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William Schmarzo

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Big Data Storymap Revisited | @BigDataExpo #BI #IoT #M2M #BigData #Analytics

I thought I’d take the opportunity to re-visit the storymap to see what we have learned over the past nearly 4 years

In January 28, 2013, we released the “Big Data Storymap.” Since releasing the storymap, we have gotten lots of positive feedback. It really seemed to work in highlighting the key aspects and approaches to achieving big data success. So I thought I’d take the opportunity to re-visit the storymap to see what we have learned over the past nearly 4 years – what we got right and what we need to tweak – to ensure that the storymap is as insightful and actionable to readers as ever (see Figure 1).

Figure 1: Big Data Storymap

Landmark #1: Explosive Market Dynamics

The purpose of Landmark #1 was to highlight the market challenges that were necessitating a different approach to integrating big data (data and analytics) into one’s business (we used cute landmarks instead of phases to keep in the spirit of the storymap).

In the original blog, we discussed how organizations that don’t adapt to big data risk the following impacts to their business models:

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

We also provided some examples of how organizations could exploit big data to power their businesses, including:

  • 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 a more compelling user experience
  • Integrate new big data innovations to modernize data warehouse and business intelligence environments (real-time insights, predictive analytics)
  • Become a data-driven culture
  • Nurture and invest in data assets
  • Cultivate analytic models and insights as intellectual property

Assessment: A+. Yea, I think we got this one right. The business potential is too significant for organizations to ignore, and the Internet of Things (IoT) is only going to make data and analytics more indispensable to the future success of an organization. Also, if I were to redo the storymap, I’d definitely replace the river with a lake.

For more business challenges and opportunities afforded by big data, check out these blogs:

Landmark #2: Business and IT Challenges

The purpose of Landmark #2 was to highlight the significant challenges that organizations faced in trying to transform their business intelligence and data warehouse environments to take advantage of the business benefits offered by big data.

The chart highlighted how traditional business intelligence and data warehouse environments are going to struggle to manage and analyze new data sources because of the following challenges:

  • Rigid data warehouse architectures that impede exploiting immediate business opportunities
  • Retrospective analysis that report what happened but doesn’t guide business decisions
  • Social, mobile, or machine insights that are not available in an actionable manner
  • Batch-oriented processes which delay access to the data for immediate analysis and action
  • Brittle and labor intensive processes to add new data sources, reports, and analytics
  • Environments that were performance and scalability challenged as data scales to petabytes
  • Business analysis limited to aggregated and sampled data views
  • Analytic environments unable to handle the tsunami of new, external unstructured data sources

Assessment: C. I under-estimated the cultural challenges of moving from Business Intelligence / Data Warehouse to Data Science / Data Lake; the challenge to unlearn old approaches so that one can embrace new approaches. I also missed the growing important of the data lake as more than just a data repository; that the data lake would transform into the organization’s collaborative value creation platform that brings Business and IT stakeholders together to exploit the economic value of data and analytics.

For more details on the challenges of transforming from a Business Intelligence to Data Science mentality, check out the below blogs:

Landmark #3: Big Data Business Transformation

The purpose of Landmark #3 was to provide a benchmark that helped organizations understand how effective they were in leveraging data and analytics to power their business models. The Big Data Business Model Maturity Index introduced 5 stages of measuring how effective organizations are at exploiting the business transformation potential of big data:

  • Business Monitoring – deploys business intelligence to monitor on-going business performance
  • Business Insights – leverages predictive analytics to uncover actionable insights buried in the detailed transactional data plus the growing wealth of internal and publicly available external data – at the level of the individual (think individual behavioral analysis)
  • Business Optimization – embeds prescriptive analytics (think recommendations) into existing business processes to optimize select business operations
  • Data Monetization – aggregates the insights gathered at the individual level to identify “white spaces” in unmet market and customer demand that can lead to new products, services, markets, channels, partners, audiences, etc.
  • Business Metamorphosis – the cultural transformation to data and analytics as the center of the organization with incentives around the collection, transformation, and sharing of data and analytics including how employees are hired, paid, promoted, and managed.

Assessment: A+. Nailed it! While the phase descriptions have evolved as we have learned more, this is probably my most important contribution to the world of Big Data – the “Big Data Business Model Maturity Index.” Not only does the maturity index help organizations understand where they are today with respect to leveraging the business model potential of big data, but it provides a guide to help them become more effective. Yeah, I finally got one right!!

If you are interested in learning more about the “Big Data Business Model Maturity Index,” check out these blogs:

Landmark #4: Big Data Journey

The purpose of Landmark #4 was to define a process that drives alignment between IT and the Business to deliver actionable, business relevant outcomes. The steps in the process were:

  • 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

Assessment: B. While I think I got the process right (especially starting with the Business Initiatives, and putting the technology toward the end), I missed on the importance of identifying the business stakeholder decisions necessary to support the targeted business initiative. It is the decisions (or use cases, which we define as clusters of decisions around a common subject area) that are the linkage point between the business stakeholders and the data science team.

Here is an additional blog that further drills down into the importance of the role of decisions in delivering business benefits:

Landmark #5: Operationalize Big Data

The purpose of Landmark #5 was to define a data science process that supported the continuous development and refinement of data and analytics in operationalizing the organization’s big data capabilities. This process included the following steps:

  • 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

Assessment: C-. While again I think I got the process right, recent developments in determining the economic value of data and analytics will greatly enhance the business critical nature of this process. Data and analytics as digital assets exhibit unique characteristics (i.e., an asset that appreciates, not depreciates, with usage and can be used simultaneously across multiple business use cases) to make them game-changing assets in which to invest. All I can say at this point is “Watch this space” because “you ain’t seen nothing yet!”

Blogs that expand on data and analytics operationalization concepts include:

Landmark #6: Value Creation City

The purpose of Landmark #6 was to provide some examples of the business functions that could benefit from 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

Assessment: A. Yea, I felt all along that the real value of big data would only be realized when we got technology out of the way and instead focused on understanding where and how big data could deliver business value and business outcomes. As I like to say, the business is not interested in the 3 V’s of Big Data (Volume, Variety and Velocity) as much as the business is interested in the 4 M’s of Big Data: Make Me More Money!

Blogs that go into more details on the business value aspects of big data include:

Big Data Storymap Assessment
We did a pretty good job of assessing the Big Data market with the Big Data Storymap 4 years ago. Much has happened the past 4 years that have helped to refine the Storymap lessons and recommendations. I hope the next 4 years are equally fruitful in providing more clarity to help organizations to understand where and how they can apply big data to power their business models.

If you want to learn more, my big data books provide more details on each of Big Data Storymap Landmarks. Check them out if you are bored, or give them as a Christmas present (a gift that just keeps on giving)!

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More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business”, is responsible for setting the strategy and defining the Big Data service line offerings and capabilities for the EMC Global Services organization. As part of Bill’s CTO charter, 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, avid blogger and is a frequent speaker on the use of Big Data and advanced analytics to power organization’s key business initiatives. He also teaches the “Big Data MBA” at the University of San Francisco School of Management.

Bill has nearly three decades of experience in data warehousing, BI and analytics. Bill authored 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 Advertiser Analytics at Yahoo and the Vice President of Analytic Applications at Business Objects.