Data Engineering and BI for teams that want decisions, not spreadsheets
If you own growth, operations, or product, you do not need "more data". You need trustworthy, timely numbers that your team can use without chasing exports or arguing over which report is right.
BYBOWU is a Phoenix-based data engineering and BI partner working with US and global companies. We design and implement data pipelines, warehouses, and dashboards that turn messy data into clear decisions for founders, marketing leaders, and operations teams.
Common data problems we fix
Most teams come to us with one or more of these issues:
- KPIs live in scattered spreadsheets, ad platforms, CRMs, and product tools with no single source of truth.
- Reports are slow to prepare and full of manual steps, so decisions lag behind reality.
- Marketing, sales, and product teams each have their own numbers and dashboards that do not match.
- Existing data pipelines are fragile, undocumented, and regularly fail without anyone noticing.
- Leadership wants more advanced analytics or AI, but the data foundation is not ready yet.
Our job is to replace this with a clean, documented data layer and BI stack that your team can trust and actually use.
How we approach your data and BI project
We keep the process practical and outcome-driven. No endless slide decks, just enough planning to build a system that works now and scales later.
- Discovery and metrics. We start with your business model, key decisions, and existing tools. Together, we define the KPIs that matter, the questions you need to answer, and the constraints around budget, security, and compliance.
- Data architecture and tooling. We map your data sources, design the warehouse or data store, and choose realistic tools for ingestion, storage, and visualization. We avoid exotic technology when a simpler, maintainable option will do.
- Pipeline and model implementation. We build ETL or ELT pipelines, clean and transform your data, and implement core models (such as customer, order, product, or event schemas) that match how your business actually operates.
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Dashboards and reporting. We design BI dashboards focused on the roles that will use them: leadership overviews, marketing performance, product usage, finance and operations views, and more.
- Validation, documentation, and training. We reconcile new metrics with your current reports, document how everything works, and train your team so they can explore data without constantly calling an engineer.
- Ongoing improvement. After launch, we can stay on to refine models, add new sources, and connect your data layer with AI and automation initiatives, often together with our AI & Automation Solutions team.
What you get as concrete deliverables
Every engagement ends with tangible assets your team can rely on, not just a diagram in a slide deck.
- Documented data pipelines (ETL or ELT) from your core systems to a central warehouse or data store.
- Cleaned and modeled data tables that reflect real business entities like customers, orders, subscriptions, and events.
- Role-based dashboards and reports for leadership, marketing, product, and operations teams.
- Data quality checks and monitoring so broken feeds are caught early instead of weeks later.
- Architecture diagrams, naming conventions, and a short data glossary so new team members can onboard quickly.
- Optional integration with web and app analytics from our Product Analytics Implementation and Web Development services.
What you can order
- Data audit and roadmap — A focused review of your current data sources, reporting, and tooling, with a practical architecture proposal, priorities, and a phased implementation plan.
- Marketing and revenue analytics foundation — Consolidate ad platforms, web analytics, CRM, and payment data into a single warehouse, with dashboards for acquisition, funnel performance, and revenue.
- Operational BI and reporting — A set of pipelines and dashboards for operations, support, and finance teams to track volume, SLAs, and costs in one place.
- Data warehouse modernization — Move from legacy or ad‑hoc databases to a modern, structured warehouse, often together with our Data Warehouse Modernization service.
- End-to-end product analytics — Implement tracking for your web or mobile apps, ship a central event model, and build product usage dashboards with our Product Analytics Implementation and Mobile App Development teams.
- BI dashboard rollout — Design and implement a library of BI dashboards on top of your existing warehouse, including access control, documentation, and user training.
How engagement works with BYBOWU
You are busy, so we keep the collaboration straightforward and transparent.
- 1. Intro call. We talk through your goals, existing stack, and timelines. If there is a fit, we propose a short discovery or audit.
- 2. Scope and estimate. You get a structured proposal with scope options, phases, and an estimated budget. We usually suggest a realistic "Phase 1" that starts delivering value quickly.
