When Big Data Becomes More Valuable Than Your Products/Services

A recent global study across 1000 executives conducted by EMC and Capgemini reports the following: “64% of respondents said that big data is changing traditional business boundaries and enabling non-traditional providers to move into their industry, and over half (53%) expect to face increased competition from start-ups enabled by data.”

My take: Eventually any company expecting to compete effectively must become a software company, where data is the primary asset driving business strategy and revenue. Going a step further, by monetizing big data, companies are creating new revenue streams that will actually eclipse the value of a company’s existing products or services over time. This is supported by the EMC and Capgemini study as well: “Among our respondents, 63% consider that the monetization of data could eventually become as valuable to their organizations as their existing products and services.”


The question is how to find gold in the flood of data flowing in and out of the organization to compete effectively, especially against new digital startups. To answer this question, I spoke to Capgemini’s Global Vice President for Big Data Steve Jones who strongly believes the answer lies within the power of a business data lake.

1.  As an industry leader in big data, what is so exciting about a data lake solution?

I’d like to first define what we coined a ‘business’ data lake back in 2013, which is an architecture that supports big, fast, and managed data. Managed data is key since it is the data that is managed or governed in line with business objectives.

What’s exciting about a business data lake is that enables organizations to achieve a very important goal – uncovering new business models through big data analytics. Generating new revenue streams is the primary differentiator between a successful and unsuccessful business.

Because the data is governed in line with the business, organizations have more flexible control of the data to make it accessible for analysis across the entire organization. Business units are no longer constrained by traditional reporting platforms, but rather, can freely discover and distill the information they need to uncover insights. These traditional reporting platforms were sufficient for specific uses in the past, but now there is a new generation of data driven companies creating unprecedented competition. This is why companies need a fundamentally new architecture such as a business data lake to challenge the competition.

2.  Tell us why customers looking to transform their business with big data should work with Capgemini.

Through our experience with big, fast, and managed data, we were the first understand the changing landscape and create the ‘business data lake’ approach. We published the first reference architecture that not includes the technology components, but also the business factor or change that needs to occur to make the business data lake effective.

So not only do we have the experience, but we also have the vision that is needed to help organizations make a fundamental transformation in how they manage, execute, and derive business value from data. This is a multi-year transformation that requires you to work with an experienced partner that helps set the vision and closely works with you over time to achieve results. Again, this includes both the technology and business transformation that needs to occur across the organization.

3.  Why has Capgemini become a premier partner of the EMC Business Data Lake?

Back in 2012 we recognized that big data transformation wasn’t happening fast enough because our large customers were not achieving business results and needed more support. Unlike technology powerhouses such as Facebook, Yahoo, and Google, a typical customer is not equipped to custom engineer a big data solution to support their business needs.

We knew an industrialized approach was the answer to get our large customers transformed and compress the time to value. The EMC Business Data Lake is this approach, providing fully-engineered solution that is simple to deploy and operate. We also like its open partner ecosystem approach since it allows our customers to use products and tools they already have.

4.  What makes EMC Business Data Lake a successful, fully-engineered solution?

EMC recognized that a single company alone is unable to provide an industrialized solution. Instead, they utilized the Federation and developed a partner ecosystem to collaborate and engineer a business data lake solution to remove the headaches and cost of integration. Pre-configured building blocks, core Federation technologies and an open ecosystem allows a typical customer to quickly adopt and subsequently transform.


5.  What customers are the ideal candidates for the EMC Business Data Lake?

Any customer that doesn’t want to set up a specialized IT team just to handle data such as setting up and managing a Hadoop clusters since this requires huge budgets and resources. Also, an ideal customer is one that prioritizes business innovation over technology innovation, since this solution compresses the time to value through rapid deployment.

Innovating The Marketing Process With A Data Lake

Like many global companies, EMC depends heavily on a CRM to manage sales and purchasing data about its vast global installed base. Over time, we realized that without big data analytics, this customer data was trapped inside our systems and providing limited value.


EMC decided that offering analytical capabilities through a data lake architecture would substantially increase the value of this data. To get there, EMC hired Todd Forsythe, EMC Vice President, Corporate Marketing, to create the Marketing Science Lab. I spoke to Todd about why he is so excited about the impact of big data and the Marketing Science Lab on sales and marketing:

1. What is the Marketing Science Lab?

The EMC Marketing Science Lab funnels CRM data into EMC’s data lake solution for predictive modeling, segmentation analysis, and customer profiling. By marrying our internal data with external, unstructured data sources, such as social media conversations, we’re able to find individuals who are talking about progressive views on IT and business transformation.

Mix in analytics and we’re suddenly talking about targeted up-sell and cross-sell programs that drive far more engagement and sales than ever before.

With a 360 view of our customers using all of these digital breadcrumbs, our campaigns are getting to a higher level of personalization and precision targeting. Ultimately, we’re lowering acquisition costs and improving conversion rates—all music to the ears of anyone in marketing or sales.

