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Getting Started with Analytics: 5 Basic Steps

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You can get started with your big data analytics project by following the five basic
steps.
Step 1: Focus on the business problems you are trying to solve.

Work with your business users to articulate the business opportunities. 

• Identify and collaborate with business users—including analytics, data, and compliance officers; data scientists and stewards; citizen data scientists; and developers—to find the best business opportunities for big data analytics in your organization.
• Consider an existing business problem—especially one that is difficult, expensive, or impossible to accomplish with your current data sources and analytics systems. Or consider a problem that has never been addressed before because the data sources are new and unstructured.
• Prioritize your opportunity list and select a project with a discernible return on investment. To determine the best project, consider your answers to these questions:
•What am I trying to accomplish?
•Does this project align with strategic business goals?
•Can I get management support for the project?
•Does big data analytics hold a unique promise for insight over more traditional analytics?
•What actions can I take based on the results of my project?
•What is the potential return on investment to my business?
•Can I deliver this project with a 6- to 12-month time to value?
•Is the data that I need available? What do I own? What do I need to buy?
•Is the data collected in real time, or is it historical data?

Collaborate with business leadership on a big data strategy and approach.
Develop:

• The business case for analytics – Define how analytics drive value for your business. Identify the key business challenges analytics solutions will address.
• Short-, mid-, and long-term objectives – Outline the key phases to achieving your analytics goals.

Step 2: Understand how analytics will impact your culture and operations.

Develop a closer understanding of data analytics solutions.
• Talk with your peers in IT and the business.
• Take advantage of Intel resources for analytics to get up to speed on the technologies.
• Understand vendor offerings.
• Leverage tutorials and examine user documentation, such as that offered by the Apache Hadoop project.

Evaluate your infrastructure and operational needs. Consider:
• The current and future state of your IT infrastructure – Can your data center support the big data platform? Assess your current data center technology and describe, if necessary, your plan to upgrade computing, storage, and networking resources.
• Data sources and data quality – What are the primary sources of data internally?
What additional data might you purchase? How will you ensure quality?
• Analytics platform and tools – What platform will you use to build your solution?
What software and tools are needed to achieve your purpose?
• Metrics for measuring success – How will you measure system performance?
Base your success on how many jobs are submitted, parallel processed, and
completed efficiently.

Step 3: Identify and cultivate the skills you need.

Understand and plan for the skills required in both business and IT.
• Identify the skills you need to successfully accomplish your analytics initiative.
• Determine if the needed resources are in-house.
• Determine if you can build skills from within the company.
• Determine where your analytics professionals will reside within the business or
IT organization.
• Hire new talent or outsource certain functions as needed.

Step 4: Consider your technology requirements.

Identify the gaps between current- and future-state capabilities.
• What additional data quality requirements will you have for collecting, cleansing, and aggregating data into usable formats?
• What data governance policies will need to be in place for classifying data; defining its relevance; and storing, analyzing, and accessing it?
• What infrastructure capabilities will need to be in place to ensure scalability, low latency, and performance, including computing, storage, and network capabilities?
• Do you need to add specialized components like a NoSQL database for lowlatency lookups on large volumes of heterogeneous data?
• If you plan to process a steady stream of real-time data, what additional infrastructure and memory capabilities will you need? Will you require an MPP in-memory analytics appliance? A CEP solution?
• Identify the analytical queries and algorithms required to generate the desired outputs.
• If you are considering cloud computing for your delivery model, what type of cloud environment will you use? Private, hybrid, public?
• How will data be presented to users? Findings need to be delivered in an easyto-understand way to a variety of business users, from senior executives to information professionals.

Step 5: Implement your data solution.

Develop use cases for your project.
• Identify the use cases required to carry out your project.
• Map out data flows to help define your required analytics capabilities.
• Decide what data to include and what to leave out. Identify only the strategic data that will lead to meaningful insight.
• Determine how data interrelates and the complexity of the business rules.
• Consider whether you need to support advanced analytics, such as interactive queries or predictive analytics, or support real-time data streams.

Develop a test environment for a production version.
• Adapt reference architectures to your enterprise. Intel is working with leading partners to develop reference architectures that can help as part of the Intel Cloud Builders program around big data use cases.
• Define the presentation layer, analytics application layer, data warehousing, and, if applicable, private- or public-based cloud data management.
• Determine the tools users require to present results in a meaningful way. User adoption of tools will significantly influence the overall success of your project.

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