With Analytics8 DTaaS, you get a team accountable for measurable business value, not just technical outputs, bringing the strategic judgment to define the work and the cross-lifecycle expertise to deliver it. Table of Contents: What Data Team as a Service Is and Why Companies Use It ↵ How the Analytics8 DTaaS Model Works ↵ The Details: Typical Timeline, Deliverables, and Commitment ↵ Why Partner with Analytics8 for your Data Teams as a Service ↵ Frequently Asked Questions ↵ What Data Team as a Service Is and Why Companies Use It Organizations typically start to explore DTaaS (a managed data services delivery model) when the problem is no longer capacity, but ownership. They lack clarity and accountability around the “what, when, and how” required for sustained execution and return on data investments. Many in-house data teams are fully occupied “keeping the lights on” — maintaining pipelines, answering ad-hoc requests, and ensuring reports continue to run — which leaves strategic initiatives in the backlog because no one has the time to define the work, prioritize it against business goals, and drive it forward consistently. DTaaS with Analytics8 closes that execution gap: we own the full lifecycle of your ongoing data initiatives — from discovery and planning to prioritization and execution. How Data Team as a Service Differs from Traditional Consulting and Staff Augmentation Data Team as a Service is built for ongoing execution, not a one-time project or a temporary increase in capacity. Traditional consulting and staff augmentation solve different problems: Staff augmentation adds data engineers or data analysts who execute tasks defined by your organization. Project-based consulting delivers a defined initiative within a fixed scope and timeline that are known in detail from the outset. Both models are valuable in the right circumstance, but they rely on internal leadership to define the work, set priorities, and coordinate execution. Data Team as a Service with Analytics8 is an end-to-end-service where we take accountability for defining the work, sequencing it against business priorities, and delivery measurable outcomes — working collaboratively with your team while owning delivery. DTaaS Customer Spotlight: From 11 Vendors to 1 Data Team Following major acquisitions that added multiple franchise brands to their portfolio, Driven Brands faced a complex data challenge. Eleven different vendors were managing pieces of their data ecosystem. Each brand within their organization had its own infrastructure. Data Moved slowly — taking days or weeks to get from source systems into reports. The technical debt was compounding, and sprawling systems were driving millions in annual storage costs. And because each brand operated in its own silo, there was no dingle source of truth for their analysis. Staff augmentation wouldn’t solve this. Adding more people to a fragmented system would only add more complexity. “When work is siloed between BI and data engineering, you see conflict occur — whose responsibility is it to handle this? The business doesn’t know nor care about the distinctions between data engineering versus BI development. They need to be able to go to one team and say, ‘Hey, this is an issue,’ and be assured that team is going to resolve the issue.” “Under the DTaaS model, we worked one cohesive unit to address common issues in a way that siloed processes just can’t do.” – Travis LaMont, Analytics8 Project Management Lead The scope of their work was best addressed with a complete data team. The results from the DTaaS engagement: Replaced 11+ vendors and FTEs with a single accountable team, supporting Driven Brands for 3+ years and counting 95% reduction in data intake time — data that once took days now flows in hours 80% elimination of inefficient code that was creating technical debt Millions of dollars saved annually in data storage costs by modernizing how data was managed A single source of truth that gave leadership consistent, reliable insights across all brands How the Analytics8 Data Team as a Service Model Works An Analytics8 DTaaS engagement operates through a structured delivery model where we govern how new data initiatives are evaluated, prioritized, and executed: Analytics8 manages intake and prioritization Our team evaluates each initiative against your business priorities, technical dependencies, and current commitments. Work is then sequenced deliberately so that we can focus on the initiatives that align with your organization’s strategic objectives and will advance your data maturity. “We own the discovery, planning, prioritization, and execution of ongoing data work as a cohesive set of responsibilities. We collaborate closely with the client, but from an accountability standpoint we own making sure the work gets defined, prioritized, and delivered.” – Hart Shuford, DTaaS Lead Analytics8 structures the work through defined workstreams Delivery is organized around workstreams tied to business outcomes. Depending on your priorities, these workstreams may include: Data platform and engineering — ingestion pipelines, transformation logic, testing, monitoring, and