Get your systems to agree.
Reports and AI are only as good as the data underneath them. Data engineering is the work of moving, connecting, cleaning and storing that data, so the people and software that depend on it can trust what they see.
Every gap between systems gets filled by a person.
Someone rekeys the order. Someone reconciles the month-end report by hand. Someone exports a file every night and hopes nothing broke. None of it appears as a line item, but it shows up in overtime, in decisions made on last week's numbers, and in a dashboard nobody quite believes.
Most mid-size companies don't need a data science team first. They need the plumbing: pipelines that pull data out of the ERP, the CRM and the plant systems, one place to keep it, and checks that catch bad records before they reach a report.
Fix one flow end to end, measure what it saves, then do the next. That order keeps every dollar tied to a result you can point to.
Through MSquare Technology, our data engineering partner.
Data PipelinesBatch and real time
ETL and ELT pipelines that pull from your ERP, CRM, plant and older systems on a schedule or as events happen, with validation and error handling built in rather than bolted on.
Data PlatformOne place to keep it
A warehouse or lakehouse on Databricks, Snowflake or Amazon Redshift, sized to the data you actually have, not the data a vendor hopes you'll have one day.
IntegrationSystems that stay in step
CRM, ERP, third-party APIs and legacy systems kept in sync, so the same customer or order isn't typed into three places and argued about in a fourth.
Salesforce + DatabricksThe right connection
Salesforce data into Databricks for analytics, or warehouse data back into Salesforce for agents and segments. The free route finder shows which connection fits your data.
Data Quality & GovernanceNumbers people trust
Duplicate and conflicting records caught before they spread, with clear rules for which system owns which field and who signs off when they disagree.
Staff AugmentationEngineers when you need them
MSquare data engineers join your team for a project or a season, without a six-month hiring cycle, and hand the work back to your people when it's done.
How the work runs.
Data assessment · 1 to 2 weeks
Where your data lives, how much of it there is, how fast it changes, and where the quality breaks. You get the findings whether or not anything else happens.
Architecture design · 2 to 3 weeks
The pipeline design and the platform choice, with security planned in, agreed before anything is built.
Development and testing · 4 to 8 weeks
Pipelines built, transformation rules written, and the output checked against the numbers your team already trusts.
Deployment and monitoring · 1 to 2 weeks
Live in production, with monitoring, alerts and documentation your own team can run. We stay accountable for the outcome the whole way through.
Durations follow MSquare's own published process. Your scope sets the real timeline, and you'll have it in writing before work starts.
Before you book the call.
Databricks or Snowflake?
It depends on what you'll do with the data, not on which platform is in the news this quarter. Mostly SQL reporting on structured ERP and CRM data points one way. Heavy data science, streaming or unstructured data points the other. The two cover more of each other's ground every year, and MSquare works with both, so the recommendation can follow your data instead of a partnership.
Do we need this before AI?
Usually, yes. Gartner predicted in February 2025 that through 2026, organizations will abandon 60% of AI projects that lack AI-ready data. An AI tool reading duplicate, conflicting records gives confident wrong answers faster than a person would.
We already have a data team. Where does MSquare fit?
Alongside them. MSquare can add engineers for a project or a season, or take one flow end to end while your team keeps the rest. The good engagements end with your people running more of it, not less.
Is this a data engineering problem or a reporting problem?
If one system holds everything and you only need better dashboards, it's a reporting problem, and Decypher's BI work may fit better. If the numbers disagree because they come from several systems, it's data engineering. If it's neither, we'll tell you that on the first call.
What does it cost to start?
Nothing to start. MSquare funds the discovery and a proof of concept. The price of the full work depends on how many systems and how much data are involved, and you'll have that number, and see the proof of concept, before you commit to anything.
