Guide for Building A Data Analytics Practice

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Guide for Building
A Data Analytics Practice

Diagram

Organizational Foundation

How do we build an organizational foundation for data and analytics?

A key objective for any executive team embarking on a data and analytics journey is to create a strong foundation for the program even before making any technology and people investments.

Questions to Explore

  • What do we want to achieve?
  • Who should be involved?
  • How will we know it worked?
  • Data Accountability and governance

Key Things to Accomplish

  • Define your Data and Analytics Vision and Strategy
  • Determine how you will measure results and gauge success
  • Identify the appropriate organizational structure for the data analytics program
  • Foster the development of a data culture within the organization through education and example

Common Mistakes to Avoid

  • Failing to understand your data analytics maturity and the impact that will have on the initiative.
  • Not aligning data analytics with business strategy
  • Being unrealistic about the credit union’s capacity to absorb change
  • Not seeking outside perspective and expertise from trusted advisors

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Investment and ROI

How much should we invest and what return should we expect?

It can be overwhelming to see how many vendors and solutions are available for data management and analytics. And the costs for these can be high, especially for smaller organizations.

Questions to Explore

  • What is required and what is optional?
  • What can we do economically with existing resources?
  • What is a realistic investment amount?
  • What return should we see for that investment?

Key Things to Accomplish

  • Prioritize the data projects that will have the greatest impact
  • Determine which will have the fastest time to value
  • Assign project management resources to help with time and cost estimation
  • Build the business case for each phase of your analytics program
  • Establish frequent review cadence to determine cost, benefit, return on investment and return on effort

Common Mistakes to Avoid

  • Failing to address technology, process and personnel
  • Not establishing measurable success criteria
  • Focusing on task completion rather than results
  • Measuring only technology costs and not including effort and opportunity cost in the equation

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Organizational Alignment

How do we align the organization and measure our progress?

It is critical to be certain that everyone is moving in the same direction and not at cross purposes. It is equally important to have periodic opportunities to ensure that plans are achieving the intended results.

Questions to Explore

  • How do we build a cross-functional roadmap for analytics?
  • What are the KPIs we should monitor?
  • How do we stay agile on a potentially long-term project?

Key Things to Accomplish

  • Clearly define objectives, success measures and activities
  • Build cross-departmental strategy maps that show dependencies, KPIs and target results
  • Communicate to all stakeholders both the how and the why of the plan
  • Establish regular and frequent reviews to identify issues that must be addressed as early as possible

Common Mistakes to Avoid

  • Failing to ensure that everyone understands both the ‘why’ and the ‘how’ of the strategy
  • Viewing this as an IT only initiative
  • Not identifying performance measures that monitors progress toward desired outcomes
  • Avoiding tough discussions about risk and failures encountered during the journey

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Analytics Building Blocks

What are the building blocks to a strong program and where should we start?

There are important steps in building an analytics program that must be undertaken before implementing any system.

Questions to Explore

  • What business problem are you solving and how can data help?
  • What is your true data environment?
  • What data is needed?
  • What technology is needed to collect, integrate and analyze that data?
  • Does your team have the necessary skills?

Key Things to Accomplish

  • Define and align on your data objectives and confirm executive support
  • Assess the current state of your data environment
  • Determine what infrastructure is needed to achieve your objectives
  • Identify technology vendors and establish good partnerships
  • Provide needed training to develop technical skills and capabilities
  • Augment teams as needed with external expertise

Common Mistakes to Avoid

  • Failing to understand your data analytics maturity and using that to help manage the pace of change
  • Data analytics is not aligned with business strategy
  • Belief that a software tool will do everything
  • Not seeking outside expertise and not building good vendor partnerships

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Demonstrating Value

How do we demonstrate the value that data brings?

It is important to identify all ways that data analytics benefits the business.

Questions to Explore

  • How does data analytics help us optimize our processes reduce costs by identifying more efficient ways of doing business?
  • How does analytics allow us to make better business decisions?
  • How does analytics help understand member and market trends and satisfaction, which can lead to new—and better—products and services.

Key Things to Accomplish

  • Identify the greatest business needs of your organizations – growth, cost containment, efficiency
  • Define the desired outcomes for each of these business needs and determine what impact analytics will have
  • Ensure that data programs have business stakeholders actively involved
  • Report on outcomes using business language and not technical terminology

Common Mistakes to Avoid

  • Treating data analytics as a project with finite scope
  • Forgetting to provide regular updates and share metrics-based success stories with stakeholders throughout the journey
  • Not having business team members as a part of each stage of the project
  • Failure to prioritize an issue with broad visibility and impact in the organization

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Building Trust in Analytics

How do we build trust and involve others in the program?

Successful analytics programs are not isolated in IT and implementing a software tool by itself will not deliver results.

Questions to Explore

  • Have we identified and involved the business users who will benefit most from analytics?
  • Have we addressed people, process and technology?
  • Have we accounted for the organization’s capacity to absorb change?
  • Have we looked for and communicated quick wins?
  • Ensure data accuracy, quality, consistency.

Key Things to Accomplish

  • Include nonexperts and everyday users on decision-making teams about new technology. 
  • Focus on benefit and value of the program to the business
  • Communication is key to maintaining commitment. Remember to translate into the business language of the recipient.
  • Measure progress often and adjust quickly if plans are not yielding the expected results

Common Mistakes to Avoid

  • The data analytics initiative is IT led and does not include business users
  • Not addressing people, process and technology
  • Failing to identify and address new data related issues and opportunities as they arise
  • Not fostering project transparency and not regularly communicating both successes and setbacks

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