SAS Solutions Technology enthusiasts – or ‘techies’ – are always interested in the latest trends, and for years, “big data” was trending at the top of every must-have technology capability list. Advisory firms, such as Gartner, published a myriad of research on big data and provided guidance regarding the emerging technology’s lifecycle and capability to solve real-world business challenges. The Gartner Hype Cycle, for example, is a graphical representation of the maturity, adoption, and application of emerging technology. Big Data emerged and rose to the peak of inflated expectations in 2013 and then began to fall, causing many to wonder whether the promise of big data was mostly hype and in its demise. So where are we in the lifecycle and evolution of big data today? No longer a buzzword, big data disappeared entirely from Gartner’s hype cycle in 2016, but not because it is no longer relevant. By contrast, it has rapidly become an integral part of the mainstream, reflecting the vast amounts of data we produce every day and the expectations for continued exponential growth. Yet, businesses remain challenged by the need to capture, store, analyze, and curate large volumes of data in order to leverage it as a strategic asset. In many cases, data sets are so massive and complex that they can’t be sorted out by conventional data processing technology and software.
“Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.” – Vishal Kumar, AnalyticsWeek, Big Data Facts
In theory, big data has many practical applications, but the reality of finding business value in big data is a prominent problem for companies across industry verticals. As a result, technology companies have been inspired to develop creative solutions to address many facets of this business dilemma. Big data has evolved into a major catalyst for innovation and more recent emerging technologies.
Big Data Inspires Innovation
The practical application of big data to real-world business problems is complex; and companies are undertaking big data initiatives and investing in innovative technologies to address data challenges, including:
- Big Data Management & Storage: Technology disruptors such as Hadoop and NoSQL have provided cost-effective alternatives to traditional relational database management systems (RDBMS) that operate on a single server and are more difficult to scale to meet big data needs. The parallel processing capabilities of distributed computing architectures like Hadoop are what companies now require when faced with managing large volumes of data.
- Big Data Processing & Analysis: Big data is characterized by its Volume, Velocity, and Variety, all of which increase the complexity of data processing and analysis. For large volumes of data, analytics processing capabilities are innovatively being placed closer to where data resides to eliminate the need to move or copy data for analytical processing purposes. In-database technology improves performance and drastically speeds the return of results, and is widely used in the financial services industry for credit and fraud applications. Edge computing is another innovative technology approach that is tackling the challenges of big data. The variety of data being collected and analyzed for business purposes includes device data from sensors and the Internet of Things (IoT). Processing critical data locally at the edge of a network and closer to its source reduces the data latency that would otherwise result from needing to send all data across a large network to a central data center before it can be processed.The velocity, or flow, of data is also a factor to contend with, particularly when data is generated and collected in real-time (i.e., the Twitter “firehose”). Businesses that need to make smart decisions in real time are relying on advanced technology that provides real time decisioning and event stream processing capabilities. The ability to collect and analyze data in real-time enables marketers to easily personalize engagements with consumers and react quickly, for example, delivering a discount offer while a consumer is shopping on an e-commerce site and begins to empty or abandon a cart.
- Big Data Visualization: With the capability to collect, store and process large amounts of data, came the business need to easily transform data from disparate sources into intelligence, extracting data insights that can be easily shared across the organization. Data Visualization tools like SAS Visual Analytics use in-memory technology that enables business users to explore and manipulate massive amounts of data in mere seconds. Charts, reports and graphs can be easily shared via the web, mobile device, or ported into commonly used Microsoft Office applications. Businesses of all sizes and across all industries are leveraging data visualization capabilities to make better sense of big data.
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About the Author
Danielle Maughn, Director of SAS Solutions Consulting
Danielle serves as a trusted adviser with over 15 years of experience working with well known brands across retail, hospitality, and telecommunications in the marketing and technology space. Prior to joining, Danielle was a Solution Architect in the Global Customer Intelligence Practice at SAS, designing solutions enabling companies to manage customer engagement in a personalized and profitable manner. She earned a Bachelor of Liberal Arts with a concentration in Biology from Harvard University.