Extracting Big Value from Big Data

Greg Reeder |  February 2021

When we picture the universe and all its planets, moons and solar systems, we attribute its infinite expanse to the Big Bang, an origin story for the universe as we know it.

And now, we also have a digital universe, which exists in a series of code and numbers. While we’re not talking about the same extraterrestrial proportions as the Big Bang, “big data” is similarly expanding our digital universe like we never could have imagined.

Big data sets are so large that specialized software is needed to manage them. It takes time and effort to prepare data for analysis and verify the results. Good data preparation and careful analysis leads to information that is useful and trustworthy. Trust is at the center of the big data universe.

The federal government is no stranger to big data. The Social Security Administration manages massive amounts of retirement data and disability claims. The Securities and Exchange Commission manages financial report information from public companies across the U.S. NASA’s missions to outer space require big data, as does cancer research and national security. Even the American experience is chronicled in huge data sets by the Library of Congress.

As you might guess, there are many challenges to getting value from big data. The main challenges are volume, velocity and variability, also called “the three Vs.”

Volume, Velocity, and Variability

First, volume. As mentioned, agencies have heaps of data. It’s spread across numerous systems, even including social media, which accounts for an enormous amount of data by itself, and is growing at an exponential rate. As the sheer amount of data widens, the resources to support it must keep pace.

That leads to the issue of velocity. Technical advances have enabled data production and collection at the speed of the internet. That means lots of data materializes quickly, but then it has to be ingested, processed and stored – and eventually retrieved, parsed and analyzed. It’s easy for human and technology resources to fall behind when data is pouring in like water from a firehose.

Then there is variability. Data comes in all types of formats from different sources. Some may be numeric data in ordinary databases, like SQL or relational databases, or other structured data like documents, emails and financial transactions.

There are also data lakes that store large sets of raw, unstructured data. Think of structured data like bottled water – it’s packaged and ready for use. Compare that to unstructured data, which is more like a secluded body of water that is mostly untouched. Getting back to data sources, they all require different types of processing.

Volume analytics on a cloud platform is a solution that addresses big data pain points. The cloud provides easy access and does the heavy lifting as far as infrastructure is concerned. Volume analytics is a pipeline structure that ties all of the parts of a data processing engine to a data catalog, so you can trust your data, discover where it came from and learn how it was processed.

SAP NS2 solutions resolve issues by enabling the agency to integrate data from multiple sources with less manual effort. It leverages cloud-based dynamics for efficient processing, while ensuring consistency across the enterprise. With errors minimized and improved data quality, agencies can trust their data for decision-making.

The beauty of big data is the value of information that results from mining, extraction and careful analysis. As the volume, velocity and variability of your agency’s data stretch further and faster, a cloud volume analytics service keeps the world of data firmly in your hands.

This article is an excerpt from GovLoop Academy’s recent course, “Managing the Volume, Velocity and Variability of Data,” created in partnership with GovLoop. Access the full course here.


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