section services -- business intelligence
Business intelligence services that turn data into decisions.
Data audits, Power BI and Tableau dashboards, data warehousing, and predictive analytics, built around the decisions your team actually needs to make.
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Trusted by industry giants, enterprises, and startups
definition
What are business intelligence services?
Business intelligence services turn scattered operational data into dashboards and reports a team can actually act on: pulling data from your CRM, ERP, and product systems into one place, then designing visualizations around the decisions your business makes every week, not every metric that could technically be tracked.
key takeaways
A dashboard nobody opens is not a BI project, it's a spreadsheet with better fonts. Adoption is the actual measure of success.
The global business intelligence market was valued at $34.82 billion in 2025, on track for $72.21 billion by 2034 (Fortune Business Insights, 2025).
Cloud-deployed BI platforms now hold 53.6% of the market, and 81% of businesses use AI or machine learning somewhere in their analytics stack (Hydrogen BI, 2025).
Forecasting and anomaly detection are client-visible parts of a modern BI platform now, surfaced directly inside the same dashboard your team already checks.
// BI vs. a spreadsheet
What separates BI from a well-built spreadsheet? A spreadsheet answers the question it was built to answer, once, until someone changes a formula and nobody notices. A BI platform pulls from live data sources, updates automatically, and is built to survive the person who built it going on vacation. That reliability is the entire point: a report your team can trust without double-checking it against the source system every time.
capabilities - seven
What our BI team does.
Seven capabilities, one goal: a dashboard your team actually opens.
- 01
BI strategy & data audit
A read on what data you already have, where it lives, and what gaps stand between you and the report you actually want.
- 02
Dashboard & reporting design
Power BI and Tableau dashboards built around the three or four decisions your team makes weekly, not every metric that could technically be displayed.
- 03
Data warehousing & pipelines
Data pulled from your CRM, ERP, and product systems into one warehouse, so a dashboard update stops meaning a manual export.
- 04
Predictive analytics & forecasting
Machine learning models layered on top of historical data to forecast demand, churn, and risk, visible directly inside the dashboards your team already checks.
- 05
Embedded analytics
BI dashboards embedded inside your own product or internal tools, so customers or staff see the numbers without logging into a separate platform.
- 06
KPI & metric design
Metrics defined once, consistently, so finance and marketing stop arguing over whose number is right.
- 07
BI training & adoption
Hands-on training so your team actually opens the dashboard instead of falling back on a spreadsheet by week three.
method
How does a BI engagement actually work?
A Syndell BI engagement runs five stages: data audit, warehouse and pipeline setup, dashboard design, a predictive layer where the data supports it, and training. Every stage ships something your team can click through.
- 01
Data audit & discovery
// outcome
-> a map of every data source and the gaps between them
- 02
Warehouse & pipeline setup
// outcome
-> data flowing automatically from source systems into one place
- 03
Dashboard design & build
// outcome
-> a working dashboard built around the decisions your team makes weekly
- 04
Predictive layer (where warranted)
// outcome
-> forecasting models added once the underlying data supports them
- 05
Training & handover
// outcome
-> a team that actually uses the dashboard, with documentation for what feeds it
platforms our BI team builds on
- Power BI
- Tableau
- Looker
- Snowflake
- AWS Redshift
- Google BigQuery
-- business intelligence, in numbers --
global business intelligence market size in 2025, projected to reach $72.21 billion by 2034 (8.40% CAGR)
src - Fortune Business Insights, 2025share of the BI market held by cloud-deployed BI platforms as of 2025
src - Fortune Business Insights, 2025of businesses now use AI or machine learning somewhere in their analytics systems
src - Hydrogen BI, 2025the honest part
Why most BI dashboards go unused.
The most common BI failure is not a broken pipeline, it's a dashboard built around the metrics a vendor thought looked impressive instead of the two or three decisions a team actually makes each week. Within a month, everyone is back in a spreadsheet.
We start every engagement by asking what decision the dashboard needs to answer, not what data exists to display. That constraint is uncomfortable at first, because it means leaving out metrics that are easy to build and interesting to look at, in favor of the handful that actually change what someone does on a Monday morning.
Information is the oil of the 21st century, and analytics is the combustion engine.
- Peter Sondergaard
Senior VP, Gartner Research -- Gartner Symposium/ITxpo, 2011
Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway.
- Geoffrey Moore
Author, Crossing the Chasm, 2012
engagement
Which engagement model fits you?
| Dashboard sprint | Full BI platform build | Dedicated BI team | |
|---|---|---|---|
| Best for | Getting one high-value dashboard live fast | Combining multiple data sources into one reporting layer | Ongoing analytics work across a growing number of reports |
| You get | A working Power BI or Tableau dashboard on your priority data source | Data warehouse, pipelines, and a dashboard suite across your key metrics | Embedded analysts who ship new dashboards and models continuously |
| Timeline | Weeks | Months, scope-dependent | Quarterly, renews |
| Team | BI analyst + data engineer | BI analyst + data engineer + data scientist | Your composition, our bench |
We don't publish a pricing table here on purpose - data complexity and scope decide the real cost, and an audit gets you a number a generic rate card never could. Tell us what decision the data should answer.
frequently - asked
Five questions,
straight answers.
01What is included in business intelligence services?
A data audit, dashboard and reporting design in Power BI or Tableau, data warehousing and pipeline setup, KPI definition, and predictive analytics where the data supports it. You can start with a single dashboard or a full data platform.
02How is Syndell's BI approach different from a typical consultancy?
BI work runs out of the same engineering studio that builds your software, so a dashboard that needs a new data pipeline gets built end to end instead of stalling on a data-engineering handoff. Dashboards are designed around decisions your team actually needs to make, not every metric that could technically be shown.
03Do we need a data warehouse before starting a BI project?
Not always. Small teams often start with dashboards built directly on existing systems. A dedicated warehouse becomes worth building once you're combining multiple data sources or the reporting load starts slowing down the source systems.
04Which BI platform should we use, Power BI or Tableau?
It depends on your existing stack and team. Power BI integrates tightly with Microsoft tools and is usually the lower-cost choice; Tableau tends to handle complex, highly custom visualizations better. We recommend a platform based on your data sources and team skills, not the one we happen to prefer.
05How much do business intelligence services cost?
It depends on data complexity and scope: a single dashboard build costs a fraction of a full data warehouse and pipeline project. We don't publish rate cards because they'd mislead you in both directions. Book a call and you'll get a concrete estimate against your actual data sources.
the practice
Explore our data and AI practice.
BI rarely runs alone. Most engagements pair it with the rest of the data and AI practice:
- Data science
the predictive modeling practice that feeds the forecasting layer inside your dashboards
- Data visualization
chart and dashboard design work for teams that need the visual layer scoped on its own
- AI consulting
for teams that want AI-driven features scoped once the underlying data platform is solid
- AI and machine learning development
production ML systems built on the same data your BI dashboards already surface
- Digital marketing
dashboards that connect campaign spend to pipeline are a common pairing with this practice
-- next issue - your data platform --
Start turning your data into decisions.
Tell us what decision the data should answer - we'll tell you honestly what it takes to get there.
