Big Data

The Big Data Storymap

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) January 28, 2013

I wanted to share some recent work that we have been doing inside EMC Global Services, to create a “Big Data Storymap” that would help clients understand the big data journey in a pictorial format.

The goal of a storymap is to provide a graphical visualization that uses metaphors and themes to educate our clients about the key components of a successful big data strategy.  And like any good map, there are important “landmarks” that I want to make sure you visit.

Landmark #1:  Explosive Market Dynamics

Market dynamics are changing due to big data.  Data, like water, is powerful.  Massive volumes of structured and unstructured data, wide variety of internal and external data, and high-velocity data can either power organizational change and business innovation, or it can swamp the unprepared.  Organizations that don’t adapt to big data risk:

  • Profit and margin declines
  • Market share losses
  • Competitors innovating faster
  • Missed business opportunities

On the other hand, organizations that aggressively integrate big data thinking and capabilities will be able to:

  • Mine social and mobile data to uncover customers’ interests, passions, associations, and affiliations
  • Exploit machine data for predictive maintenance and operational optimization
  • Leverage behavioral insights to create more a compelling user experience
  • Integrate new big data innovations to modernize data warehouse and business intelligence environments (real-time, predictive)
  • Become a data-driven culture
  • Nurture and invest in data assets
  • Cultivate analytic models and insights as intellectual property

Landmark #2:  Business And IT Challenges

Big Data enables business transformation, moving from a “rearview mirror” view of the business using a subset of the data in batch to monitor business performance, to the predictive enterprise that leverages all available data in real-time to optimize business performance.  However, organizations face significant challenges in leveraging big data to transform their businesses, including:

  • Rigid architectures that impede exploiting immediate business opportunities
  • Retrospective reporting that doesn’t guide business decisions
  • Social, mobile, or machine insights that are not available in an actionable manner

Traditional business intelligence and data warehouses struggle to manage and analyze new data sources.  Their architectures are:

  • Batch-oriented which delays access to the data for analysis
  • Brittle and labor intensive to add new data sources, reports, and analytics
  • Performance and scalability challenged as data scales to petabytes
  • Limited to aggregated and sampled data views
  • Unable to handle the tsunami of new, external unstructured data sources

Landmark #3:  Big Data Business Transformation

Where are an organization’s aspirations with respect to leveraging big data analytics to power value creation processes?  Some organizations struggle understanding the business potential of big data.  They are unclear as to the different stages of business maturity.  Our Big Data Maturity model benchmarks an organization’s big data business aspirations, and provides a way to identify the level of sophistication desired for data monetization opportunities:

  • Business Monitoring – deploys business intelligence to monitor on-going business performance
  • Business Insights – leverages predictive analytics to uncover actionable insights that can be integrated into existing reports and dashboards
  • Business Optimization – embeds predictive analytics into existing business processes to optimize select business operations
  • Data Monetization – creates new revenue opportunities by reselling data and analytics, creating “intelligent” products, or over-hauling the customer engagement experience
  • Business Metamorphosis – leverages customers’ usage patterns, product performance behaviors, and market trends to create entirely new business models

Landmark #4:  Big Data Journey

The big data journey requires collaboration between business and IT stakeholders along a path to identify the right business opportunities and necessary big data architectures.  The big data journey needs to 1) focus on powering an organization’s key business initiative while 2) ensuring that the big data business opportunities can be implemented by IT.  The big data journey following this path:

  • Identify the targeted business initiative where big data can provide competitive advantage or business differentiation
  • Determine – and envision – how big data can deliver the required analytic insights
  • Define over-arching data strategy (acquisition, transformation, enrichment)
  • Build analytic models and insights
  • Implement big data infrastructure, technologies, and architectures
  • Integrate analytic insights into applications and business processes

Landmark #5:  Operationalize Big Data

Successful organizations define a process to continuously uncover and publish new insights about the business.  Organizations need a well-defined process to tease out and integrate analytic insights back into the operational systems.  The process should clearly define roles and responsibilities between business users, the BI/DW team, and data scientists to operationalize big data:

  • Collaborate with the business stakeholders to capture new business requirements
  • Acquire, prepare, and enrich the data; acquire new structured and unstructured sources of data from internal and external sources
  • Continuously update and refine analytic models; embrace an experimentation approach to ensure on-going model relevance
  • Publish analytic insights back into applications and operational and management systems
  • Measure decision and business effectiveness in order to continuously fine-tune analytic models, business processes, and applications

Landmark #6:  Value Creation City

Big data holds the potential to transform or rewire your value creation processes to create competitive differentiation.  Organizations need a big data strategy that links their aspirations to the organization’s key business initiatives.  Envisioning workshops and analytic labs identify where and how big data can power the organization’s value creation processes.  There is almost no part of the organization that can’t improve its value creation capabilities with big data, including:

  • Procurement to identify which suppliers are most cost-effective in delivering high-quality products on-time
  • Product Development to identify product usage insights to speed product development and improve new product launches
  • Manufacturing to flag machinery and process variances that might be indicators of quality problems
  • Distribution to quantify optimal inventory levels and supply chain activities
  • Marketing to identify which marketing campaigns are the most effective in driving engagement and sales
  • Operations to optimize prices for “perishable” goods such as groceries, airline seats, and fashion merchandise
  • Sales to optimize account targeting, resource allocation, and revenue forecasting
  • Human Resources to identify the characteristics and behaviors of the most successful and effective employees

The Big Data Journey Storymap

The big data storymap provides an engaging visual for helping organizations understand some of the key components of a successful big data strategy.  I hope that you will enjoy the storymap as much as I enjoyed the opportunity to work with Mark Lawson and Glenn Steinhandler to pull it together!

 


 

Bill Schmarzo

About Bill Schmarzo


CTO, Dell EMC Services (aka “Dean of Big Data”)

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting the strategy and defining the Big Data service offerings and capabilities for Dell EMC Services Big Data Practice. As the CTO for the Big Data Practice, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power the organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill was ranked as #4 Big Data Influencer by Onalytica.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored Dell EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a masters degree in Business Administration from the University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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12 thoughts on “The Big Data Storymap

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  4. This is a very well-thought out and visualized allegory of the big data context that companies need to embrace or are already contending with without much of any guidelines.

    Thanks for putting this excellent collateral together and sharing it with everyone!

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