Service
Data analytics and dashboards your team trusts
We turn scattered information into numbers you can act on. We bring data together from your tools, clean it, agree what every figure means, and build dashboards and reports that update themselves.
- 1,200+ clients helped
- Building since 2010
- 15 services, one team
- 50+ technologies
At a glance
What we build
We collect information from your tools, clean and combine it, and show it in reports and dashboards you can trust.
Problems we solve
Numbers differ from report to report, reports are built by hand, and nobody is sure which figure is right, so decisions rely on guesswork.
Who we serve
Owners, managers, and finance and operations teams who need clear numbers, and anyone buried in spreadsheets.
How to choose
Match the tool to the job: a spreadsheet for small, simple needs, a dashboard tool when several sources feed one view, and a data warehouse when information is large or many people rely on it.
- 1,200+Happy clients
- 1,500+Projects delivered
- 100+Engineers and designers
- 20+Countries served
- 22Certifications
Every project gets its own squad from our 100+ engineers and designers: a project lead, a product designer, and engineers matched to your stack, with the same people from kickoff to launch.
Our approach
Decisions made on numbers, not guesses
We turn the information spread across your tools into answers: what sells, where time is lost, which customers are likely to leave. That means one place for the data, checks that it is correct, and reports people read in a minute.
The charts are the visible part. Most of the work happens before them: finding where information lives, fixing mistakes, joining sources, and agreeing what each number means.
That is why analytics projects stall when they start with a dashboard tool. We start with the questions you need answered.
The problem
Why numbers disagree
If two people measure "sales" differently, they will always get different answers. Agreeing the meaning once solves it for good.
Why numbers disagree
Before: Each team has its own figure. Sales spreadsheet: One total.; Finance report: A different total.; Manager's own file: A third total.. After: One agreed definition. Sales, defined once: Written down and shared.; One source of truth: All reports draw from it.; A dashboard everyone trusts: It updates on its own..
Before
Each team has its own figure
Same question, different answers.
- Sales spreadsheetOne total.
- Finance reportA different total.
- Manager's own fileA third total.
After
One agreed definition
Everyone reads the same figure.
- Sales, defined onceWritten down and shared.
- One source of truthAll reports draw from it.
- A dashboard everyone trustsIt updates on its own.
The pipeline
From scattered information to a trusted dashboard
Trusted numbers come from collecting, cleaning, combining, and defining data before anyone draws a chart.
From scattered information to a trusted dashboard
A sequence of 5 steps. Collect: Information is gathered from your tools.; Clean: Duplicates and mistakes are fixed.; Combine: Sources are joined into one picture.; Agree the meaning: Each number gets one clear definition.; Show: Dashboards and reports update themselves..
- CollectInformation is gathered from your tools.
- CleanDuplicates and mistakes are fixed.
- CombineSources are joined into one picture.
- Agree the meaningEach number gets one clear definition.
- ShowDashboards and reports update themselves.
Data you can trust
What makes information trustworthy
Before any chart, we check the information against four simple tests.
Complete
- Nothing important is missing.
- Gaps are found and either filled or flagged.
Consistent
- The same thing is recorded the same way everywhere.
- Duplicates and spelling variations are merged.
Current
- Information updates on a schedule you can rely on.
- Each report says when it was last refreshed.
Owned
- A named person is responsible for each source.
- Changes to definitions are written down.
Choosing a setup
Spreadsheet, dashboard tool, or data warehouse?
You do not need a large system to get good numbers. Pick the lightest setup that does the job.
How much information do you have, and how many people rely on it?
Choose this when
The information is small, comes from one or two places, and a few people use it.
- What you get
- Quick results and no new tools to learn.
- Keep in mind
- It gets fragile as it grows, and errors are easy to introduce.
Choose this when
Several tools feed one view, and managers want live numbers without asking someone to build a report.
- What you get
- Automatic updates and shared dashboards, usually in days or weeks.
- Keep in mind
- Without clean definitions, the dashboards can still disagree.
Choose this when
The information is large, comes from many systems, and many people or processes depend on it.
- What you get
- One organized home for all your information, ready for reports, dashboards, and AI.
- Keep in mind
- It needs planning, ownership, and ongoing care.
Focus
From many numbers to the few that matter
A good dashboard is not the one with the most charts. It is the one that helps people decide.
From many numbers to the few that matter
Stages that narrow: Everything you collect: Every record in every tool.; What is worth keeping: Information that is correct and useful.; Measures you agree on: A short list with clear definitions.; The few numbers leaders watch: Reviewed every week.; Decisions: What to change, start, or stop..
- Everything you collectEvery record in every tool.
- What is worth keepingInformation that is correct and useful.
- Measures you agree onA short list with clear definitions.
- The few numbers leaders watchReviewed every week.
- DecisionsWhat to change, start, or stop.
