Understanding Big Data - Success Knocks | The Business Magazine
Big Data
1374
post-template-default,single,single-post,postid-1374,single-format-standard,bridge-core-2.6.5,qode-news-3.0.2,qode-page-transition-enabled,ajax_fade,page_not_loaded,qode-page-loading-effect-enabled,,qode_grid_1400,footer_responsive_adv,hide_top_bar_on_mobile_header,qode-content-sidebar-responsive,qode-theme-ver-25.0,qode-theme-bridge,qode_header_in_grid,wpb-js-composer js-comp-ver-6.5.0,vc_responsive,elementor-default,elementor-kit-344

Understanding Big Data

Understanding Big Data

F

inding real value in data is critical to every business today. But before we mine it for business insights, we need to access this data from all of our relevant sources accurately, safely, and quickly. How? With a foundation that integrates multiple data sources and can transition workloads across on-premise and cloud boundaries.

What is big data?

Big data is data that is either too large or too complex for traditional data-processing methods to handle. In general big data has come to be known for its “three Vs”: volume, variety, velocity. Volume refers to the extreme size, variety refers to the wide range of nonstandard formats, and velocity refers to the need to process quickly and efficiently.

Why does big data matter?

Data is valuable, but only if it can be protected, processed, understood, and acted upon. The goal of harnessing big data is to offer real-time information that you can use to improve your business. Real-time information processing is one of the major goals for companies attempting to deliver value to their customers in a consistent and seamless manner and is one of the crucial features of edge computing. Insights from big data could allow you to cut costs, operate more efficiently, and discover new ways to boost profits and reach new customers.

What makes big data so important?

Consumers live in a digital world of instant expectation. From digital sales transactions to marketing feedback and refinement, everything in today’s cloud-based business world moves fast. All these rapid transactions produce and compile data at an equally speedy rate. Putting this information to good use in real-time often means the difference between capitalizing on information for a 360 view of the target audience, or losing customers to competitors who do.

The possibilities (and potential pitfalls) of managing and utilizing data operations are endless. Here are a few of the most important ways big data can transform an organization:

Business intelligence

  • Coined to describe the ingestion, analysis, and application of big data for the benefit of an organization, business intelligence is a critical weapon in the fight for the modern market. By charting and predicting activity and challenge points, business intelligence puts an organization’s big data to work on behalf of its product..

Innovation

  • By analyzing a periscope-level view of the myriad interactions, patterns, and anomalies taking place within an industry and market, big data is used to drive new, creative products and tools to market.
    Imagine “Acme Widget Company” reviews its big data picture and discovers that in warmer weather, Widget B sells at a rate of nearly double Widget A in the Midwest, while sales remain equal on the West Coast and in the South. Acme could develop a marketing tool that pushes social media campaigns that target Midwestern markets with unique advertising highlighting the popularity and instant availability of Widget B. In this way, Acme can put its big data to work with new or customized products and ads that maximize profit potential..

Lowered cost of ownership

  • If a penny saved is a penny earned, then big data brings the potential to earn lots of pennies. IT professionals measure operations not by the price tags on equipment, but on a variety of factors, including annual contracts, licensing, and personnel overhead.
    The insights unearthed from big data operations can quickly crystalize where resources are being underutilized and what areas need more attention. Together this information empowers managers to keep budgets flexible enough to operate in a modern environment.

Organizations and brands in almost every industry are using big data to break new ground. Shipping companies rely on it to calculate transit times and set rates. Big data is the backbone of groundbreaking scientific and medical research, bringing the ability to analyze and study at a rate never before available. And it impacts how we live each day.

Big data analytics and IT optimization

Big data analytics is the term for the process of taking all of your raw and dark data and making it into something you can understand and use. Dark data is data that organizations collect during normal business activities that they must store and secure for compliance purposes. Dark data is often overlooked but, like the rest of your data, can yield valuable insights that you can use to improve your business.

Big data insights can help you prevent costly problems instead of reacting to them. Analyzing data patterns can help you predict customer behaviors and needs instead of guessing (which can also help you increase revenue).

Data lakes, data swamps, and big data storage
A data lake is a repository that stores near-exact or exact copies of your data in a single location. Data lakes are becoming more common in enterprises who want a holistic, large repository for their data. They are also less expensive than databases.

Data lakes let you keep an unrefined view of your data so that your top analysts can explore their refinement and analysis techniques outside of traditional data storage (like a data warehouse) and independent of any of the system-of-record (a name for the authoritative data source for a given element of data). If you want your most highly skilled analysts to continue honing their skills and exploring new ways of analyzing the data, you need a data lake.

Large organizations have several business units (BUs) each with their own unique data needs. Each of these BUs has to compete in some way to get access to the data and infrastructure in order to analyze it—it’s a problem of resources. Data lakes don’t solve this problem. What you need, instead, is multi-tenant workload isolation with a shared data context. What does that mean?

Basically, instead of making a full copy of your data every single time a new business unit needs access (complete with the admin work of writing scripts to copy the data and make it all work), this solution enables your organization to pair down to just a handful of copies that can be shared across BUs through containerizing or virtualizing the data analytics tools.