- 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.
Leverage Big Data by Starting Small Research & Tools
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.Besides the small introduction, subscribers and consulting clients within this management domain have access to:
- Leverage Big Data by Starting Small – Phases 1-4
1. Undergo big data education
Build a foundational understanding of the current big data landscape.
- Leverage Big Data by Starting Small – Phase 1: Undergo Big Data Education
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.
- Leverage Big Data by Starting Small – Phase 2: Assess Big Data Readiness
- Big Data Maturity Assessment Tool
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.
- Leverage Big Data by Starting Small – Phase 3: Pinpoint a Killer Big Data Use Case
- Big Data Use-Case Suggestion Tool
4. Structure a big data proof-of-concept project
Leverage a repeatable framework to detail the core components of the pilot project.
- Leverage Big Data by Starting Small – Phase 4: Structure a Big Data Proof-of-Concept Project
- Big Data Work Breakdown Structure Template
- Data Scientist
- Big Data Cost/Benefit Tool
- Big Data Stakeholder Presentation Template
- Big Data Communication Tracking Template
Workshop: Leverage Big Data by Starting Small
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 delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.
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
1.1 Determine which of the four Vs is most important to your organization.
1.2 Explore new data through a social lens.
1.3 Brainstorm new opportunities for enhancing current reporting assets with big data sources.
Outputs
Relative importance of the four Vs from IT and business perspectives
High-level improvement ideas to report artifacts using new data sources
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
2.1 Determine your organization’s current big data maturity level.
2.2 Plan for big data management.
Outputs
Established current state maturity
Foundational understanding of data management practices in the context of a big data initiative
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
3.1 Identify big data use cases at the industry and/or departmental levels.
3.2 Conduct big data brainstorming sessions in collaboration with business stakeholders to refine use cases.
3.3 Revisit the variety dimension framework to scope your big data initiative in further detail.
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.
3.6 Perform a value-effort assessment to prioritize your initiatives.
Outputs
Potential big data use cases
Potential initiatives rooted in the business context and identification of valuable data sources
Identification of specific data sources and data elements
Characterization of data sources/elements by value and risk
Prioritization of big data use cases
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
4.1 Construct a work breakdown structure for the pilot project.
4.2 Determine your project’s need for a data scientist.
4.3 Establish the staffing model for your pilot project.
4.4 Perform a detailed cost/benefit analysis.
4.5 Make architectural considerations for supporting the big data initiative.
Outputs
Comprehensive list of tasks for implementing the pilot project
Decision on whether or not a data scientist is needed, and where data science capabilities will be sourced
RACI chart for the project
Big data pilot cost/benefit summary
Customized, high-level architectural model that incorporates technologies that support big data