Aligning Big Data and Data Center Infrastructure Strategies

Featured article by Mark Wachtmann, Vice President Product and Technology at IO

Big data has become a de facto part of business, and there is no reason to treat it otherwise. Despite the mainstream nature of leveraging big data, many organizations struggle to adopt and align a big data mindset with their day-to-day business. By grasping the basics of big data and facing the obstacles head-on, IT leaders can take advantage of all that big data has to offer.

Big Data Basics

Big Data is a strategy that requires asking the right questions and analyzing the right data with the support of executives and the right infrastructure. Without that foundation, getting mired in the “big data or not” and “Hadoop vs. Spark” debate is pointless.

A big data foundation starts with a solid understanding of a business objective. For instance, a marketing director at a pharmaceutical company may want to cut the time and expense of getting the next “PharmaStar” to market. A loan officer at a mortgage company may want to minimize risky loans to ensure profitability. Retail buyers may want to gauge trends so that they can deliver the right product at the right time—and through the right channel—to their customers.

Understanding what questions to ask keeps the process from going down the rabbit hole.

A big data strategy also requires the right, high quality data. This is often referred to as the “single source of truth”—the one source of data that everyone in the company agrees is the real, trusted number. In other words, bad data cannot be trusted to deliver good results.

Obstacles to Big Data

One key to a big data strategy is executive buy-in. This means showing the executive team the value of a big data solution, but that’s often the easy part. The more difficult part is getting them to commit the resources necessary to establish a single source of truth—integrating all the data silos—and to ensure that the infrastructure has the capacity and performance needed to analyze all of that data.

Once the right questions, a single source of truth, and the executive support and resources are established the largest obstacle remains: the infrastructure. This is especially true for rigid legacy infrastructures that lack the capacity and performance requirements needed to quickly scale and ingest massive amounts of data.

As a result, many companies are turning to cloud services to run their big data workloads. Even though cloud computing and big data are still evolving, they are completely conjoined. The cloud can provide a cost-effective and scalable infrastructure that nimbly supports big data and business analytics.

Colocation is another option to consider as a data center infrastructure for big data and analytics, particularly with data housed in multiple locations. Colocation providers, especially those that are carrier-neutral, offer multiple connectivity options so different types of data from different sources can move into a single repository for analysis.

Like cloud computing, colocation also offers scalability and thus the option to store massive amounts of data and adjust capacity as needed. Colocation has the added advantage of allowing a business or organization to build a high-performance computing (HPC) environment for more research-intensive needs—something that’s nearly impossible in most corporate data centers.

Finally, both cloud computing and colocation offer considerable business value for big data projects, helping deliver an excellent return on investment.

Big Data at Your Business

Pilot projects are an excellent way to set the course for big data strategies that can benefit your company. They can also be very persuasive with getting your executive team on board. Both cloud and colocation can provide the right infrastructure to run a pilot project.

Going forward, it’s clear that most successful big data strategies will incorporate a range of big data technologies running in the cloud or in a colocation environment.