Leveraging Big Data in the Modern Tax Function
by Terri Eyden on
By Stephanie Maxwell, David Steiner, and Scott Stein
Technology and the modern tax function go hand in hand. The complexity generated by increased globalization as well as a wealth of new regulatory and compliance requirements can only be managed effectively through the use of solutions geared toward the myriad calculations, reports, and analyses demanded of tax departments. The amount of data that is generated on a daily basis has swelled to staggering proportions.
Organizations that hope to remain competitive must be prepared to not only manage but capitalize on this windfall of information. Emphasis on data-driven decision making must be an enterprise-level undertaking, but such a transformation will take time and may be best accomplished in stages. This article will focus on the advent of big data, as well as solutions and tools that may be leveraged by best-in-class tax departments to make the transition to an efficient and effective member of the digital universe.
The Era of Big Data
One of the biggest challenges facing organizations today is how to handle the huge amounts of data that are being generated, including new and increasingly complex data types. Walmart handles more than a million customer transactions every hour, feeding databases estimated at more than 2.5 petabytes. Facebook is home to forty billion photos and their corresponding metadata. And decoding the human genome involves analyzing three billion base pairs, which took ten years the first time it was done in 2003 but can now be achieved in one week.
These are examples of a phenomenon that has been termed "big data." According to the Gartner IT Glossary, "Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making."
Volume is typically measured relative to the capacity of the existing available resources for storage and management of the data. The number of devices and applications capable of capturing digital data is growing daily, which means that the volume of available data will continue to expand.
In addition, the types of data being captured through social media, websites, and e-mails contribute to an ever-increasing variety of information available to organizations. Of particular relevance to tax departments, for example, is the potential to leverage information captured in e-mails and paper documents to comply with FATCA's US indicia requirements.
There are two basic categories for this data – structured and unstructured. Structured data refers to the more traditional data types that can easily be read or managed by existing technology, such as relational databases. Unstructured data is less formal and may be textual, such as e-mails, documents, or instant messages, or it may be generated by non-textual sources, such as images or audio and video files and the corresponding metadata, or "data about data." With so many sources, the volatility or frequency of the data produced by organizations is growing by leaps and bounds. However, the crux of the challenges and opportunities presented by big data is value: finding the proverbial needle in the haystack. Identifying and using the meaningful data among the mass of information flowing into an organization requires not only deep analytical skills, but tools designed for just this purpose. The difficulty of this task is increased when data is buried in disparate or obsolete systems built for other purposes.
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