Data Analytics Consulting and BI Reporting
Our data analytics consulting connects the ERP, CRM, spreadsheets, and legacy databases you already run, builds the pipelines and data platform underneath them, and delivers reports and dashboards that show every team the same numbers.
Source systems stay in place
- Manual exportsAutomated pipelinesLive
- Spreadsheet reportsDashboardsLive
- Access databaseCloud databaseMoving
- Ad hoc questionsQueued
ONE DATA PLATFORM
ERP, CRM, and operations
Still running
What Is Data Analytics Consulting?
It is the work of turning the data in your business systems into reports, dashboards, and answers that people use to make decisions. The work runs from pulling data out of source systems, through cleaning and storing it, to the dashboard a leader reads.
Most companies already have the data. It sits in an ERP, a CRM, operational databases, and spreadsheets that were never built to share it. The useful part of the work is connecting those sources, settling what each number means, and keeping reports current without manual effort.
How is BI reporting different?
Business intelligence reporting is the part people see: dashboards, scheduled reports, and self-service views in tools such as Power BI or Tableau. Analytics work also covers the pipelines and platform underneath, which decide whether those reports are correct.
SIGNS
Signs Your Data Is Slowing Decisions
Data problems rarely announce themselves. They show up as slow answers, manual work, and reports that depend on one person. These are six of the most common.
Simple questions take a week
A leader asks for a number, and the request joins the BI team's queue. By the time the report arrives, the decision has already been made.
People move data by hand
Staff download files from one system, reformat them, and upload them to another every day, and every manual step is a chance for an error.
One person keeps reports running
The scripts, scheduled jobs, and report logic live in one engineer's head. When that person is out, reporting stops.
Data loads fail without warning
Nightly jobs run as scheduled tasks on a server with no monitoring, so a failed load shows up only when someone spots a stale number.
Every tool has its own data
Each SaaS product keeps its own copy of customers and orders, none of them agree, and nobody can see the whole business in one place.
Core data sits on an unsupported platform
An Access database or an aging application holds data the business depends on, and the platform under it is no longer supported.
OUR APPROACH
How We Approach Data and Reporting
We start with the decisions your leaders need to make, then build only the data work those decisions require. Senior US-based engineers lead our builds, with nearshore support available for ongoing work.
Tool-first data projects
- A platform bought before the questions are known
- Every pipeline rebuilt at once
- Dashboards nobody trusts
- Logic only one person understands
Decision-first, senior-led
Start with the questions
We list the decisions and reports that matter most, then trace each one back to its source systems and data.
Keep what works
A nightly file transfer that runs reliably can stay as it is. We fix the pipelines and reports that cause problems and leave stable ones alone.
Fit the platform to your stack
We pick platforms based on the task, accuracy needs, privacy, latency, and cost, on Microsoft, AWS, or both.
Built for your team to run
You own the platform, the data model, and the pipelines. Existing BI and data teams can often run them without new specialist hires.
WHAT WE DO
Our Data and Analytics Services
Our data analytics services cover five connected areas. Most engagements start in one and add the others as the data foundation grows.
Data Engineering
Pipelines that pull data from ERP, CRM, SaaS tools, and files, check it, and deliver it on a schedule or as it changes. For a regional bank, a daily pipeline replaced the manual handling of about 30,000 records a day.
Explore data engineeringData Warehouse & Data Platforms
One central platform, such as Microsoft Fabric, Snowflake, Amazon Redshift, or a well-modeled SQL database, that holds cleaned data from every source for reporting and AI.
Explore data warehouse consultingBusiness Intelligence & Power BI
Dashboards and reports in Power BI or Tableau, built on one trusted data model, so finance, sales, and operations bring the same numbers to the same meeting.
Explore business intelligence and Power BIAI-Powered Analytics
Plain-language questions answered from your own data, plus forecasting, with people reviewing results before anyone acts on them. For a manufacturer, a forecasting model reached 85 to 90% accuracy on sales forecasts.
Explore AI-powered analyticsData Modernization
Data moved off Access databases, aging applications, and unsupported platforms onto ones your team can support, in stages and checked against the old system before cutover.
Explore data modernization
HOW IT WORKS
How a Data Analytics Engagement Runs
A data engagement runs in five steps. The first reports reach your leaders before every source is connected.
STEP 1
Decisions and reports
We list the decisions leaders make and the reports they rely on today.
STEP 2
Source assessment
We inventory your systems, data quality, and the jobs that move data now.
