Rethink BI : Business Insights over Business Intelligence
The purpose of this business insights thought leadership blog is to share the HP point of view on industry trends such as Big Data and Real Time Analytics, and provide updates on key innovations and solutions.

Five Big Data Myths Busted (in under five minutes)!

Happy New Year to all! (This is our first post of the year)

 

At HP Discover last December, we had the opportunity to chat with a number of HP customers and business partners on Big Data. As a result, we were able to unearth a number of myths on the key technologies, approaches and challenges of enterprises looking to build analytics platforms for Big Data.


1. Big Data = Social.

 

It's 2013 and yet there are people who believe that Big Data is all about the social media explosion. Granted that most presentations on the subject start with a cornucopia of frightening stats on the data generated by our social lives and how social media giants and startups alike have single handedly fueled the mercurial growth of Big Data technologies. However, there is more to Big Data than social. Much much more. While unstructured data gets all the buzz, there are piles of structured (think machine-generated and sensor data) and semi-structured (think standard Excel sheet templates and XML documents) that deserve analytic love too. 

 

2. Big Data is for others. Not me. 

 

An immediate corollary of the Big Data = Social discussion is that since Big Data is Big, it isn't for me because I deal with GB, or TB, of structured data (database tables). However the truth is that for most enterprises, Big Data is a matter of when, not if. The more prepared you are now, the better your chances of survival when the Big Data wave hits your industry or enterprise. Decision making based on analytics is becoming a key differentiator across verticals, across geographies and across company sizes. Research suggests that top performing companies are over five times more likely to use an analytic approach within their business processes than to rely on intuition.


3. I cannot scale my existing systems to Big Data proportions.

 

While Big Data by definition consists of data sets whose analysis is uneconomical using existing BI systems and analytic cubes, it is important to understand that most companies use their existing infrastructure as a starting point. With good reason too, because there are many low hanging fruit to be had by optimizing, rewiring, and scaling existing infrastructure to handle Big Data workloads. Additionally, open source technologies such as Apache Hadoop lend themselves to a heterogeneous environments with old cranky servers, shiny new blades and even laptops all coexisting in   the same cluster because Hadoop has the smarts to allocate workload by compute resources. In fact, a large portion of Hadoop projects start out as pilots running on surplus (read outdated) servers and transitioned to newer scale out servers in production.


4. Cloud and Big Data don't mix.

 

Duh! If at all, cloud is a convenient, low risk option to earn your Big Data stripes. Many LOBs (lines of business), frustrated by rigid and unresponsive IT, have found much success building pilot analytics projects in the cloud. In some cases, they have transitioned to production while still in the cloud, and in other cases have been able to insource the project quite seamlessly. In addition, many CIOs are looking to offer private cloud solutions to LOBs on a pay-as-you-go basis. This has been the preferred approach, especially in governance-sensitive verticals such healthcare, insurance and financial services.

 

5. Specialized Big Data devices lock me in.

 

Actually, this one is partially true depending on the vendor in question. All Big Data specialized devices (or appliances) are not created equal. There are those who take an "iPhone approach" (Thou shalt only use iTunes apps, never change the battery or add memory) and there are those, like HP, that use an open approach (à la Android) that focuses on customer choice and industry standards. HP Appsystems for Big Data offer you the right mix of time to value and flexibility, so you can deploy a fully functioning Hadoop system, for instance, in hours or days and yet grow incrementally as your business needs evolve. In addition, HP includes data connectors to ease integration into your data center so you can focus on the analytics rather than data center plumbing and platform engineering. 

 

So there you have it! Five Big Data myths busted in under five minutes - we would love to hear your thoughts and feedback. Here is a video recorded at HP Discover, by Jake Ludington, that captures some of this discussion and more...

 

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