Making Big Data Smarter: How to Move from Raw Data to Real Value at Scale

Posted by Anatoli Olkhovets on Wed, Jul 06, 2016

In a previous post, “5 Obstacles to Achieving Scalable Data Science, and How to Overcome Them,” we talked about perspectives distilled from hundreds of conversations with our customers and partners and the challenges they face in trying to achieve a scalable data science capability. All of these customers have an extensive backlog of ideas, but they struggle to convert these ideas into actual use cases, or mini-applications, that can run in a production environment and generate real business value. These businesses universally encounter the following key obstacles:

(1) They have too many tools and technologies to manage effectively.

(2) Data is everywhere, but deriving value from it is extremely difficult.

(3) The traditional “artisan” approach to use cases severely limits the number of business problems they can solve.

(4) Operationalizing data science, with hundreds of models in production, is extremely difficult.

(5) Companies are willing to experiment but are afraid to make the long-term commitment necessary to foster widespread adoption.

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Topics: Big Data, Signal Hub Technologies

Big Data Analytics: Necessary but Not Sufficient

Posted by John Mack on Thu, Jun 30, 2016

Part 2 of 4: Grow Revenue from Your Existing Customers: How Big Data Analytics Can Help

This post is the second in a four-part series. The first installment, “Go Beyond the Symptoms: How to Overcome Revenue Growth Challenges,” discussed the key signs of slowing growth and the first steps organizations can take to turn it around. Here, we’ll discuss three new ways in which companies can use Big Data analytics to improve a business’ ability to consistently improve revenue growth from their existing customers.

Big Data analytics in 2016 occupies roughly the same spot in the corporate consciousness as did the concept of cloud computing in 2008. By now, every world-class company that generates vast quantities of data has recognized that this data has exceptionally high value as an asset. These companies have made technology investments accordingly, procuring software solutions to organize, analyze, and manage the data, storage solutions (cloud or on-premise) to facilitate access to and distribution of the data, and often also professional services to enable and operate this infrastructure.

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Topics: Big Data, Signal Hub Technologies, Analytics

5 Obstacles to Achieving Scalable Data Science, and How to Overcome Them

Posted by Anatoli Olkhovets on Wed, Jun 22, 2016

The struggle is real — and it’s becoming increasingly apparent to companies that have dipped their toes into popular data science tools. As enterprises test the limits of their new tools, old technology, and data scientists’ time, their infrastructure is starting to show its cracks. Read on to see how these issues are revealing themselves — and more importantly — gather some ideas on what to do about it.

Over the past year, I have been averaging 2–3 customer meetings per week, resulting in over 100 customer and partner conversations around Big Data, analytics, and data science for the enterprise. From these conversations, I have found one key recurring theme: scale. Large enterprises no longer want to build one model quickly or implement just one use case in production. They all struggle with a large backlog of ideas. They need a way to rapidly turn these many ideas into real use cases that deliver tangible business value.

However, many companies simply can’t find a pathway to make this happen. Across my numerous conversations, I noticed very similar patterns and identified 5 common obstacles that can prevent companies from achieving scale for data science.

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Topics: Big Data, Data Science, Signal Hub Technologies, Machine Learning, Hadoop, Analytics, Spark

Grow Revenue by Using Big Data More Intelligently

Posted by John Mack on Fri, Jun 17, 2016

Go Beyond the Symptoms: How to Overcome Revenue Growth Challenges

Part 1 of 4: Grow Revenue from Your Existing Customers: How Big Data Analytics Can Help

This post is the first in a four-part series. Here, we discuss the key signs of slowing revenue growth and the first steps organizations can take to turn it around.

It isn't hard to Identify the symptoms of declining revenue growth. Often, more than one of the classic signs will be evident at any given time. A declining growth rate or a shortfall in revenue vs. the target level are perhaps the most obvious indicators. Operating expenses growing faster than revenue can be yet another strong harbinger of revenue growth challenges, as well as a potential indicator of a cost structure that is no longer aligned with the business’ sales capabilities. While they require more research and calculation to derive, declining market share or shrinking revenue growth rates vs. competitors are other telltale signs.

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Topics: Big Data, Signal Hub Technologies, Marketing

5 Key Big Data Trends to Watch

Posted by John Mack on Tue, Feb 23, 2016

As a company that works intensively with Fortune 500 companies, our finger is firmly on the pulse of the latest needs, wants, and aspirations of the world’s biggest Big Data drivers. Here are five key trends we’re seeing for 2016 and beyond.

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Topics: Big Data, Machine Learning

The Key to Making Big Data Valuable: Make It Personal

Posted by Laks Srinivasan on Mon, Oct 26, 2015

Most companies realize they are sitting on a treasure trove of customer data that has the potential to deliver tremendous business benefits; however, most also have no idea how to realize those benefits. How can companies use their data to bring in more customers, increase the amount they spend, and make them more loyal? How can companies use data to turn unhappy customers into loyal champions of the brand? And perhaps most important, how can companies use that data to drive a significant increase in revenue?  

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Topics: Big Data, Data Science, Marketing

How Natural Language Processing Can Improve Spend Analytics

Posted by Sushil Sharma on Wed, Aug 19, 2015

Have you ever wondered how Google or Hotmail finds and blocks a single spam email out of billions of emails? Or how companies analyze tweets for customer sentiment? Or how questionable content is identified on a Website? Natural language processing (NLP) does this in real time — and it can be used for a lot more than spam filtering.

When it comes to analyzing a company's spend, the old adage "what gets measured gets managed" is definitely true. However, measuring an enterprise's spend when you have free-text fields, or fields where employees can type in any response they want, can be an insurmountable task. Rarely do two people use the same words and phrases to describe the same thing. Even spelling varies. These variations make measuring how much is being spent on a given category or with a given vendor a very common challenge.

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Topics: Big Data, Supply Chain & Operations

The Growing Government Open Data Movement

Posted by Daniel D. Gutierrez on Mon, Jul 07, 2014

It was 7:00 a.m. on a Saturday morning, and the 10th floor of Los Angeles City Hall was filled with more than 450 people gathered to spend their day off of work with their noses buried in their laptops. They were data scientists, and they had come to innovate new technologies to solve complex social problems using the city’s newly open data.

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Topics: Big Data, Data Science

My Struggle With Marketing Automation

Posted by Todd Higginson on Mon, Jun 30, 2014

Marketing automation's time has finally come, but it's been a long, tortuous road.

I’m a big fan of marketing automation. My experience goes back a few years. I was just getting settled at a new company when our CFO received a renewal invoice from a marketing automation vendor. She didn’t know how we were using the software, and neither did I. 

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Topics: Big Data, Machine Learning, Marketing

Data Science As the Panacea for Healthcare Fraud, Waste, and Abuse

Posted by Daniel D. Gutierrez on Thu, Jun 12, 2014

Edited by Yan Zhang

Healthcare fraud, waste, and abuse (FWA) are national problems that affect all of us either directly or indirectly. National estimates project that hundreds of billions of dollars are lost to healthcare FWA on an annual basis. These losses lead to increased healthcare costs and subsequently increased insurance premiums. 

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Topics: Healthcare, Big Data, Data Science, Machine Learning