This article is to help you get more clarity about what is Big Data, a key technology within the Digital Transformation era.
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What Does Big Data Mean
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy.
So, What is Big Data
Big data is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis. The purpose of Big Data is to enable enhanced insight, decision making, and process automation.
Big Data includes both, traditional data and digital data. Traditional data are derived from product transaction information, financial records and interaction channels, such as the call center and point-of-sale. All of that is big data, too, even though it may be dwarfed by the volume of digital data that’s now growing at an exponential rate.
Once we start tackling big data, we learn what we don’t know, and we get inspired to take steps to resolve any problems. Best of all, we can use the insights we gather at each step along the way to start improving our customer engagement strategies and immediately add more value to both your offline and online interactions.
Unstructured data vs Multi-structured data
In defining big data, it’s also important to understand the mix of unstructured and multi-structured data that comprises the volume of information.
Unstructured data comes from information that is not organized or easily interpreted by traditional databases or data models, and typically, it’s text-heavy. Metadata, Twitter tweets, and other social media posts are good examples of unstructured data.
Multi-structured data refers to a variety of data formats and types and can be derived from interactions between people and machines, such as web applications or social networks. A great example is web log data, which includes a combination of text and visual images along with structured data like form or transactional information. As digital disruption transforms communication and interaction channels—and enhance the customer experience across devices, web properties, face-to-face interactions and social platforms—multi-structured data will continue to evolve.
The 7 Vs of Big Data
Gartner (the global IT research firm) defines Big Data with the terms of “volume” (the amount of data), “velocity” (the speed of information generated and flowing into the enterprise) and “variety” (the kind of data available) to begin to frame the big data discussion. Others have focused on additional V’s, such as big data’s “veracity” and “value.”
“Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” —Gartner
Big data may be defined with the seven Vs: Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value.
Volume
Volume is how much data we have – what used to be measured in Gigabytes is now measured in Zettabytes (ZB) or even Yottabytes (YB). The IoT (Internet of Things) is creating exponential growth in data. This infographic from CSC does a great job showing how much the volume of data is projected to change in the coming years.
Velocity
Velocity is the speed in which data is accessible. I remember the days of nightly batches, now if it’s not real-time it’s usually not fast enough.
Variety
Variety describes one of the biggest challenges of big data. It can be unstructured and it can include so many different types of data from XML to video to SMS. Organizing the data in a meaningful way is no simple task, especially when the data itself changes rapidly.
Variability
Variability is different from variety. A coffee shop may offer 6 different blends of coffee, but if you get the same blend every day and it tastes different every day, that is variability. The same is true of data, if the meaning is constantly changing it can have a huge impact on your data homogenization.
Veracity
Veracity is all about making sure the data is accurate, which requires processes to keep the bad data from accumulating in your systems. The simplest example is contacts that enter your marketing automation system with false names and inaccurate contact information. How many times have you seen Mickey Mouse in your database? It’s the classic “garbage in, garbage out” challenge.
Visualization
Visualization is critical in today’s world. Using charts and graphs to visualize large amounts of complex data is much more effective in conveying meaning than spreadsheets and reports chock-full of numbers and formulas.
Value
Value is the end game. After addressing volume, velocity, variety, variability, veracity, and visualization – which takes a lot of time, effort and resources – you want to be sure your organization is getting value from the data.
Are you ready for Big Data? Are you thinking in implementing Big Data in your organization? Are you thinking on building a career on Big Data?
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