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.”

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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.

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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.

Destination Data Lake: Accelerating the Big Data Journey

Most people understand that big data and analytics can have a positive impact on their business. What trips them up is how to make that happen. EMC’s answer to that complex challenge is the EMC Business Data Lake, the industry’s first fully engineered, enterprise-grade data lake that’s redefining big data.  For details, check out the virtual launch event.

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I spoke with Aidan O’Brien, Senior Director of EMC’s Strategic Big Data Initiative, and asked him why he’s excited about EMC Business Data Lake and why it sets precedence in the world of big data analytics.

1.  What are extraordinary outcomes companies may achieve with big data analytics?

There are many well-understood examples already. A great one is Rolls Royce. Instead of selling their high-end jet engines, they practically give them away and sell an associated multi-year data-driven service contract.

The service contract helps customers minimize downtime if there’s a problem with components in the engine. Each component has sensors that capture performance and health data along with the aircraft’s coordinates. That’s all fed in real time to an analytics system at the company’s global service center. If any component performance abnormality is detected, the system kicks off an automated logistics process that ensures that the replacement part is shipped to the right gate even before the plane lands at its destination.

This returns planes to the air faster so airlines can get back to making money. In the meantime, Rolls Royce has found an innovative way to strengthen their customer relationships.

What’s extraordinary about this, and many other similar examples, is that business value has so clearly shifted from the inanimate objects that customers produce to the data about that product.

2.  You speak to customers often about the power of big data analytics, but what are their key challenges in embracing big data?

The challenges vary based on an organization’s maturity level with big data. While every customer’s big data journey is unique for various business and technology reasons, we generally group companies into one of three buckets.

First are companies in the exploratory phase. They’ve heard about big data and are trying to work out what it is, how it’s different from business intelligence, what skills they need, and so on. The big challenge for these folks is figuring out how to identify the right opportunity to get started.

Some companies are a little farther along and have big data projects springing up all over the place. Their prime challenge is how to show meaningful value to the business from their various initiatives.

Then there are companies achieving big results with big data. Their challenges relate to making the necessary changes across people, process, data, and technology so that transformation and improved business performance stick.

3.  Why do you think EMC’s approach is appropriate for these companies?

What excites me about our approach is that we have an engineered solution and a range of services offerings that can help companies address the challenges at each phase of their big data journey.

For example, when first starting out on a big data journey, companies usually want to understand exactly what is possible and then identify and focus on key use cases. That’s what EMC’s Big Data Vision Workshop is all about. It gets IT and business stakeholders on the same page so they can prioritize use cases that are feasible and expected to deliver meaningful outcomes for their business.

Companies trying to get their hands dirty and build skills in data science, machine learning and rapid application development can use our Proof of Value Service. This helps them deploy a small but viable analytics project to demonstrate ROI for a target use case.

And for more mature companies struggling to manage and scale their big data infrastructure, we offer EMC’s Technology Onboarding Service. This includes consulting and deployment services to move them quickly to the EMC Business Data Lake.

We also see a number of more mature companies already knowing the business application they want.  For these customers, we look to engage them via the Pivotal Labs group. That engagement also tends to lead to the implementation of the underpinning EMC Business Data Lake.

4.  How does the Federation Business Data Lake accelerate adoption of big data analytics to achieve these kinds of results?

EMC Business Data Lake enables more people to benefit from big data quickly and effectively. We see customers struggling for weeks and months to instantiate these complex environments.

The EMC Business Data Lake is a platform that delivers greater standardization to help people stand up them up more quickly. Yet it also provides flexibility by letting people select the different technology products they need to deliver on a particular use case. As much as we’re seeking to make the job of the IT operator easier, the ultimate goal is to provide a self-service big data environment for the wide variety of people involved in big data, including data scientists, application developers, and line-of-business analysts.

5.  What makes the EMC Business Data Lake unique?

Clearly, being the first fully engineered, enterprise grade business data lake in the industry is important, as is its ability to bring together data, analytics and applications. To me, what makes the EMC Business Data Lake stand out the most is the way it combines our top Federation technologies with the ecosystem of third-party products.

Because it’s built on a platform that embraces third-party technologies, new products can be easily embedded into the platform and made available to developers or data scientists almost immediately. Being able to evolve big data analytics environments over time as technology changes is critical. Traditional, physical infrastructures simply aren’t agile enough to keep up with that pace of technology change.

The prospect of EMC and the Federation being able to keep up to date with the rapid change in the big data market is why I’m so excited about the EMC Business Data Lake.

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.

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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.

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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.