4. Big Data success requires scale and speed. Hadoop can process a lot of data, but it is a batch process. In many industries, real-time decision making is no longer optional. Driscoll avers that putting SQL on top of Hadoop or other Big Data stores enables organizations to actually use Big Data information in a timely way. As he puts it, "I am advocate for ‘Know SQL’ over ‘NoSQL’."
5. Data visualization is important for Big Data users. Front line professionals and others who are expected to be able to take action based on Big Data insights need an easily digestible delivery mechanism.
6. Big Data implementations belong in the cloud, insists Driscoll, because that’s where Big Data lives. While others will disagree, for various regulatory or corporate culture reasons, he says the data and the applications should be accessible via a software as a service (SaaS) approach. One of the primary reasons for putting the Big Data program in the cloud is lesson learned number 7.
7. Big Data access via mobile devices. The latest generation of touch-enabled smartphones and tablets are driving a huge change in the way companies operate and communicate internally and with their partners and customers. Ignoring their demand for access to manipulate Big Data information and insights via their mobile device is a career-shortening decision for IT managers.
8. Don’t stop at stage one, deploying Big Data to find cost reductions. Once the technology is proven, the next stage is to identify opportunities to improve an organization's top line growth. "Most companies tend to start on their Big Data voyage with a goal of achieving cost savings and then expand from there to add additional forms of data and perform analytics that contributes to top line revenue," IBM's Bhambhri notes. "Once they prove out these cost savings, they start to leverage the platform to bring in other sources of data to combine with the data they have off-loaded or the models they have now moved to the big data platform." She adds that such data types include but are not limited to telemetry data, geospatial data, additional master data from other enterprise systems, click stream data and social media data. Adding these data types enables "other LOBs in the enterprise to leverage the power and scale of the platform as well as the content in it."
This is the pattern that Bhambhri has seen in over 500 implementations across industries, including telecommunications, automotive and finance sectors.
9. If you're not in the Big Data pool now, the lifespan of your career is shrinking by the day. "If you want to stay current and in demand, it's a good idea to buy access to a Hadoop cluster and get some experience with it, as well as scripting languages," urges Davenport. "Smart IT people start to master/explore new technologies ahead of the demand and the price/performance is so much better than data appliances and data warehouses."
Indeed, Big Data projects are underway in at least a third of the large organizations responding to various surveys I've worked on, so it's clear that the hype cycle has yet to peak. If you're in IT and not already climbing the Big Data mountain, in a few years you may find yourself technologically obsolete.