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Realizing The Potential of Data Monetization…Do I Have Your Attention Now?

Bill Schmarzo

Bill Schmarzo

Chief Technology Officer, Global Services Big Data Practice

The importance of data has changed. As the volume, variety and velocity of the data grew over the past few years, the data has been transformed to provide organizations a broader, more granular and more real-time range of customer, product, operational and market interactions. Today, business leaders see data as a monetization opportunity, and their organizations are embracing data and analytics as the intellectual capital of the modern organization.

The Internet of Things is accelerating this drive towards “data monetization.”  However organizations are quickly learning that you don’t necessary monetize the data as much as you monetize the customer, product, and operational insights derived from the data to create new revenue opportunities: new products, new services, new channels, new markets and new partnerships (see Figure 1).

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What’s Driving the Data Lake?

Bill Schmarzo

Bill Schmarzo

Chief Technology Officer, Global Services Big Data Practice

EMC’s Federation Business Data Lake (FBDL) announcement has been a long time in the making.  It’s been a perfect storm of industry trends that enable big data and make data lakes a feasible data architecture option.  These trends include:

Data Growth – Web applications, social media, mobile apps, sensors, scanners, wearable computing and the Internet of Things are all generating an avalanche of new, more granular data about customers, channels, products and operations that can now be captured, integrated, mined and acted upon.

Cheap Storage – The cost of storage is plummeting, which enables organizations to think differently about data. Leading organizations are transitioning from viewing data as a cost to be minimized to valuing it as an asset to be hoarded. Even if they don’t yet know how they will use that data, they are transitioning to a “data abundance” mentality.

Limitless Computing – The ability to bring to bear an almost limitless amount of computing power to any business problem allows organizations to process, enrich and analyze this growing wealth of data, uncovering actionable insights about their customers and their business operations. (more…)

Data Lake: Platform for Business Transformation

Bill Schmarzo

Bill Schmarzo

Chief Technology Officer, Global Services Big Data Practice

When we engage with clients to help them identify where and how to leverage big data for business value, we frequently use the Big Data Business Model Maturity Index (BDBM). This helps organizations understand how effective they are at leveraging data and analytics to power their value creation processes.

Big Data Business Model Maturity Index

Big Data Business Model Maturity Index

Applying the BDBM can help an organization identify how it should enact changes to people, processes, and technologies to enable the creation of analytic insight that drives its top-level strategic initiatives.  Organizations that adopt this approach can utilize advanced analytics to couple new sources of customer, product and operational data, optimizing key business processes and uncovering new monetization opportunities.

However from an IT perspective, what does this look like?  The traditional data warehouse just can’t support these new data and analytic capabilities. (more…)

Business Intelligence Analyst or Data Scientist? What’s the Difference?

Bill Schmarzo

Bill Schmarzo

Chief Technology Officer, Global Services Big Data Practice

Business AnalystI am a huge Thomas Davenport fan. His book “Competing on Analytics: The New Science of Winning” was the first book to make organizations aware of the business potential of analytics, even prior to the craziness brought on by Big Data. I happened upon a recent article of his titled “Looking Outward with Big Data: A Q&A with Tom Davenport” and one item from that article really jumped out at me:

“Initially, I didn’t see much of a distinction [between business analytics and big data], and I thought that I could kind of rest on my laurels and not write a book about big data—because the fact is that the analytical tools and approaches used are not all that different for big data. But when I started talking to companies and data scientists, I realized that there really were some fairly substantial differences—some that have yet to be fully articulated and some that are already in evidence.”

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