Many data analytics projects falter not from a lack of data, tools, or talent, but from a mismatch between the project’s unique needs and the methodology applied to it. In this blog, we explain how the Analytics8 Delivery Methodology (ADM) can deliver quick wins and ensure your data analytics projects align with your organizational goals and deliver lasting value.

While data analytics projects and general IT projects share some commonalities, they should not be approached with the same one-size-fits-all methodology. The unique and complex nature of data analytics projects requires a tailored approach.

To ensure consistent and reliable delivery of data analytics projects, we use our Analytics8 Delivery Methodology (ADM), which is based on more than 20 years of industry experience. The ADM delivers quick wins, incorporates client feedback throughout the project lifecycle, and results in sensible solutions that meet our clients’ needs.

In this blog, we explain:

    1. Why you need an iterative and practical approach to data and analytics ↵
    2. The key components of the Analytics8 Delivery Methodology ↵
    3. The ADM Advantage: Why ADM is Better and Different ↵

Why You Need an Iterative and Practical Approach to Data and Analytics

Data and analytics projects can be complex and often end up not meeting the needs of the business. We have found a few main reasons that data initiatives fail:

  • Lack of stakeholder involvement: When key stakeholders are not involved early and often enough, it leads to misalignment between the data initiative and the organization’s strategic goals. Stakeholders must be engaged throughout the process.
  • Absence of a retrospective approach: Data initiatives should be viewed as ongoing processes. Failure to continuously assess and gather feedback will result in a data solution that does not meet the needs of an organization and its end users.
  • Methodology is too generic: While generic methodologies provide a helpful structure to project management, data analytics projects benefit from specialized methodologies that address the unique characteristics, challenges, and opportunities presented by data initiatives.

We know that data analytics projects are rarely a straight line to the finish line. As work goes on, new requirements emerge, additional dependencies are uncovered, and early project successes reshape the project vision. These factors inevitably change the way a roadmap looks to meet your business goals.

Because of this, we take an intentional approach — one where we:

  • Plan a custom approach to client delivery
  • Build in retrospectives after each iteration
  • Ensure stakeholders remain invested from project conception to implementation

The time spent “not building” should not be viewed as a speed bump to reaching the finish line; it’s actually the secret sauce that helps our clients reach their goals more efficiently.

Project methodologies are not all the same and you should ask any provider you are considering what approach they follow.

Key Components of the Analytics8 Delivery Methodology

Using the Analytics8 Delivery Methodology (ADM), the initial iteration of a project results in a Minimum Viable Product (MVP) that satisfies core requirements. This serves as a foundation from which we continue to build on, leading to better outcomes over time.

This approach allows us to deliver data analytics solutions quickly, enabling our clients to start realizing value in weeks, not months.

Illustration of the Analytics8 Delivery Methodology showing a cyclic process that begins with Project Initiation, followed by Requirements Gathering, then moving through Design, Test & Build stages, and ending with Release & Iterate for continuous improvement. The graphic highlights steps like Kick-off Prep, Skeleton Project Plan, interviews, workshops, conceptual data model, data profiling, infrastructure & analytics design, and a focus on incremental delivery for effective analytical and data analytics methodologies.

Breaking down the Analytics8 Delivery Methodology: From initiating projects to iterative releases, each step provides value on the way to your optimal data analytics solution.

Here are the components of the ADM:

1.) Project Initiation: We do our homework and prepare ahead of time to ensure that the project gets started quickly. This includes reviewing information gathered during the sales process and referencing relevant solutions we’ve built for other clients.

Then we develop a skeleton project plan that outlines the scope, objectives, tasks, timelines, and other essential details to ensure your engagement is well-planned and aligned with your business goals. This project plan is a flexible, living, breathing document because we know things change and adjustments are necessary to delivering the right solution.

2.) Requirements Gathering: Before diving into any technical work, we invest the time to understand your needs, goals, and challenges. Our consultants don’t simply collect requirements. They leverage their expertise to provide recommendations and guidance to ensure that all business and technical requirements are defined, feasible, and aligned with your overall goals.