2.  What were the key business drivers leading to the creation of the Marketing Science Lab?

As we’ve moved to a digital, social world, we found that highly predictive customer data often does not sit within the walls of our company. Instead, it resides in conversations across social media, community forums and so on. This has meant that the volume of data available to marketing has exploded. We needed a big data solution to store, analyze and visualize all of these social digital touch points with customers.

3.  What excites you most about the data lake and Marketing Science Lab?

Early in my career, I realized that data, especially customer data, was a magical lever that you can pull to improve marketing effectiveness and gain a richer understanding of your customers. Marrying modern social data with legacy data is exciting to marketing because it opens up a completely new way of looking at and interacting with our customers.

Traditionally, marketing organizations create a message, design a creative concept, identify the target market and use data to execute and measure. We now realize that big data should come first. It completely flips the marketing process upside down. Your customers’ behavior should drive your messaging, creative, and execution.

4.  What other organizational changes were necessary?

We’ve needed build up completely new skill sets in marketing. big data experts and PhD statisticians who can apply mathematical methodologies to large data sets have joined our team. We hired data scientists to manipulate and visualize large data sets. Having this layer of visualization is key so that marketers can easily consume and understand the findings and insight.

5.  What challenges did your team face with creating the Marketing Science Lab?

When we embarked on the Marketing Science Lab project, the biggest challenge was speed. We went from tumbleweeds to a full team with an operational big data solution in six months. Finding data scientist talent with experience in marketing also was hard to come by and took longer than we anticipated. And when marketing got wind of our new analytics capability, demand quickly outstripped our resources. So we needed to revamp our process to focus on the biggest priorities.

6.  How is EMC’s business benefitting from the Marketing Science Lab?

The results have been tremendous. When we apply models that identify customers with the highest propensity to buy in our marketing campaigns, we see response and conversion rates up to 10 times higher than before. Delivering more relevant offers to our customers improves sales productivity because better conversion rates means that the selling process is more effective. And it increases ROI because we’re spending less while creating greater yield.

7.  What are some of the best practices you would recommend to other organizations embarking on a big data project?

If your organization wants to tap into the power of big data, you first need to look closely at your end process and understand clearly how big data analytics would affect your process at every step. Second, determine how to scale insights to create the greatest impact. For example, as we build these predictive models, we embed them into our CRM environment in a way that makes it easy for marketers across the globe access segmentation and propensity to buy information and engage in data-driven decision-making.

Want To Build A Data Science Team? EMC Offers a Holistic Approach

Many of our customers invest in big data solutions to target their sales prospects better, explore advanced medical research, and make their internal processes more efficient. The biggest obstacle to getting these initiatives out of the gate is the shortage of big data skills within their own firms and across the industry.

To address this skills gap, EMC has developed a thorough data science and big data analytics curriculum for our customers. EMC was one of the first companies to offer data science education with rigorous, live instruction using free and open source tools. As of today, more than 10,000 customers, partners, and college students have attended the training.


I spoke with EMC’s David Dietrich, who leads this unique program to discuss his approach to data science education, which differs from more traditional product-oriented education. What I found most interesting is that in addition to David’s work at EMC, he has also helped design big data analytics curricula for Babson College and other universities.  More recently,  David has published a book, Data Science and Big Data Analytics, to help further develop data science skills and expertise in the industry.

1.  Why is EMC pushing so hard to educate and develop data scientists?

As an information company, we’re extremely attuned to the value of big data, which is exploding in both the sheer amount and how organizations in virtually every field and industry are using it to solve critical problems. When EMC acquired our first big data company, Greenplum, several years ago, we quickly became aware that there was a shortage of people who had the data science and business skills to help companies utilize big data.

2.  How is EMC taking a holistic approach to data science education?

We recognize that learning how to use big data technology alone does not ensure success. Senior management must make sure that appropriate people and processes are in place to drive the change and innovation necessary for valuable big data results to occur. To help companies on their journey, we offer courses for data scientists, who execute big data projects, and business executives who sponsor, run and manage them.

Our goal is to educate all levels of an organization so that data scientists and business people understand one another. That way, the organization is able to roll out big data projects with greater adoption and success. In addition to offering courses to our customers, we also work closely with universities and educational institutions to help them develop their own curriculum and programs.

3.  Please describe some of the important skills for aspiring data scientists.

Working in strategy and analytics for the past 20 years, I’ve always been drawn to experimenting with data to solve problems, which is exactly is the mindset you need to tackle big data. Companies often ask me how to go about using massive amounts of structured and unstructured data to solve business problems. How do they know what to choose and ignore? How do they know what algorithms to apply? Our courses encourage a culture of experimentation that leads to answering these questions. We teach our students how to test an idea with data, measure it quantitatively, learn from it and iterate. This test and learn mindset is critical to becoming a talented data scientist and data-driven organization.