STEP 3
Platform and model
Senior engineers choose the platform and design the data model.
STEP 4
First pipelines and reports
We connect the most important sources and ship the first reports.
STEP 5
Expand and hand off
More sources follow in stages, then knowledge transfer to your team.
Start with the reports that matter most
We don't connect every system before anyone sees a result. The first stage covers the sources behind the reports leaders use most, so trusted numbers arrive early. More sources and reports follow on the same platform, and each stage is planned and estimated on its own.
What your team gets at handoff
You own the platform, the data model, the pipelines, and the reports. Services we can provide at handoff include knowledge transfer sessions and documentation covering the data model, the pipelines, and how to add new sources and reports. Ongoing support is also available.
RESULTS
Data and Analytics Results
For a post-acute care provider, JBS Dev unified four separate systems into one data warehouse, rebuilt five legacy reports, and added ten new ones. For a regional bank, JBS Dev replaced the manual handling of about 30,000 client records a day with an AWS pipeline, delivered in just under four months.
4
Systems unified into one data warehouse
Healthcare
One Data Warehouse for a Post-Acute Care Provider
Challenge: Four systems ran in isolation, and analysis depended on Excel pivot tables.
Result: A unified data warehouse with Tableau became one source of truth, with five legacy reports rebuilt and ten new ones added.
Read the post-acute care case study3 mo
To a working analytics dashboard MVP
EdTech
Analytics Dashboard MVP for Noodle Partners
Challenge: No single platform gave staff the data they needed, so they worked across several systems.
Result: JBS Dev delivered a working dashboard MVP on AWS in the planned three months and is building it into a full analytics platform.
Read the Noodle Partners case study"JBS feels more like an extension of our internal team than a traditional vendor relationship."
Senior IT Leader, AmeriVet Partners
TECHNOLOGY
Data Technologies We Use
We work on Microsoft and AWS data platforms and recommend the one that fits your systems, skills, and budget. On Microsoft, that usually means Microsoft Fabric, Azure SQL Server, Azure Data Factory, SSIS, and Power BI. On AWS, it means Amazon S3, AWS Glue, EventBridge, Amazon Redshift, and Aurora.
We also work with Tableau, Snowflake, Databricks, Fivetran, PostgreSQL, and SQL Server, and with streaming tools such as Apache Kafka and Amazon Kinesis.
ON MICROSOFT AZURE
Azure SQL
Microsoft Fabric
Power BI
ON AWS
AWS Glue and EventBridge
Pipelines and triggers
Amazon Redshift and Aurora
Warehouse and databases
Does the data need to be perfect first?
No. Messy data is the normal starting point, and pipelines with validation and reconciliation improve it each time data loads. AI tools that answer questions from your data can work with imperfect data too. Machine learning models are the exception, because their accuracy depends on the quality and volume of the data they learn from.
Where AI fits
An AI model connected to your BI platform can turn a plain-language question into a query and return the result. We run that analysis in a sandbox instead of your production data warehouse, and people review results before acting on them. See AI-powered analytics.
COST AND TIMELINE
What Drives Data Analytics Cost and Timeline
Cost and timeline depend mostly on the number of data sources and the state of the data in them. Six factors drive most estimates:
- 01Number and type of data sources
- 02Data quality and history
- 03Number of reports and dashboards
- 04Platform licensing and cloud compute
- 05Security and access requirements
- 06Support after launch
How the timeline is set
Timelines depend on the number of sources and the state of the data, and the assessment sets them. We deliver in stages, so the first reports reach leaders early. As a reference point, a regional bank's daily data pipeline went from kickoff to running in just under four months.
How cost is set
We don't quote a number before we understand your sources and reporting needs. The assessment turns the factors above into an estimate for each stage, including ongoing platform and cloud costs. Before recommending any piece of work, we ask whether it will pay for itself. If it won't, we'll tell you.
ENGAGEMENT MODELS
Ways to Work With Us
Each model stands alone or leads into the next. Compare all the ways to work with us.
Assessment & Strategy
For companies that need clarity before committing to a build.
Start here if you're not sure what to change first. We review the system component by component, weigh difficulty against business impact, and recommend a path for each part, including what to leave alone. You get a roadmap you can act on with us or on your own.
- Technical assessment
- Architecture review
- AI opportunity mapping
- Modernization roadmap
- Prioritized recommendations
Best when: You need to understand the opportunity, risk, architecture, or roadmap before investing in a larger build.
