Leverage Big Data by Starting Small

Big data is becoming simply data. Get to the core of your business needs and pinpoint the data sources that will propel their processes.


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Your Challenge

  • The desire for rapid decision making is increasing and the complexity of data sources is growing; business users want access to several new data sources, but in a way that is controlled and easily consumable.
  • Organizations may understand the transformative potential of a big data initiative, but struggle to make the transition from the awareness of its importance to identifying a concrete use case for a pilot project.
  • The big data ecosystem is crowded and confusing, and a lack of understanding of that ecosystem may cause a paralysis for organizations.

Our Advice

Critical Insight

  • Big data is simply data. With technological advances, what was once considered big data is now more approachable for all organizations irrespective of size.
  • The variety element is the key to unlocking big data value. Drill down into your specific use cases more effectively by focusing on what kind of data you should use.
  • Big data is about deep analytics. Deep doesn’t mean difficult. Visualization of data, integrating new data, and understanding associations are ways to deepen your analytics.

Impact and Result

  • Establish a foundational understanding of what big data entails and what the implications of its different elements are for your organization.
  • Confirm your current maturity for taking on a big data initiative, and make considerations for core data management practices in the context of incorporating big data.
  • Avoid boiling the ocean by pinpointing use cases by industry and functional unit, followed by identifying the most essential data sources and elements that will enable the initiative.
  • Leverage a repeatable pilot project framework to build out a successful first initiative and implement future projects en-route to evolving a big data program.


  • Albert Hui, Principal, Data Economist
  • Lynn Langit, Big Data and Cloud Architect, Technical Author
  • Nick Burch, Chief Technology Officer, Quanticate
  • Olivier Riboux, Director of BI Consulting, Project X Ltd.
  • Paresh Yadav, Big Data/Hadoop Architect, StratIS Inc.
  • Sean Jackson, Chief Marketing Officer, Exasol

Want to Participate in Our Research?

  • Analyst Interviews: Share your best practices, opinions, tools or templates with your peers.
  • Webinars: Interactive session to keep us focused on topics you want to tackle.
  • Upcoming Workshops: Accelerate your project with an onsite, expert analyst to facilitate a workshop for you. Contact us for more details.

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Get to Action

Start here – read the Executive Brief

Read our concise Executive Brief to find out why you should leverage big data, review Info-Tech’s methodology, and understand the four ways we can support you in completing this project.

  1. Undergo big data education

    Build a foundational understanding of the current big data landscape.

  2. Assess big data readiness

    Appraise current capabilities for handling a big data initiative and revisit the key data management practices that will enable big data success.

  3. Pinpoint a killer big data use case

    Armed with Info-Tech’s variety dimension framework, identify the top use cases and the data sources/elements that will power the initiative.

  4. Structure a big data proof-of-concept project

    Leverage a repeatable framework to detail the core components of the pilot project.

Guided Implementation icon Guided Implementation

This guided implementation is a ten call advisory process.

    Guided Implementation #1 - Undergo big data education

  • Call #1: Discuss the evolution of big data and the current landscape in terms of uses and technology.

  • Guided Implementation #2 - Assess big data readiness

  • Call #1: Discuss Info-Tech’s maturity model and how to evaluate your organization’s current capabilities.

  • Call #2: Revisit key data management practices to consider before taking on big data.

  • Guided Implementation #3 - Pinpoint a killer big data use case

  • Call #1: Discuss industry and department use cases you identified.

  • Call #2: Refine the use cases according to your business context.

  • Call #3: Review your top use cases and plan for the next phase.

  • Guided Implementation #4 - Structure a big data proof-of-concept project

  • Call #1: Discuss the formation of an effective project team.

  • Call #2: Review prioritized initiatives, preliminary work breakdown structure, CSFs, and risks.

  • Call #3: Understand the operational and architecture considerations for big data.

  • Call #4: Discuss how to successfully put together and execute a stakeholder presentation.

Onsite Workshop

Module 1: Undergo Big Data Education

The Purpose

  • Understand the basic elements of big data and its relationship to traditional business intelligence.

Key Benefits Achieved

  • Common, foundational knowledge of what big data entails.

Activities: Outputs:
1.1 Determine which of the four Vs is most important to your organization.
  • Relative importance of the four Vs from IT and business perspectives
1.2 Explore new data through a social lens.
1.3 Brainstorm new opportunities for enhancing current reporting assets with big data sources.
  • High-level improvement ideas to report artifacts using new data sources

Module 2: Assess Your Big Data Readiness

The Purpose

  • Establish an understanding of current maturity for taking on big data, as well as revisiting essential data management practices.

Key Benefits Achieved

  • Concrete idea of current capabilities.
  • Recommended actions for developing big data maturity.

Activities: Outputs:
2.1 Determine your organization’s current big data maturity level.
  • Established current state maturity
2.2 Plan for big data management.
  • Foundational understanding of data management practices in the context of a big data initiative

Module 3: Pinpoint Your Killer Big Data Use Case

The Purpose

  • Explore a plethora of potential use cases at the industry and business unit level, followed by using the variety element of big data to identify the highest value initiative(s) within your organization.

Key Benefits Achieved

  • In-depth characterization of a pilot big data initiative that is thoroughly informed by the business context.

Activities: Outputs:
3.1 Identify big data use cases at the industry and/or departmental levels.
  • Potential big data use cases
3.2 Conduct big data brainstorming sessions in collaboration with business stakeholders to refine use cases.
  • Potential initiatives rooted in the business context and identification of valuable data sources
3.3 Revisit the variety dimension framework to scope your big data initiative in further detail.
  • Identification of specific data sources and data elements
3.4 Create an organizational 4-column data flow model with your big data sources/elements.
3.5 Evaluate data sources by considering business value and risk.
  • Characterization of data sources/elements by value and risk
3.6 Perform a value-effort assessment to prioritize your initiatives.
  • Prioritization of big data use cases

Module 4: Structure a Big Data Proof-of-Concept Project

The Purpose

  • Put together the core components of the pilot project and set the stage for enterprise-wide support.

Key Benefits Achieved

  • A repeatable framework for implementing subsequent big data initiatives.

Activities: Outputs:
4.1 Construct a work breakdown structure for the pilot project.
  • Comprehensive list of tasks for implementing the pilot project
4.2 Determine your project’s need for a data scientist.
  • Decision on whether or not a data scientist is needed, and where data science capabilities will be sourced
4.3 Establish the staffing model for your pilot project.
  • RACI chart for the project
4.4 Perform a detailed cost/benefit analysis.
  • Big data pilot cost/benefit summary
4.5 Make architectural considerations for supporting the big data initiative.
  • Customized, high-level architectural model that incorporates technologies that support big data

Workshop Icon Book Your Workshop

Onsite Workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn’t enough, we offer low-cost onsite delivery of our Project Workshops. We take you through every phase of your project and ensure that you have a road map in place to complete your project successfully.

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  • Missing comment
    Edwin Anand | 10-07-2015

    "Pattern" here reflects Business Impetus that drives IT initiatives. Infotech has categorized into 4 themes: Cost Savings, Financial/Risk Management, Improved Service Delivery, Increased Revenue. There could be more.
    The central idea here is to align Big Data initiatives to the key business initiatives that thrive and make sense.
    Interesting concept - but nothing new. I found the template to be quite trivial. Overall, this topic needs more meat

    • 432c05244a845caaca3b276adb15a11e comment
      Info-Tech Research Group | 10-15-2015

      Thank you for your feedback.
      To better respond, we'd like to set up a call between you and our analyst to address your concerns. You should receive the request through your account manager.

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