A quick primer on how organizations are starting to adopt analytics to serve customers and increases sales, with some important caveats.
Judy Selby, Law Technology News
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Data breaches and other security concerns; skyrocketing costs related to electronic data discovery and data storage; legal and regulatory compliance headaches.The downside of--and risks associated with--big data are well documented. But more and more organiations are beginning to recognize and exploit the immense benefits that Big Data and Big Analytics can provide in a variety of contexts. To understand this phenomenon, it's key to first understand Big Data and how Big Analytics can be used.
Many people think that Big Data refers simply to huge volumes of data. Although data volumes are undeniably massive--estimated by McKinsey Global Institute to grow at a rate 40 percent per year--volume is just one characteristic of Big Data.The others are:
* Velocity: Social media data streams, for example, very quickly produce staggering amounts of data.
* Variety: New data types in non-traditional formats are much less static than traditional data forms.
* Value: The challenge is to identify the value within the data and formulate a way to access and analyze it.
Big Analytics involves extracting data points from Big Data and analyzing it for valuable business information. Gerard Britton, CEO of Topiary Discovery, says that although everyone talks about Big Data, “In reality, Big Analytics is what matters.”
Big Data and Big Analytics can be used for a wide array of purposes, including increasing sales, improving competitive intelligence and managing workforces, by all sorts of entities. McKinsey reports that retailers adopting Big Data can increase operating margins by more than 60 percent. During the past holiday season, for example, Macy’s and J.C. Penney partnered with a shopping app providerto reward customers with discounts, or song downloads, for trying on clothes and making purchases. Best Buy and Kohl’s both used location-based promotions to push notifications to near-by or in-store customers.
In the travel industry, firms are using Big Data to develop new products and services--such as ratings for airline flights and hotels--to improve customer satisfaction and decrease vacancy rates. Pandora, a streaming radio service, uses data generated from its customers’ listening patterns to generate highly-targeted advertising. Other businesses are using a fraud triangle data analysis model, which focuses on incentive or pressure, opportunity and rationalization, to examine huge amounts of structured and unstructured data to identify internal fraud and corruption.
Sports and movie fans are well aware of how the Oakland Athletics baseball team studies Big Data to field competitive teams without a mega-payroll. The National Basketball Association also draws on Big Data to provide real-time statistics on even the most subtle of player characteristics, to help coaches make better game-time decisions and to enhance television broadcasts. Pharma companies, which have successfully used Big Data for marketing purposes for years, are now hopeful that it will pay big dividends in the area of research and development by permitting analysis of vast quantities of data across multiple modalities.
In December 2013, a Midwestern gas station chain used data collected from customer cell phones to increase sales by more than 8,000 gallons. The Veterans Administration examines hundreds of data points to identify and intervene with patients at the highest risk of hospitalization and death. And although most people are aware of Amazon’s use of Big Data to increase sales by suggesting targeted products to customers based on their buying history and patterns, Amazon also has been credited with using data to provide superior customer service. Big Data also is responsible for the shift from intuition-based to data-driven decision making by some human resources recruiters and other corporate decisions-makers.
What Are The Challenges?
It’s clear that there can be a wealth of valuable information in Big Data, but many enterprises don’t know how to access it. Big Data is largely raw, semi- or unstructured and in various nonrelational formats, which can’t be analyzed using traditional tools and platforms. In order to fully exploit its benefits, Big Data platforms, such as Apache Hadoop and analytics platforms such as SAS Enterprise Miner and IBM SPSS Modeler, must be used so that data in all formats, including text, sensor data and other media sources--as well as structured data--can be accessed and analyzed together. And, this analysis must be done quickly in order to gain a competitive edge. The goal is to extract from unstructured Big Data discreet and structured data points to which Big Analytics can be applied.
As noted above, use of analytics can yield game-changing results in a wide range of situations. Britton notes that “Big Analytics have been particularly useful in conducting ‘sentiment analyses’ to determine customer satisfaction or predict customer churn using diverse Big Data sources such as social media, voice data and email Fraud investigations, which require the ability to switch from structured transactional data to social media and other unstructured data, also are well suited to the use of Big Analytics.”
Britton recommends that organizations take an enterprise-wide approach to Big Data analysis, ideally with buy-in from the C suite “Siloed, ad hoc projects in Big Data analysis are among those most likely associated with waste or failure. A high-level corporate commitment is ideal to set a structure for the implementation of Big Analytics and to keep everyone on the same page and working towards holistic goals.”
Big Data also means big concerns about privacy. Effectively using Big Data to better tailor services to individual consumers, for example, requires access to each consumer’s personal information. Headline-making news about data breaches and cybercrime highlight what can go wrong for entities that collect mass quantities of confidential information. In addition to focusing on cyber security, enterprises also need to appropriately apprise their customers as to how they intend to use their confidential information.
Even taking the challenges into account, organizations that adopt the right approach to Big Data and analytics can achieve results that were inconceivable a few short years ago. As sources and volumes of data continue to grow, it makes sense to consider how Big Data and Analytics can be used to improve decision-making and achieve enterprise-wide goals.
Judy Selby is a partner at Baker & Hostetler, based in New York. Twitter: @judy_selby.
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