The Rise of Big Data in the Enterprise Space and How to Handle It

Big data and the insights that companies can glean from big data analytics has become a transformational tool for enterprises across all industries. In a study from NewVantage Partners, 48.4% of executives report that their firms have realized measurable benefits as a result of highly successful big data initiatives, with only 1.6% classifying their efforts as a failure.

Big data can help businesses across many industries to uncover hidden trends and patterns in the marketplace, as well as achieve insights into customer needs and preferences. This information can enable an organization to create more effective marketing campaigns, improve customer service, discover new opportunities for revenue, and improve operational efficiency. Baseline researchers found that a 10% increase in data accessibility translates to $65.7 million for the typical Fortune 1000 company, making big data analytics an essential step for a competitive enterprise.

Technological advances in networking and computing have made big data analysis accessible to companies of all sizes. However, an enterprise considering the launch of a big data analytics program must plan carefully, taking into account the challenges posed by the practical application of big data analytics.

The Three V’s of Big Data: Volume, Velocity, and Variety

You can characterize the information collected and analyzed through big data applications by three V’s – volume, velocity, and variety. Volume refers to the enormous amount of data that is being analyzed. Organizations must scale hardware, software, and storage to accommodate this. Velocity refers to the speed at which new data is created, as well as the need for an enterprise to conduct analysis immediately and make necessary changes in real time to reap the benefits of big data analysis. Finally, variety refers to all the different formats in which data is collected, including IoT devices, spreadsheets, text, video and audio recordings, and traditional databases. Combining structured data from a database or spreadsheet with unstructured data, like that from a video recording, requires a sophisticated mesh of analytical tools.

An enterprise must have a plan in place to accommodate the complexities introduced by big data analysis, including volume and variety of data, and velocity of networks and analytical applications. A big data analytics program also means that organizations must have the appropriate system in place, one that is fully scalable to deal with the growth of data that you must transport, analyze, structure and store.

Preparing the Network for Big Data Analytics

An Edison Carrier Solutions technician working in the field.Exploring the feasibility of a dedicated circuit, such as a managed wavelength service is one proactive way to prepare an enterprise network for the influx of data, with an eye to maintaining transmission speeds (addressing volume as well as velocity). Managed wavelength services provide a point-to-point circuit for transporting data over a DWDM network. The carrier manages the service, offering a private, dedicated network to carry data.

In addition to providing the capacity for a big data analytics application and the subsequent volume of data, an organization must plan for network congestion. Congestion can result in reduced quality of service for employees and external customers alike. To avoid bottlenecks, an enterprise might consider expanding the network, via additional bandwidth or a redundant circuit, to provide for redundancies in case of extreme congestion.

Gain a Competitive Advantage with Big Data

The opportunities presented by big data analytics are enormous and could provide an essential competitive advantage in today’s fast-paced marketplace. Enterprises can reduce costs by using big data analytics to find opportunities for operational efficiencies. They can also reap the benefits of better decision-making, using real-time data to respond to environmental changes for the interests of the company.

An organization can also differentiate itself through improved customer service, applying the findings of big data analysis to be more responsive to customers and to improve products and services. Predictive analysis can be used to reduce fraud and risk to organizations while helping to keep an eye on future trends and customer needs.

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