4.  What are some of the challenges with evolving into a data-driven organization?

There can be a substantial divide between data scientists and business people who manage and work with them on big data projects. Many business people lack the technical background to understand how the algorithms apply to the problem and how to test ideas with data. And some data scientists may not understand the business context. We’re trying to educate each side so they can get a clearer picture and drive toward common goals. Once you bridge that gap, you can start driving real change, and solving old problems with big data or new information sources that were once unusable.

5.  What should companies expect after they have successfully made the leap to big data?

We’re educating them in how to train and staff a big data team, as well as build processes to be effective and successful. With this approach, companies can more effectively define the business problem, acquire the right data sets, experiment, communicate the results, and finally, operationalize the new processes.

EMC CIO Takes On Big Data Problems With Big Data Analytics

Every second of every day, IT generates enormous amounts of data around operational activity – system behavior, application performance, user actions, security activity, and more. Instead of viewing this data explosion as a Big Data problem, IT views it as opportunity to use Big Data solutions such as IT Operations Analytics to improve the quality of their services.


For example, 75% IT professionals surveyed recently said that they believe that IT Operations Analytics are able to transform data into relevant insights into actionable plans for improvement. I spoke with EMC CIO Vic Bhagat to describe how EMC is embracing Big Data for IT Operations Analytics to solve critical problems affecting EMC IT Operations and customers.

1.  What are the biggest problems faced by IT Operations Management at EMC and how were these problems addressed before the world of Big Data?

IT generates enormous amounts of data when monitoring complex, rapidly growing and changing IT infrastructures and the applications. The challenge for IT Operations Management is to leverage this data to build an adaptive system that is more proactive, and less reactive. The more the system can learn from the data, the better it can identify variances and problems areas in a timely manner to help IT fix issues before it negatively impacts the business such as downtime or poor performance.

In the past, we relied on traditional business intelligence and data warehousing systems to gain intelligence or insight based on historical trends. Now, with analytics, we can uncover important variables and modify them to predict an outcome. And, the more data we collect at a detailed level, the more accurate we can be.

2.  How does Big Data analytics change the game to address these problems more effectively?

It cuts down the time to gain insight. The most heavily used word after ‘selfie’ is now ‘data lake’. Everyone wants to build a data lake since it provides the right architecture and capabilities to cut down the cycle time in deriving newer, predictive insight, and then continuously integrating these results back into our business processes and decision-making. At EMC, we are moving away from data warehouses to a data lake architecture enabling us to not only gain faster insight, but also gain newer insight by bringing together and analyzing both structured and unstructured data.

For example, in a data warehouse you manage structured data such as part numbers, bay numbers, disk numbers, chassis numbers, and more. In a data lake you can manage all of this structured data in addition to unstructured data such as user manuals for each system and component. Let’s now apply this data lake solution to a use case – we continuously monitor the health of a customer’s infrastructure with our call home systems. We can now leverage a data lake with more data sets to not only make more accurate component failure predictions, but we can also provide the relevant information needed from user manuals to fix the problem in a timely manner so the customer experiences no downtime.

3.  What is EMC’s IT Operations Analytics solution leveraging Big Data technologies and techniques?

We are leveraging the entire Pivotal Big Data Suite to ingest and store all of the structured and unstructured data – Pivotal Gemfire XD, Pivotal HD, Pivotal HAWQ, and Pivotal Greenplum Database. Our Data Scientists are then able to apply advanced analytic techniques to the data they need using their choice of tools which are MadLib, R, and Python. This Big Data environment will be part of a wider business data lake strategy, where all enterprise data will be managed, accessed, and used equally by all business applications, not just IT Operations. Only a few legacy or specialized applications will standalone.

4. What benefits has EMC gained from this Big Data solution?

The benefits are enormous and can be extracted from both business and technical benefits. Building predictive models and predicting imminent system failure reduces downtime and the number of alerts and enables us to identify the real issues faster, reducing the cycle for decision making and taking corrective action. This improves our performance, productivity and value we gain from Big Data.

But we are only scratching the surface. The more we can optimize our Big Data environment so that it is elastic and accessible, the faster and more precise Data Scientists will be in solving problems. For example, we can now predict MS Exchange outages two hours in advance.

5. One of the biggest barriers to getting value from Big Data is the skills shortage. How does EMC IT Operations address this issue?

EMC had the foresight to build Centers of Excellence (COE) around the globe, producing the expertise and skills needed to transition into the realm of Data Science. We are fortunate to leverage talent within the company, but also leverage the COE to attract and acquire new Data Science talent outside the company.

6. What books are you currently reading on your Kindle or if you are still paper based like me, what books are stacked on your nightstand?

I’m Kindle based, so I read periodicals such as Techmeme and Engadget. Since we are a company that is data and digital driven, I am reading a book called ‘Leading Digital’. I want help lead this digital revolution at EMC and this book provides great examples of how digital makes significant changes in how a company operates and kills bureaucracy.