platform modernization Analytics and BI — metric definitions, semantic layers, dashboards, and analytical interfaces Data infrastructure and security — scalable architecture, access controls, and compliance safeguards Data governance and operating model improvements — establishing definitions, ownership, and standards that support reliable analytics Advanced analytics and AI — predictive models and AI-enabled workflows that support decision-making “What the model allows is multiple workstreams moving forward at the same time. We’re not limited to a single project or phase. We can execute across engineering, analytics, governance, or platform work depending on what the organization needs most.” – Hart Shuford, DTaaS Lead Analytics8 provides leadership responsible for delivery A Consulting Director oversees delivery standards, engagement health, and outcomes. A Project or Program Director manages intake, prioritization, and coordination. Expert data, analytics, and AI consultants execute within defined workstreams. This structure ensures the engagement maintains both strategic oversight and execution capacity, while providing ongoing transparency and collaboration with stakeholders. “One of the biggest differences in this model is the level of ownership. When something is blocked or incomplete, the expectation is that they take ownership of resolving it rather than waiting for someone else to define the next step.” – Travis LaMont, Analytics8 Project Management Lead Progress and priorities are reviewed with your leadership Analytics8 works with your leadership to review progress, introduce new initiatives, and adjust priorities. Your key stakeholders have a transparent view into progress, risks, and tradeoffs, and remain involved at key decision points. Because the same team handles planning and execution, continuity is maintained as priorities evolve. The Details: Timeline, Deliverables, Investment What is the typical timeline for a DTaaS engagement? Most engagements run six months minimum (the threshold for moving beyond tactical fixes into strategic outcomes), with many extending one to two years as the work evolves. A typical 30/60/90-day progression looks like this: First 30 Days – Discovery & Assessment Team onboarding and technical access Discovery sessions with stakeholders Assessment of the current data environment Initial roadmap and work plan creation 60 Days – Data Foundation Work Foundational architecture decisions Initial platform or infrastructure work underway Early pipelines, models, or reporting components in development 90 Days – Execution Against Data Strategy Data flowing through the new architecture Early analytics and reporting outputs available to the business Execution underway against the longer-term roadmap 3 – 6+ months – Ongoing Prioritization, Increased Data Maturity, and Adoption Continuous execution against evolving priorities and workstreams Expanding organizational data capabilities and maturity Governance standards, operating processes, and knowledge transfer for internal teams What is the engagement cadence of DTaaS? The engagement typically includes: Daily stand-ups with the project manager and deliver team to coordinate technical work and track progress Weekly stakeholder reviews to discuss progress, risks, and upcoming priorities Executive check-ins to confirm alignment with organizational objectives and for key decisions This ensures that work remains visible, decisions are addressed quickly, and progress continues even as priorities evolve. What is the client’s commitment for a DTaaS engagement with Analytics8? While Analytics8 owns delivery, successful engagements still require collaboration with your internal team. At minimum, you would provide: Access to key data platforms and systems Availability for periodic progress discussions and decisions Access to business stakeholders when clarification or feedback is needed Client involvement is typically highest during the early discovery and planning stages and becomes lighter as the engagement moves into steady-state execution. Why Partner with Analytics8 for Your Data Team as A Service When leaders choose a DTaaS partner, they’re placing accountability for sustained delivery with a team that must know what works, what breaks, and how to keep progress moving when priorities shift. Our consultants have delivered DTaaS across organizations from greenfield startups building their first data infrastructure to established enterprises modernizing platforms that support millions in annual revenue. We’ve worked with clients running 6-month focused engagements and multi-year transformations. That experience is built into how we structure engagements, manage teams, and maintain momentum. “When you hire us, you’re not just hiring the individuals on the delivery team, you’re actually hiring the whole of the consulting firm. The delivery team are the main executors, but they can leverage the collective expertise of the rest of the firm — and experience across the entire data lifecycle.” – Justin Lee, Managing Consultant We start with your context — where you are, what you’re trying to accomplish, and what constraints you’re operating under — then build the team composition and delivery approach to fit. “With 20+ years in data and analytics, we’ve built a culture of always asking ‘why.’ Why are we building this? How will the business use it? That mindset permeates everything we do — from data engineering to strategy.” – Travis LaMont, Analytics8 Project Management Lead How to Evaluate Data Team as a Service Providers When evaluating DTaaS partners, five factors matter most: Accountability: Some providers offer “data team” structures but still require you to define the work, manage priorities, and coordinate delivery. Analytics8 owns discovery, planning, prioritization, and execution as a cohesive set of responsibilities, all while offering transparency into progress and collaboration on key decisions. Expertise: Many firms specialize in one area (data engineering or BI or strategy), and struggle when work requires coordination across the full lifecycle. Analytics8 consultants work across multiple areas. The same team that builds your pipelines can architect your platform and design your semantic layer. Flexibility: Business needs shift. New initiatives emerge mid-engagement. The model flexes as priorities change — engagements can start focused on analytics and expand to platform ownership, or shift from engineering to governance, without disrupting delivery. Speed: DTaaS should deliver value in weeks, not quarters. Engagements should show measurable progress, proving value fast and building momentum for longer-term work. Transparency: Not every situation needs DTaaS. If you have clear, well-defined work and capacity to manage execution, a fixed project engagement may be more appropriate. A good partner advises which model is best for your needs. The Analytics8 Difference: We Turn Your Data into Measurable Business Impact The market is saturated with staff augmentation and project-based offerings. A Data Team as a Service engagement with Analytics8 is designed to accelerate your data maturity over time — without the disruption of repeated scoping exercises or team resets. When you work with us, you get the experience of a team that’s done this before — across industries, scales, and technical environments. Our difference includes: Business outcomes, not just technical outputs: Before we write any code, we start with your business goals, challenges, and decision processes. Every engagement is anchored to measurable business outcomes, from new revenue streams and stronger margins to faster decisions and greater operational efficiency. Depth of experience across the data lifecycle: Our consultants don’t specialize in narrow lanes. They work across strategy, architecture, engineering, analytics, and governance — bringing cross-functional perspective that eliminates silos and keeps delivery cohesive. Owned delivery, not task execution: We take accountability for defining the work, sequencing it against business priorities, and delivering outcomes. You make key decisions through informed collaboration with our team. We own execution. Speed to measurable value:begin with discovery and alignment on priorities, then move quickly into sustained execution through defined workstreams with clear ownership and regular reviews. Built-in knowledge transfer: We don’t create dependency. We document as we build, train as we deliver, and prepare your organization to take ownership when the engagement evolves. Frequently Asked Questions What’s the difference between Data Team as a Service and staff augmentation? Staff augmentation provides individual contributors who work under your direction. You define the work, set priorities, and manage execution. The consultants execute tasks you assign. A Data Team as a Service engagement with Analytics8 provides a team that owns discovery, planning, prioritization, and execution. We don’t just execute your backlog, we help determine what should be in it, in what order, and then deliver it. You make strategic decisions. We own the work. This means you stay informed and equipped to make key decisions without taking on the additional work of managing a data team. It also means the investment is structured differently: initial engagements are sized for real discovery and onboarding, and continuation depends on demonstrated value, not predetermined headcount targets. How long does a Data Team as a Service engagement last? Six months is typically the threshold for moving beyond tactical fixes into strategic outcomes. Many engagements extend one to two years as the work evolves. What if our priorities change mid-engagement? A Data Team as a Service engagement with Analytics8 is a dynamic delivery model that is designed for ongoing changes in priorities. We collaborate with clients to understand what needs to be prioritized, why changes need to be made, and what the tradeoffs are. The goal of a DTaaS engagement is to execute data work over a sustained period; shifts in priorities are almost guaranteed, and we equip our teams to manage that. What size team should we expect in a Data Team as a Service engagement? Team size depends on scope. A single