should start with the applications before moving on to infrastructure evaluation – or in his words, “Create the new application environment and then gather the tools to make it happen”.


To some extent this resonates with the advice I offered in my blog Creating business value with big data, but I would go a bit further than Erik. Don’t start with the applications; start with a real understanding of your customers and their business. Then think about the data and information that you have, or could get. This will enable you to work with your customer to identify services you can offer that will differentiate you from the competition. It is only when you have understood the opportunity for business services that you should start to think about applications and infrastructure.


If you are only ever going to create one IT service that makes use of big data, then Erik’s advice to create the application environment and then design the infrastructure might make sense, but I don’t think this is a very likely scenario. Once your customers have seen the enormous competitive advantage they can get from understanding, consolidating and using the data that they own, they will almost certainly want to create more services that make use of this data. If you have created an environment that can only scales to their original idea, then what are you going to do next?


So, to answer my opening question “should you start with applications or infrastructure?” I think the answer is no, you shouldn’t. Start with understanding how you create business value, and then develop applications and the infrastructure in parallel, ensuring that the infrastructure will support the needs of current and future applications, services and business opportunities.


The HP approach is to build a big data refinery that can be scaled to meet your customers’ current and future big data needs. My colleague Amos Ferrari expressed this in his blog Designing Your Big Data Infrastructure: A New Functional Architecture, where he said “Even though today Big Data adoption tends to be based on a phased – “start small” – approach, organizations should carefully consider from the beginning how to connect new Big Data technologies to their existing infrastructure.”



Learn more about HP Consulting Services and how HP can help you shift your focus from operation to innovation.



If you want more ideas to help you think strategically about IT services, then read some of my other blogs (most recent blog is at the top):

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Great point -- we're doing all this work based on a business need.

I think the general approach of starting with business value is sound, but that there is also a potential danger in that approach. As IT moves from being the “keepers of the technology” to “partners in the business” they take on a kind of consultative selling role. In consultative selling it is tempting to simply ask “what is your pain?” and develop solutions based on that pain. The difficulty is that this approach nearly always filters all of the technical possibilities through the business perspective – without considering the potential business value.  My point is that the business may not know what is possible, so may miss out on implementing truly differentiating capabilities. 


IT’s role must certainly shift to that of business partner: but it cannot be a passive “tell us what you want and we will implement it” role. As technology investments become more expensive, strategic, and interconnected, IT must play an increasingly important role in building and preserving the value of IT assets. I think we still have a lot of potential for growth in this area.


Charlie, Joe,


Thank you for these comments. There is a limit to how much content fits in a blog post. I will be delivering a presentation at HP Discover where I will elaborate on these ideas and present a suggested structure for engaging the business in a discussion on value of big data.


As usual, Stuart, I agree with you.  A large convenience retailer found this out when they were tracking beer sales.  The merchandising VP noticed a swing in beer buying patterns.  Customers were increasingly buying domestic brands instead of imports.  They realized, 6 months before the fact, that a recession was on the way.  What they didn't know was how big it was going to be.


That event made them realize that they needed to rethink their entire IT strategy.  Their infrastructure and most application management were outsourced, so what they really needed to do was look at the way they structured, used and responded to their information.


They did so by appointing business people to co-manage key IT positions with technical people.  The merchandising VP became the CIO.  This company has revolutionized every key business process so that the quality, price and selection of goods is geared towards every micro-demographic in every area they do business.  They can tell you more about the impact of what's happening in any part of the world than any newspaper that they sell.


Thank you David, that sounsd like a really interesting case study.


In my HP Discover presentation I discuss 4 general categories where big data can help with value creation. One of these is "Understanding variability in customers and markets so that you can offer differentiated products or services to different market segments". This concept seems to apply across many different industries, but I didn't have an example based on beer before!

I am co-presenting this topic with Stuart at this year's Discover. Our key message was around how more than ever IT and Business must work together to optimize the value of bigdata. While this may not change the way we manage IT operations, I believe the skills requirement in the IT organization will increase moving forward. IT will require more industry and market understanding and innovation.

David's beer story is a prime example of such.

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