Three big benefits of big data analytics

After interviewing more than 50 companies, Tom Davenport reveals the true value of big data at work

By Tom Davenport, IIA Director of Research and faculty leader

Google, eBay and LinkedIn were among the first to experiment with big data. They developed proof of concept and small-scale projects to learn if their analytical models could be improved with new data sources. In many cases, the results of these experiments were positive.

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Tom Davenport is an analytics industry pioneer and serves as the IIA Director of Research and faculty leader. He is a Visiting Professor at Harvard Business School, a distinguished professor at Babson College, a research fellow of the MIT Center for Digital Business, and a senior adviser to Deloitte Analytics.

Today, big data analytics is no longer just an experimental tool. Many companies have begun to achieve
real results with the approach, and are expanding their efforts to encompass more data and models. For a SAS-sponsored project called “Big Data in Big Companies” and my new book Big Data at Work, I interviewed more than 50 companies that were using big data analytics. Here’s how they’re getting value:

1. Cost reduction

Big data technologies like Hadoop and cloud-based analytics can provide substantial cost advantages. While comparisons between big data technology and traditional architectures (data warehouses and marts in particular) are difficult because of differences in functionality, a price comparison alone can suggest order-of-magnitude improvements.

Virtually every large company I interviewed, however, is employing big data technologies not to replace existing architectures, but to augment them. Rather than processing and storing vast quantities of new data in a data warehouse, for example, companies are using Hadoop clusters for that purpose, and moving data to enterprise warehouses as needed for production analytical applications.

Well-established firms like Citi, Wells Fargo and USAA all have substantial Hadoop projects underway that exist alongside existing storage and processing capabilities for analytics. While the long-term role of these technologies in an enterprise architecture is unclear, it’s likely that they will play a permanent and important role in helping companies manage big data.

2. Faster, better decision making

Analytics has always involved attempts to improve decision making, and big data doesn’t change that. Large organizations are seeking both faster and better decisions with big data, and they’re finding them. Driven by the speed of Hadoop and in-memory analytics, several companies I researched were focused on speeding up existing decisions.

For example, Caesars, a leading gaming company that has long embraced analytics, is now embracing big data analytics for faster decisions. The company has data about its customers from its Total Rewards loyalty program, web clickstreams, and real-time play in slot machines. It has traditionally used all those data sources to understand customers, but it has been difficult to integrate and act on them in real time, while the customer is still playing at a slot machine or in the resort.

Caesars has found that if a new customer to its loyalty program has a run of bad luck at the slots, it’s likely that customer will never come back. But if it can present, say, a free meal coupon to that customer while he’s still at the slot machine, he is much more likely to return to the casino later. The key, however, is to do the necessary analysis in real time and present the offer before the customer turns away in disgust with his luck and the machines at which he’s been playing.

In pursuit of this objective, Caesars has acquired Hadoop clusters and commercial analytics software. It has also added some data scientists to its analytics group.

Some firms are more focused on making better decisions analyzing new sources of data. For example, health insurance giant United Healthcare is using “natural language processing” tools from SAS to better understand customer satisfaction and when to intervene to improve it. It starts by converting records of customer voice calls to its call center into text and searching for indications that the customer is dissatisfied. The company has already found that the text analysis improves its predictive capability for customer attrition models.

3. New products and services

Perhaps the most interesting use of big data analytics is to create new products and services for customers. Online companies have done this for a decade or so, but now predominantly offline firms are doing it too. GE, for example, has made a major investment in new service models for its industrial products using big data analytics.

Verizon Wireless is also pursuing new offerings based on its extensive mobile device data. In a business unit called Precision Market Insights, Verizon is selling information about how often mobile phone users are in certain locations, their activities and backgrounds. Customers thus far have included malls, stadium owners and billboard firms.

For the Phoenix Suns, an NBA basketball team, Verizon’s Precision Market Insights offered information on where people attending the team’s games live, what percentage of game attendees are from out of town, and how often game attendees combine a basketball game with a baseball spring training game or a visit to a fast food chain. Such insights are obviously valuable to the Suns in targeting advertising and promotions.

Ready for prime time

These examples make clear that big data analytics projects are delivering value. There are, of course, still some issues to be worked out with regard to how big data capabilities will evolve, but the time for questioning big data’s business value has passed. These companies and many more have already shown that they can analyze big data successfully to achieve cost reductions, faster and better decisions, and even new offerings for customers. It’s clear that the big data era will be one of dramatic business opportunity – don’t wait too long to exploit its potential.

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Praise for Big Data @ Work

The latest book from Tom Davenport, best-selling co-author of Competing on Analytics, is creating big buzz:

Big Data at Work is the first and only book to describe how real organizations are using big data, extracting value from it, and combining it with other forms of data and analytics. It’s an invaluable guide to planning and action.
Jane Griffin, Managing Director of Analytics, Deloitte Canada and Americas

Is big data a buzzword, or does it have practical applications in business? Big Data at Work goes beyond tech talk to help business people turn big data into big decisions.
Jonathan D. Becher, Chief Marketing Officer, SAP

Big Data at Work is conversational, engaging and an exceptional guide for decision making in the big data world.
Adele Sweetwood, Vice President, Americas Marketing and Support, SAS

 


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