- 3. Build in clear iterations. We work in sprints, share progress frequently, and agree on which KPIs and dashboards will be available at each milestone.
- 4. Validate and launch. Before we call anything done, we verify numbers against your existing reports and run through real decision-making scenarios with your team.
- 5. Iterate or hand off. We can continue as your long-term data partner or hand over to your internal team with documentation and knowledge transfer.
If you are local and want in-person workshops, we can meet in Phoenix, AZ. If you are elsewhere in the US or abroad, we run everything over video and async channels without slowing you down.
Why choose BYBOWU for data engineering and BI
- Business-first, not tool-first. We start from the questions you need answered and only then choose the stack. You do not get an overbuilt system that your team cannot maintain.
- Engineering and product under one roof. Our data work is tightly aligned with your web, app, and backend systems, thanks to our Custom Software and DevOps & Cloud teams.
- Senior attention and clear language. You work with people who can explain trade-offs in plain English, not just SQL and schemas. We flag risks early and do not disappear when things get tricky.
- Focus on reliability and ownership. We care about monitoring, data quality, and documentation so you are not dependent on a single engineer or agency forever.
- Ready for advanced analytics and AI. A solid data foundation makes it much easier to add forecasting, personalization, or other AI use cases later with our AI Solutions & Custom AI Development.
Proof it works in the real world
Marketplace performance visibility
For an online marketplace in the fashion and tactical apparel space, we organised product, order, and traffic data into structured views so the team could see which categories, brands, and campaigns were actually driving revenue and repeat buyers. See project details.
B2B dropshipping reporting
A wholesaler and dropshipping platform needed clear reporting on suppliers, resellers, and product performance. We helped lay the foundations for a data model and reporting layer that reduced manual reconciliation and made it easier to spot profitable relationships. See project details.
User behavior insights for a roommate finder
On a real estate roommate-finding platform, we implemented event tracking and structured analytics so the founders could see how users moved from search to inquiry and where they were dropping off. This informed UX changes and product roadmap decisions. See project details.
Analytics backbone for ecommerce brands
Across several ecommerce projects, we integrated storefront, payment, and marketing data, giving teams reliable views of lifetime value, acquisition cost, and funnel health instead of siloed platform reports. See project details.
Questions founders usually ask
What budgets do you typically work with?
It depends on scope. A focused audit or dashboard implementation is at the lower end, a full data platform with pipelines and warehouse is mid-range, and complex, multi-source setups are higher. If you share your constraints, we can shape a phased approach. You can also review typical ranges on our Prices page.
How long will my data project take?
Roughly, a targeted audit or a small set of dashboards can take 2–4 weeks, a first production-ready data foundation with core KPIs often takes 4–8 weeks, and larger multi-system projects 8–16 weeks. We confirm milestones, dependencies, and launch windows with you before we start.
Can you work with our existing tools and stack?
Usually, yes. We can integrate with the databases, CRMs, analytics tools, and BI platforms you already use, or recommend alternatives where needed. Our goal is to minimize disruption and avoid locking you into tools that do not fit your team's skills.
How do you handle data security and access control?
We follow common-sense security practices: role-based access for dashboards, separation of production and analytics layers, careful handling of PII, and least-privilege access for pipelines. If you have specific compliance needs, we design around them and coordinate with your internal team.
Do we need a data team to work with you?
Not necessarily. We often work directly with founders, heads of marketing, or operations leaders. If you have analysts or engineers on staff, we involve them early and make sure the system we build is something they can extend and own.
What happens after the initial implementation?
You can keep us on for ongoing improvements, new data sources, and support, or take over internally. Many clients choose a light ongoing engagement combined with our Support & Maintenance services so critical pieces stay healthy while they focus on using the insights.
Talk through your data and BI roadmap
If you already know what you want to track, we can usually outline an initial architecture, rough budget, and timeline within one business day.
If you are still sorting out priorities, we are happy to review your current reports and tools and suggest a realistic first phase.
Contact us for a 24-hour estimate or request a Phoenix data and analytics review.
