Because it can benefit business at nearly every level of operation — organizational efficiencies, development, customer interactions, and management processes — Big Data is here to stay.
For Big Data to live up to its big promises, however, businesses must take steps to ensure that they have the right strategies and technologies in place to gather, store, and analyze the massive amounts of information they collect.
What factors should businesses consider when figuring out their Big Data plans? Let’s take a look at a few:
One of the first questions to answer is: What kind of data is my Big Data? According to 88 percent of organizations surveyed in a TDWI report, their primary Big Data types are traditional transactional and analytical data living in relational databases. But Big Data can also be unstructured information like text, images, or videos.
Knowing your data can help you make the right decisions about the technology that will process it. For example, does your platform ensure high availability of your Big Data clusters with redundancy across all nodes, load balancing, auto failover, and resynchronization? Once data is ready for analysis, where will it be loaded — data marts, internal users for local analysis, external sources?
Because of the sheer breadth and depth of the data that gives this trend its name, it’s important for your business not only to collect information, but to make sure it has the technology that’s capable of growing with the data.
Such technology is tightly integrated, horizontally scalable, and extensible, says Alteryx COO George Mathew, who was a speaker at a recent SiliconANGLE Big Data event. In servers, he says, taking the layers of traditional vertical stacks and scaling them out horizontally holds the promise of enabling a fundamental shift in how businesses can process and consume information.
No matter how big Big Data becomes, it must still integrate into and complement your overall organization, including existing analytics, BI, databases, and systems.
There is no one right way to approach Big Data architecture, and components should be modified for workload, but a good plan takes into account how the new data will be merged with a business’s current data. One way is to make sure Big Data is defined and documented in an enterprise data dictionary or a data model.
No discussion on Big Data would be complete without the mention of a particular buzzword: Hadoop. This software enables large data sets to be distributed and processed across clusters of servers. And it holds a lot of promise for businesses honing their Big Data strategies because of its ability to start with smaller-scale deployments for specific types of data.
By starting small, businesses can focus on using data for specific knowledge, such as understanding customer needs, making processes more efficient, reducing costs, or better detecting risks. Once businesses see value in using Hadoop for specific projects, they can then expand deployments for use across the organization.
What strategies are you employing for your organization’s Big Data programs? What Big Data technologies would you like to learn more about?