How we do it
How a data project runs
Start with the questions
We list the decisions you want to make and the numbers you would need to make them.
What it produces
- List of questions
- Measures and definitions
Find and check the information
We locate where each piece lives, and check its quality and gaps.
What it produces
- Source map
- Quality findings
Bring it together
We connect the sources and clean the information, on a schedule that suits you.
What it produces
- Connected sources
- Cleaning rules
Build the views
We create dashboards and reports with the people who will use them, and test them against known answers.
What it produces
- Dashboards
- Reports
- Test against known figures
Keep it healthy
We monitor updates, fix breaks, and adjust as your questions change.
What it produces
- Monitoring
- Change log
- Regular reviews
Choosing the right approach
How we pick the right approach
A dashboard is only as good as the data behind it. When information is recorded inconsistently, we fix how it is captured first, then build the charts, so the numbers stay right.
You get reports people trust, not just reports that look good.
Technology
The data tools we build with
The detail a technical reviewer will ask about: storing and organizing information, moving information, modeling and definitions, and reporting and data quality.
For many businesses a well-designed database such as PostgreSQL or MySQL is enough. As volume grows we use data warehouses such as Snowflake, BigQuery, Redshift, or Databricks, chosen for cost, scale, and the tools you already use.
Data is pulled from source systems, cleaned, and loaded on a schedule or as changes happen, a process often called ETL (extract, transform, load). We build these pipelines with monitoring, so failures are caught and fixed.
We create a clear structure for your information, with each measure defined once in code, so every report draws from the same logic.
- Dashboards and reports in the tools your team prefers.
- Automatic checks for missing, late, or unusual information.
- Access rules, so people see only what they should.
- Documentation of sources and definitions.
What it looks like
See the data systems we build
Trusting the numbers
Why people stop trusting a dashboard
A dashboard is trusted only while its numbers match what people already know. When two reports disagree, or a figure changes without explanation, people go back to their own spreadsheets and the dashboard is forgotten.
We prevent this by agreeing the definition of each measure in writing, showing where every figure comes from, and displaying when the data was last updated. We also check results against a source people already trust, before anyone relies on them.
Pricing and timelines
What data and analytics work costs
Every project is quoted in writing after a short call. These ranges show where most projects land, so you can plan before you talk to us.
Dashboards on your data
Clear dashboards on the data you already have, in the tool your team prefers.
Typical range
$2,500 to $8,000
1 to 2 weeks from approved scope
Reporting system
Data pipelines that pull from your tools and keep reports up to date.
Typical range
$8,000 to $18,000
3 to 5 weeks from approved scope
Data warehouse and BI platform
One source of truth for the whole business, with governance and access rules.
Typical range
$24,000 to $48,000+
6 to 10 weeks from approved scope
The timeline starts once the scope is approved and we have what we need from you.
What moves the price
- Number of data sources
- Data quality and cleanup
- Refresh frequency
- Users and access rules
Not sure which fits? Tell us what you need and we will send a fixed quote and timeline for your project.
Get a quoteSelected work
Data & Analytics projects we have delivered
What clients say
In our clients' words
The margin report used to arrive after the buying meeting. Now I can compare margin, discounts, and stock in the same view before we decide what to reorder.
Amelia PriceMerchandising ManagerWillow & Pine RetailRetail I used to combine three exports before every morning meeting. DECHY helped us get to one set of numbers. We spend the meeting deciding what to do, not arguing over which report is right.
Michael ChenFinance Operations ManagerCrestline DistributionLogistics
Common questions
Questions about data and analytics
Where do we start if our data is a mess?
Start with one question that matters and the few numbers needed to answer it. We then trace where those numbers come from and fix those sources first. You do not have to clean everything at once.
Do we need a data warehouse?
Not always. A warehouse helps when information is large, comes from many systems, or many people rely on it. For smaller needs, a connected dashboard tool or a well-built spreadsheet can be enough. We recommend the lightest setup that works.
Can dashboards update automatically?
Yes. Once your sources are connected, dashboards can refresh on a schedule or as changes happen. Each view should show when it was last updated.
How do you keep our information secure?
We limit who can see what, protect access keys, and keep a record of who looked at what. Sensitive information is handled with extra care, including whether it should be copied at all.
Can you add AI to our analytics?
Yes, where it helps, such as predicting demand or explaining a change in a number in plain language. It works best on top of clean information. See our AI service.
How is this different from integration?
Integration moves information between systems as work happens. Analytics collects it over time, organizes it, and helps you understand what it says. Many projects use both.
Have a different question?
Tell us what you are weighing up and we will answer it plainly.
Ask a questionAlready a client? Email support@dechytech.com
Tell us the questions you want answered
Describe the decisions you want to make and where your information lives today. We will tell you what is possible, what to fix first, and the lightest setup that works.