We use traditional methods to gather requirements including interviews, workshops, observation, and research; but we also use methods specific to data analytics projects, including conceptual data modeling and data profiling.

  • Conceptual data modeling: This might seem unusual in the requirements gathering phase, but doing this helps us gain an understanding of how all your data elements relate to your business problems. It helps clarify and communicate requirements and leads to consensus across teams about shared data and definitions — ultimately helping us design a solution that will get adopted.
  • Data profiling: We use a combination of statistical analysis, observation, documentation review, and ad hoc querying to gain a comprehensive analysis of your data to understand its structure, quality, and completeness. This informs the necessary data transformations and augmentations to convert raw data into information.

3.) Design: In this phase, we design the key elements of your data analytics solution, which includes:

  • The infrastructure supporting the solution
  • Data models and data flows
  • End-user-facing analytics

We design for aesthetics, data accuracy, security, scalability, and performance — so that you have a data solution that you will use forever.

4.) Build: We build the designed solution using the right combination of technologies and techniques. Throughout the build phase, we continuously refine the solution, often revisiting the design and requirements to ensure what we are building is optimized in every way and valuable to your business.

The build stage is a collaborative effort, giving our clients a hands-on opportunity to test drive the solution in the real-world and provide feedback as we continue to iterate and refine.

5.) Test: Testing a data analytics solution is focused on the correctness of the data shown in analytics, usability, and performance. Testing is a two-stage process: starting with our complete testing cycle, and then we guide you through User Acceptance Testing to address any remaining issues. At this stage, our goal is to ensure you are confident the solution is correct, compliant, secure, responsive, and production ready.

6.) Release: The release plan is just as important as the technical solution itself.

During this stage, we equip you with resources to thoughtfully communicate the rollout and ensure adoption.

We don’t just hand over the keys; we provide training materials, announcement templates, office hours, and more, so that you have a smooth launch, and your solution doesn’t end up on the shelf.

7.) Iterate: The project is not complete when we release the solution; in fact, that’s just the beginning. We deliver in a cyclical approach where we “iterate, release, collect feedback, and repeat”. This cycle occurs throughout the project lifecycle, with each iteration resulting in a new milestone and additional value.

This flexible approach enables quick time-to-value, allows for feedback, and changes along the way, and mitigates risk of ending up with a solution you won’t use.

The ADM Advantage: Why ADM is Better and Different

The ADM is centered on adaptability, client involvement, and continuous enhancement — setting it apart from generic strategies.

The ADM is:

  • Custom: The ADM is not a rigid guide but a flexible framework, creating individualized solutions aligned with your unique goals, mission, voice, and values.
  • Data Analytics Specific: Tailored specifically for data analytics projects, the ADM draws from two decades of experience in the industry.
  • Practical: The ADM involves upfront work to gain intimate knowledge of your business, resulting in practical, flexible, and sensible solutions.
  • Flexible: Knowing things change, the ADM allows for controlled deviations so that everyone remains invested in building something they’ll use.
  • Iterative: Designed for quick wins while building agile and scalable solutions, the ADM ensures your organization will be empowered with actionable information.
  • Focused on Adoption: We don’t build shelf ware; we build solutions that your organization will actually use and will stand the test of time.

Data analytics projects are complex, but the methodologies used for them shouldn’t be. To ensure the success of data initiatives, you should follow a proven framework that involves stakeholders from conception to implementation, delivers value in increments, and is custom-built to your unique data analytics requirements.

The Analytics8 Delivery Methodology is crafted for your success — delivering quick wins while building scalable data solutions that stand the test of time.

Get In Touch With a Data Expert 

Tracey Doyle Tracey is responsible for all marketing efforts for Analytics8's products and services including lead generation, brand positioning and awareness, content creation, digital marketing, advertising, business development, events, and sponsorships.
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