workstream engagement might be 2-3 people. A full-function team covering platform, engineering, analytics, and governance might be 4-6 people. Every engagement includes a Consulting Director (delivery oversight), a Project or Program Director (intake and prioritization), and consultants executing the work. Can we scale the Data Team as a Service team up or down as our needs change? Yes. Team composition adjusts as workstreams and priorities evolve. Analytics8 manages team scaling based on the work required, adding expertise when new workstreams open, adjusting allocation as initiatives complete. We keep you informed when changes are needed, and we work within the contract model to manage scope and budget. Scaling decisions are driven by what the engagement needs to deliver, not arbitrary staffing changes. Can our internal data team work alongside your Data Team as a Service team? Yes. Data Team as a Service works whether you have an existing team or not. If you have internal data staff, we typically complement them — your team brings domain knowledge and business context, while we own the execution backlog and delivery. This frees your internal team to focus on strategic decisions and stakeholder engagement rather than getting buried in backlog work. If you don’t have an internal team or your team is focused elsewhere, we can serve as your complete data function. How do you ensure knowledge transfer in a Data Team as a Service engagement? Knowledge transfer is built into delivery, not saved for the end. We document architectures, data models, and processes as we build them. Clients participate in design decisions and review sessions so they understand not just what was built, but why. As systems go live, we train users and provide runbooks for operations and maintenance. The goal is to prepare your organization to own what we’ve built, whether the engagement continues or transitions. What happens when a Data Team as a Service engagement ends? As an engagement concludes, we conduct structured handoffs, including documentation reviews, technical walkthroughs, and knowledge transfer sessions, to ensure your team can operate and maintain what we’ve built. You receive architecture documentation, runbooks, and process guides so nothing is left undocumented. A DTaaS engagement with Analytics8 might shift to project-based work for specific initiatives, ongoing advisory support, or continue at reduced capacity as your internal team takes on more ownership. How do you handle data security and compliance in Data Team as a Service engagements? We operate within your security frameworks and policies. Our team accesses data through your existing systems and controls; we don’t create separate environments or bypass governance structures. We follow your authentication protocols, data handling policies, and compliance requirements whether you’re managing HIPAA, GDPR, or industry-specific regulations. If your security and governance frameworks need strengthening as part of the engagement, we build that into the work. Many DTaaS engagements include governance workstreams that establish access controls, data classification, and compliance processes alongside delivery. What kinds of companies do you work with for Data Team as a Service? DTaaS applies to all industries. If you have strategic work sitting in the backlog while teams stay busy maintaining existing systems. We’ve delivered DTaaS engagements across healthcare, life sciences, financial services, insurance, retail, manufacturing, and franchises. Do we need to provide office space or equipment for the Analytics8 team? No. DTaaS engagements are delivered remotely using secure access to your systems. Our team works from Analytics8 offices or home offices with the equipment and infrastructure necessary for day-to-day delivery. You provide access to your data platforms, collaboration tools, and key stakeholders — we handle the rest. On-site collaboration for workshops, discovery sessions, or stakeholder alignment occur on an as-needed basis. Talk With a Data Analytics Expert Key Takeaways Data Team as a Service solves an ownership problem, not a capacity problem. If no one is defining, prioritizing, and driving the work, progress stalls no matter how many resources you add. Traditional models rely on you to manage the work. DTaaS shifts that burden by owning discovery, prioritization, and execution as one continuous process. Consolidating delivery under one accountable team reduces friction, eliminates silos, and accelerates outcomes that fragmented teams struggle to achieve. Value should show up early. Within the first 90 days, you should see data flowing, outputs in use, and execution underway against a defined roadmap. Speed comes from disciplined prioritization. The ability to sequence work against business goals matters more than simply increasing activity. The right model adapts as priorities change. Ongoing execution requires flexibility without resetting teams or losing momentum. Long-term success depends on building internal capability. Documentation, training, and shared context should happen during delivery, not after it ends.
