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Examining the True Cost of Big Data
As you start on your Big Data journey or project, be sure to ask what exactly the business requires
By: John Bantleman
Feb. 19, 2013 06:00 AM
The good news about the Big Data market is that we generally all agree on the definition of Big Data, which has come to be known as data that has volume, velocity and variety where businesses need to collect, store, manage and analyze in order to derive business value or otherwise known as the "4 V's." However, the problem with such a broad definition is that it can mean different things to different people once you start to put some real values next to those V's.
Let's be honest, Volume can be a different thing to different organizations. To some it is anything above 10 terabytes of managed data in their BI environment and to others it is petabyte scale and nothing less. Likewise velocity can be multi-billions of daily records coming into the enterprise from various external and internal networks. When it really comes down to it, each business situation will be quite different not only from a size and speed perspective but also more important from the business use-case or requirement. A large bank's Big Data problem could be very different to that of an online retailer or an airline. If you compare what say a hospital is trying to do collecting and analyzing all the sensor patient data compared to a utilities provider running a smart-grid or a telecommunications operator. True, all could be categorized as machine generated or raw data but the exact type of data might be different not to mention the volume or growth rate. Probably the one unique common denominator across all aforementioned industries is that everyone is keeping the data for longer time-periods. No one is throwing it away - not even the detailed data.
The Many Cost Factors to Consider
Leverage What You Already Have
As we all know, traditional relational or columnar databases can't handle the unstructured data types so IT needs to rollout a different solution to satisfy the business demands. Evaluations can take many forms but typically will start with which Hadoop distribution, which NoSQL or NewSQL database and what query access tools in addition to MapReduce. It is certainly no easy task as there are a large number of technology solutions on the market today that claim to run on or with Hadoop providing MapReduce or SQL-like capabilities which all satisfy the requirement of managing volumes of unstructured data. Some are more mature than others; some proven and not all are low-cost. Open source on the surface looks very low cost but as soon as you require any level of support, which lets face it once it's live and relied upon as a business critical environment, you will need to allocate a line item on your budget. The Big Data line item won't just be one line as it will need to include all components required to properly rollout a Big Data solution to truly satisfy the business demands. Just like any other IT environment the obvious pieces will include: Software licensing and support, hardware, skilled dedicated resources, professional services and training and the dedicated time of business users to provide input on key requirements including specifying types of reports, queries and analysis which will naturally change and evolve over time.
Big Data Costs Can Quickly Creep Up
There is no denying the fact that Big Data presents great new opportunities but reaching the point of a quantifiable ROI in a fast time frame is still a very real challenge. Everyone is talking about Big Data and all the innovative technology approaches to tackling it but it is still difficult to find lots of business success stories within any one-industry sector. It's still fairly immature but the good news is that its moving at a much faster pace than any other IT project today and certainly our data warehouse and BI forefathers have provided lessons learned over the past two decades.
Big Data Is Big Business but It Comes with Strict Requirements
Given the IT budget doesn't allow for increased spend that correlates with data growth rates, they need to seriously reduce costs and so decide to go the route of a Hadoop-based environment given its promise for low-cost scale and the fact that it can provide insights into customer patterns by capturing semi- and unstructured data. Front-ending the warehouse with a dedicated Hadoop cluster is the preferred architectural approach but the business users still want access to both the Hadoop environment and the existing traditional data warehouse environment.
Given we are talking about a financial institution, the question of security and availability quickly come to the top of the requirements list. At the same time, if business users want to access that data, SQL query access and using the current BI tool against that new set of data is also a requirement. If you can avoid having to the move large chunks of data on a frequent basis from one to the other, it will not only reduce costs but also latency. In an ideal world, being able to leverage the skill sets you already have and avoiding duplication of work is key.
Below is a quick table outlining the main cost factors to be considered and a set of comments against each of these areas that could reduce costs.
Summary: Consider All Factors and Get Business Buy-in Quickly
Furthermore, we all love the promise of new innovative technologies including Hadoop and MapReduce but without leveraging tried and tested standards we have come to love and respect, it doesn't make a whole lot of sense from both a technical or economic sense. As you start on your Big Data journey or project, be sure to ask what exactly the business requires and how can you leverage what you already have today. We all know, getting business user buy-in and success is half the battle to a successful rollout.
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