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Big Data Challenges and Opportunities

As the age of digital transformation marches on, even the smallest business has a rather large challenge facing it: dealing with Big Data. The days of trying to glean insights “when possible” from a local database application or strategic decision making based on often incomplete data are long gone. In many ways, the global economy has entered what might be called “the Big Data era,” and without an effective strategy for overcoming Big Data challenges (and leveraging opportunities it provides), organizations may be sacrificing not just invaluable business intelligence, but their ability to compete effectively.

Understanding how to mitigate or circumvent Big Data challenges, how to spot Big Data opportunities, and the best places to begin Big Data in your organization is essential to transforming the mountains of information at your fingertips into actionable insights and value.

Why Understanding Big Data Challenges and Opportunities Matters

Like a lot of popular business buzzwords, “Big Data” tends to be thrown around in a very casual way whenever the discussion turns to topics like data science, digital disruption, digital transformation, and business intelligence. Big Data technologies absolutely have an important part to play in contemporary strategic planning, but before you can start turning data into demonstrable value, you need to understand what Big Data actually is—and what it isn’t.

Big Data as we know it was originally defined in 2010 within the context of Apache Hadoop, a software framework designed for “distributed processing of large data sets across clusters of computers using simple programming models.” In a nutshell, Hadoop’s original goal was to take data sets so immense that single computers of the time literally could not analyze or process them in a reasonable span of time.

In its raw form, this “Big Data” was unstructured, immense, and, while high in potential value, remained relatively useless without analysis. As today, it ranged in size from gigabytes to terabytes to exabytes and zettabytes (for reference, an exabyte is roughly 285 million DVDs’ worth of data, while a zettabyte is equal to about 281 trillion songs in .MP3 format), and it was difficult to wrangle.

Solutions at the time included a technology data scientists call grid computing, a kind of proto-cloud that used hundreds or even thousands of computers to complete tasks a single computer would find difficult or impossible to complete, such as advanced calculations, large-scale data processing, or data mining.

Flash forward to the 2020s, where the Internet alone is approaching two zettabytes in size (it took nearly four decades for it to reach a single zettabyte, but Big Data’s unique properties include an exponential, rather than linear, growth scale). Grid computing is still in use, but the cloud—a series of distributed computers processing and storing data remotely while serving results to thin clients on PCs, mobile devices, etc.—is increasingly taking its place.

Why? Because Big Data operates on the “Six Vs:”

  • Volume: Storage capacities are growing faster than ever, and data that used to be sacrificed to the virtual ether is now just another stream of information flowing into the ocean of Big Data. In the future, when storage capacities reach truly staggering volumes, it may become possible to capture and record incredibly complex data, such as sensory perceptions or holographic projections of live events, in real-time.
  • Velocity: In an always-on, increasingly interconnected world where the digital sphere and the physical realm are both constantly generating massive amounts of data, Big Data grows larger and more complex with every passing moment. Just as the Internet’s data volume took less than ten years to reach double the amount it previously accumulated in 40, the ever-growing number of data streams from smartphones and other mobile devices, the Internet of Things (IoT), and advanced machine learning algorithms creating new data as they analyze existing information have created an ever-expanding sphere of information-rich in potential value for businesses.
  • Variety: Big Data comes from numerous sources. Beyond the data businesses generate simply in the course of doing business, companies now have access to new sources of unstructured, semi-structured, and structured data, including:
    • Social networks (e.g., Facebook posts, tweets, Instagram posts, etc.).
    • Sensor data from IoT devices.
    • Video and audio data from user-created content sites.
    • Specialized application data from different sources, such as health records, vendor compliance and performance, eCommerce performance date, etc.
    • Feeds from commercial and government resources.

Consequently, Big Data analytics will only grow in importance as we move forward.

  • Veracity: Big Data’s utility is limited by its accuracy and completeness. Inconsistent and incorrect data will yield questionable results due to errors, but veracity also applies to “soft” data coming from places like social media, where analysis of consumer behaviors to measure things like popular sentiment can be hit-or-miss even with advanced algorithms. Using Big Data effectively means having a firm grip on data that’s reliable, and optimizing substandard data where possible.
  • Variability: Along with its handmaiden, complexity, variability of Big Data refers to the inconsistency with which data may be available based on the need to collect, organize, manage, and analyze data streams from multiple sources to ensure they are high-quality and of maximum utility. As the number of data sources increases and the amount of data captured from each grows in both volume and complexity, it’s crucial that companies have a way to streamline data collection, management, and analysis to achieve and maintain competitive advantage.
  • (Low) Value Density: Like metal ore or sugarcane, Big Data must be refined to be useful. The challenge is to optimize the workflows used to analyze this data, and extract maximum insights and value for an optimal return on investment (ROI) as well as peak efficiency.

Companies of all sizes need to have a firm grasp of these six factors, and the challenges they create, when developing their data management strategy. Without such understanding, they may struggle to seize the opportunities buried within the ever-expanding mass.

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