Big data is rapidly evolving from proof-of-concept to production. While the terms big data and cloud were previously taboo, many businesses are looking to big data as a source of customer insight and a way to bolster operational efficiencies. The cloud is becoming a viable option as many IT managers are faced with the challenges of finding ways to collect, store, manage, and analyze large amounts of data collected from thousands of sources.

As big data production applications migrate toward becoming the norm across organizations, it is being increasingly applied to enterprise-wide, mission-critical operations. Big data has become too massive to keep on a single proprietary system. As such, the demands for integration are increasing exponentially. The current task at hand for many IT decision makers is to create a plan for taking their businesses data to the cloud, and how it will be used after it is there. But once that is complete, the monumental task of building out a long-term strategy for their data to succeed in real-world corporate IT settings awaits.

The Big Data Model

Big data activity can be aggressive. Often this means that the issue of data collection is way ahead of storage and utilization capabilities. When managing a big data infrastructure, one of the key factors involved in making big data work successfully is building a complex stack, as outlined in the image below. When planning a big data strategy, considerations for big data's massive infrastructure requirements, along with creating a manageable big data ecosystem that supports security, scalability, and reliability can be tricky. The cloud offers viable options that are hard to match on-premise.

big data stack

Factors to Consider for a Successful Big Data Strategy

There are four key factors to consider when creating a strategy to meet your businesses objectives. Cloud platform management tools provide infrastructure managers with the ability to handle big data efficiently. The goal is to develop a big data plan that meets your business needs while providing an economical, flexible, and secure model.

1. Build an Elastic Infrastructure

Companies collect, analyze, and mine data via different avenues. While big data flows on a large scale, it can be difficult to forecast. As you begin to analyze your data flow, you will need to identify the data types and sources, and classify the funnels for use cases to leverage the data. Creating an elastic infrastructure that has the flexibility to support multiple approaches for integration with one another as well as external elements are critical for long term success. The data in your Hadoop application comes from many sources, which will be fed into a variety of applications to maximize their value for different constituencies. The constant shifting of big data workloads requires an infrastructure that can scale elastically. Cloud infrastructure allows for the ability to scale up and down at will and add or remove data analytics capacity on demand. A cloud-based infrastructure is the best option for ensuring that you have a flexible, elastic, and scalable base.

2. Availability and Reliability

Real-time big data will soon become the standard. When building a stack to support big data management success long term, high availability and reliability are key factors to consider. The thing about data collection is you never know when you may need to extract it. Data collected today might be pulled in five years to build a revenue report or a marketing campaign. The key is you have to be able to access your data in real-time when your business needs it. Big data is the future of targeted marketing. Online advertisement is a great example of this. Companies are monitoring, collecting, and analyzing their customer-based audience's behavior. In response, they create real-time ads based on a specific users data history. Using a cloud platform provides the availability and reliable infrastructure necessary to pull, store, and utilize that scale of data. The results are real revenue producing opportunities for businesses, when done correctly. As big data becomes an integral component of day-to-day customer relationships, the bottom line is that it needs to be highly available and reliable. Businesses that don't implement these key factors into their infrastructure will find themselves lagging behind the competition and, inevitability, the bottom line will prove it.

3. Plan with High-Performance and Low Latency in Mind

Since big data workloads function differently than traditional enterprise applications, a high bandwidth, low latency network is an important piece in building a successful big data model. Big data applications typically require a high level of uptime and speed, coupled with low latency, all while relying on a more robust integration across cloud and on-premise systems. Distance and network directly effect how a big data environment performs as they must be able to access and analyze data from both distant and disparate sources as though all of them were local. The closer the data center is to your users, the better the performance of your data ecosystem. The ideal environment allows for the effective ability to handle huge quantities of various types of data (structured, semi-structured, and unstructured) and, in turn, produces real-time analytics. In order to be successful, this type of environment requires high bandwidth and low latency network connectivity. Cloud infrastructure is able to deliver these key components in an economical way.

4. Don't Forget About Security & Compliance

Security and Compliance are two huge factors that are often overlooked when companies begin to lay out their big data strategy. Too often, they become an after thought, usually brought on by a catastrophic event, which causes an emergency scramble of the IT management team to repair. But generally, by this point, the damage is already done. Whether it is data loss or a security breach, the end result is almost always the same---loss of revenue and damage to the brand name of the company. These kinds of events are in the news regularly, and while even the most stringent security and compliance structure is not completely safe from falling victim to these events, there are steps that businesses should take into consideration to minimize the chances of them occurring. And, while having a successful business continuity strategy implemented is a good start, big data requires big security.

Drive Value With Your Data

As the Internet Of Things(IoT) continues to grow, so does the percentage of data available for mining. The possibilities of use cases in this data pool to drive value for corporations are endless, if time is put into the planning of the big data ecosystem. Security, reliability, and a strong infrastructure are all distinguishing criteria for a successful model. Enterprises will need to ensure they use partners with established platforms that can support highly available infrastructure and secure data stores for the collection and processing of large data sets. When deciding how to tackle big data, companies must consider compute, storage, security, and availability.

Big Data Challenges Solved in the Cloud

CenturyLink can help you create a big data strategy on a global scale that fits your company's unique needs. The vast Hybrid IT service catalog enables organizations to realize critical insights to help drive value from their data.

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