Projects
For companies ready to build or modernize a defined system.
For a defined scope, senior engineers lead delivery from architecture through release. We work in stages, write tests for core components, and ship through CI/CD. You own everything we build, plus knowledge transfer and documentation tailored to your team.
- Modernization projects
- Custom applications
- Cloud platforms
- Data systems
- Integrations
- AI agents
Best when: You have a clear business problem, workflow, or platform that needs to be built, modernized, or connected.
Teaming / Managed Delivery
For companies that need experienced delivery capacity.
When you need more than a single project, services we can provide include architectural guidance for your existing team, hands-on development, QA, and ongoing support. Senior US-based engineers lead our builds, and nearshore support is an option for ongoing work.
- Senior-led teams
- Embedded engineering
- Ongoing development
- Knowledge transfer
- Flexible support
Best when: You need reliable engineering capacity, technical leadership, or ongoing delivery support without adding unnecessary management overhead.
WHY JBS DEV
How to Choose a Data and Analytics Consulting Partner
A data partner should leave you with numbers your team trusts and a platform it can run. Here are five questions to ask, and how we answer them.
- Do they start with the tool or the question?
- The question. We start with the decisions and reports that matter, then choose the data work and platform they need. A partner who leads with a specific tool before understanding your data is a warning sign.
- Will they recommend the platform that fits?
- Yes. We are provider-aware rather than provider-locked. We pick platforms based on the task, accuracy needs, privacy, latency, and cost, and we work on both Microsoft and AWS.
- Who builds the pipelines and reports?
- Senior engineers. Engagements are led by senior engineers and technical leaders who can make architecture, integration, and security decisions directly. The people who design your data model are the ones who build it.
- Can our team run it afterward?
- Usually, yes. Existing BI and data teams can often run the platform without new specialist hires. You own everything we build, and services we can provide include knowledge transfer, documentation, and ongoing support.
- Where is the team?
- Senior US-based engineers lead our builds, with nearshore support available for ongoing work. From the first call, you will know exactly who is building your pipelines, platform, and reports.
JBS Dev is a senior-led custom software development company, founded in 1999, that builds, modernizes, and integrates enterprise systems around the technology clients already own.
FAQ
Data, BI, and Reporting FAQs
What does data analytics consulting include?
Data analytics consulting covers assessing your data sources, building the pipelines that move and check data, setting up a data platform, and delivering reports and dashboards. Ours also covers AI-powered analytics and data modernization. It ends with a platform your team owns, plus knowledge transfer and documentation so they can run it.
What is the difference between data analytics and business intelligence?
Business intelligence reports on what happened, through dashboards and scheduled reports in tools such as Power BI or Tableau. Data analytics is broader. It includes BI, the pipelines and platform underneath, and analysis such as forecasting. Most companies need trusted BI reporting before more advanced analytics pays off.
Do we need a data warehouse?
Usually, once reports draw on more than a few systems. A data warehouse holds cleaned data from every source in one place, so reports stop contradicting each other and stop slowing down operational systems. Smaller data volumes may need only a well-modeled SQL database. See data warehouse consulting.
Our data is messy. Where do we start?
Start with the reports that matter most and the sources behind them. We profile those sources, then build validation and reconciliation into the pipelines, so quality improves each time data loads. You don't need clean data before you begin. Cleaning it is part of the work, and it goes faster when it's tied to reports people use.
How long does a data and reporting project take?
It depends on the number of sources and the state of the data, and the assessment sets the timeline. We deliver in stages, so the first reports arrive before every source is connected. For a regional bank, a daily data pipeline went from kickoff to running in just under four months.
How much does a data and reporting project cost?
It depends on the number and type of sources, data quality and history, the number of reports, platform licensing and cloud compute, security requirements, and support after launch. We don't quote a number before we understand the work. The assessment turns those factors into an estimate for each stage, including ongoing platform costs.
Do you work with Power BI, Tableau, and Microsoft Fabric?
Yes. We build reporting in Power BI and Tableau, and data platforms on Microsoft Fabric, Azure SQL Server, Amazon Redshift, and PostgreSQL, among others. For a post-acute care provider, we unified four separate systems into one data warehouse with Tableau reporting. We recommend the tools that fit your stack.
Can AI answer questions about our data?
Yes, with limits. An AI model connected to your data platform can turn a plain-language question into a query and return the result. We run that analysis in a sandbox instead of your production systems, and people review results before acting on them. See AI-powered analytics.
