What is Big Data? Big data is an evolving global term that describes a voluminous amount of aggregated, complex data that cannot be processed by conventional database management tools. The data is extracted, formulated and sourced from structured, semi-structured and unstructured strains. In short, big data is data that can no longer be analyzed or managed by traditional technologies.
The multi-part, intricate nature of Big Data present major analytical challenges for organizations who cannot manage the varied, fluctuating and exponential growth of data in real-time ecosystems. Small and medium-sized enterprises are challenged to derive intelligence from the massive inflow of data that is accumulating in parallel and divergent environments. The short answer lies in infrastructure-as-a-service.
Self-generating data and data producing sources can overload and undermine system architecture if the data streams are not smartly managed, prioritized in set hierarchies and mapped-for-optimization. Furthermore, most businesses unwillingly tap into reservoirs of peripheral data sources and combine them with existing proprietary data, which can further add complexities and will further tax traditional, non-dimensional databases.
While big data is multi-layered and collects from multiple channels, it is largely bilateral. Machine-generated data, a pillar of what constitutes big data, is automatically created from applications and software. This system-initiated and machine-to-machine data being produced is static and amorphous. An example of a machine-generated data is the system logs that are seamlessly generated by operating systems and software on a daily basis. Another major component of big data is ones that are human-initiated, such as photos, documents, audio, video, emails and other forms of transaction-based data that are generated from diverse sensors. Despite having a loaded name, the bigness of data is not specified from a quantitative standpoint, although it is generally used to describe hard-to-integrate data types, like exabytes and petabytes. The term “bigness” also scares those who are trying to understand cloud computing for small business.
The ever-expanding nature of big data has spurred the need to rollout, adopt and combine smart, intuitive big data-ready architecture, which seeks to glean intelligence from complex data sets and gain business insights through the organization of the data. This insight leads to being able to accurately analyze the data for business decision-makers.
Big data is expanding on three frontlines:
- The progressively-fast growing volume of data in today’s business environments.
- The qualitative nature of data variety.
- The velocity, at which data must be processed.
Many factors contribute to the skyrocketing increases in data volume. Management is necessary or it will spiral out of control because data comes from a multiple of sources. Much of data varieties are out of your control and are generated by a system that is running in the background, such as server-side applications. In the face of multiplying data levels (where business data doubles every 40 months) and peak data loads, the need to connect and correlate meaningful data relationships and linkages becomes business-critical.
The speed by which data is created is not only unprecedented but even more critical than the amount of data that is stored. The velocity by which data is processed and understood is especially imperative for today’s business that needs to respond quickly to market trends and internal drivers. With the inpouring of real-time data, which has become standard in today’s fast-paced marketplace, the need to process and make sense of the information that is coming in at a rapid-pace necessitates smart technology and architecture that can filter, organize and deliver information on a need-to-know basis.
Today’s data comes in all types, formats and variations. The structured type of data usually comes from numeric data and most applications, which have a built-in data structuring system that pushes-out easy-to-organize information. Unstructured data can come in the ways of audio or video, where data is disproportionate, unbalanced and of different frequencies. Merging and linking these data varieties is no simple-task and is something most small-to-medium sized business still have a difficulty in managing.
Secondly, according to SMB group studies, integrating new technologies is the number one challenge for midsized businesses. There is a serious disconnect that undermines big data adoption, which results in productivity drains, predisposed to enterprise-wide inaccuracies and the absence of concrete data streams to support decision making. SMBs are opting to go for pre-integrated software suite that brings together diverging applications and put them all under one umbrella.
The need to unify reliable data lies at the base of all solid analytics and reporting. Vendors are catering to SMBs, such as Microsoft Office 365, which improves productivity, saves time and empowers midmarket organizations to make better decisions. Vendors are proactively targeting SMBs by making huge strides in user-friendly interfaces and cognitive computing that is aimed at increasing adopter rates of big data. With vendors raising the bar and looking to capture the SMB markets, midsized business can now take advantage of big data-accessible speed, customer proximity and new insights by uncovering data trends and patterns.
The third impediment is that businesses have not explored their own internal business sensors to see if they are big data-ready. SMBs must realize that big data presents a lot of opportunities, and helps in your ability to analyze and gain a better insight economy. Locate where your data streams, both inflowing and outflowing, are coming from.
Determine hierarchies and priorities, and layout the applications; determine social connections and social media presence; and business activities that you use on a day-to-day basis to regulate data streams. Map them out and show how they affect your business. The last step is to focus on your clientèle. IT focuses on automating and creating pathways in the back-end to ensure connectivity, system integrity and reliability, so you don’t have to. By keying-in on customer demands and concerns, you can ensure that data provides real-time value so that a big data ecosystem can further improve on current customer relations.