Data engineering and governance means getting your numbers from where they start to the people who use them: moved the same way each time, checked on the way, labelled with where they came from, and looked after by someone named.
Read the tag backwards
In our story, water is your numbers, the springs are your apps and spreadsheets, the pipes move the numbers into one shared reservoir, the cleaning plant is the checks, Gota’s luggage tag is the record of where each number started, the nameplates are named owners, and the private-records tap is who gets to see what. An unnamed bucket is someone nobody has said yes to yet. Tomás is the engineer who looks after the shared pipes; the Mayor stands in for a business leader.
The tag, read backwards · The stock count
The stock count comes from the apps spring, through the main pipe, the raw side, the cleaning plant and the cleaned side. The shop manager owns these numbers; Tomás looks after the pipes.
Worth knowing: Labels show where data came from. They don’t make it correct by themselves; that’s what the checks, and fixing the source, are for.
An illustration, not a measurement. Each stop punches a hole in the drop’s tag, and the same line goes on Tomás’s clipboard.
Watch · 2 min 9 sec
Every street in a hill town swears its jug of water is the right one, and they all blame Tomás. A short story about one smug drop, a frog who grabs whatever nobody owns, and why your numbers need to be moved, checked, labelled and owned.
The idea in plain words
Each one in everyday words first, then the name experts use for it.
Move it. Check it. Label it. Own it.
01
Numbers from your apps, spreadsheets and partners travel through pipes into one shared reservoir, the same way each time, sorted and labelled. Live where waiting would hurt, once a day where that is fine.
Experts call this: data pipelines, streaming and batch, data platform
Worth knowing: “Live” means within seconds or minutes, not at once, and live flows take more effort to run. Use them where waiting would hurt. A big shared store helps only when what goes in is sorted, labelled and owned. The problem is missing labels and owners, not one shared store.
02
Each delivery is checked before it moves from the raw side to the cleaned side: is it late, is anything missing or doubled, is it the right shape, and does it make sense? What looks wrong is set aside for a named person to look at.
Experts call this: data quality, data observability
Worth knowing: Checks catch many problems, not all of them.
See what the checks set aside03
Each set of numbers carries a record of where it came from, each step on the way and which checks it passed, so an odd figure can be followed back to the source that needs fixing instead of argued about.
Experts call this: data lineage, data catalog
Worth knowing: A label says where data came from, not that it is right.
Follow one glass back to its tank04
Each important set of numbers has an owner from your business, who answers for what it means and decides who can see private records. Engineers look after the pipes themselves.
Experts call this: data ownership, stewardship, data governance, access rules
Worth knowing: Rules help when people can follow them. This is not legal advice; your legal advisers have the final say.
See whose glass gets filledSee the difference
Four gates stand between the raw side and the cleaned side of the reservoir. Switch each check on or off and see what reaches the glass, and what waits on the tray for a person.
Clear glass · Someone still looks at the tray.
Worth knowing: Checks catch many problems, not all of them. Someone still has to look at what gets set aside.
An illustration, not a measurement. The glass shows clear or cloudy, not a score.
Who’s in charge
Three taps, three owners. Switch the nameplates on, then pick who is asking: shared taps pour for anyone the town has said yes to, and the private-records tap pours only for the names on its owner’s list. An unnamed bucket stands for someone nobody has said yes to yet.
Worth knowing: A big shared store helps only when what goes in is sorted, labelled and owned. The problem is missing labels and owners, not one shared store.
Bread numbers
the baker
Poured
Fire-station board
the fire chief
Poured
Private records
the clinic owner
On the list: The clinic owner
Not on the list. Ask the clinic owner
Worth knowing: Rules help when people can follow them easily, so we set them up with the teams who use the data.
Worth knowing: Helping you see who can open private or personal data is not legal advice, and it does not by itself meet any law’s requirements. Your legal advisers have the final say.
An illustration, not a measurement. A named owner decides who can see private records.
Sounds familiar?
If any of these ring true, your numbers are probably arriving without a tag or an owner.
Six plain questions. Answer what you can, and we’ll point to where we’d look first.
Answer any question and the places we’d look first appear here.
Answer “No” or “Not sure” to any question to email the list.
Nothing is stored or sent unless you choose to email it.
What we help with
Eight pieces of work. Start by tracing one number, or bring them together into one checked, labelled, owned supply.
Where your numbers start, and where they go wrong.
A map of where that number really comes from, where it goes wrong and which source to fix first.
We pick one figure that matters to you and follow it back through every spreadsheet, app and step, side by side with the people who use it.
Experts call this: data assessment, lineage discovery
Also in this area
One shared place, fed by pipes that run on their own.
Teams can draw from one sorted, labelled place instead of keeping their own copies.
Plan and build one place for your numbers that keeps a raw side and a cleaned side next to each other, moving off old stores step by step. A big shared store helps only when what goes in is sorted, labelled and owned. The problem is missing labels and owners, not one shared store.
Experts call this: data platform, data warehouse, lakehouse, migration
Numbers can arrive without anyone carrying exports by hand.
Automatic routes that bring numbers in from apps, files and partners and deliver them the same way each time, live only where waiting would hurt.
Experts call this: data pipelines, ETL/ELT (copy, tidy and load, in either order), orchestration (steps run in order), streaming and batch
Also in this area
Checks before the cleaned side, and tags that show where each number started.
Problems can show up at the plant rather than at the tap.
Tests on each delivery, before it reaches the cleaned side, for late arrivals, missing or doubled records, odd shapes and values that don’t make sense, with what fails set aside for its named owner. Checks catch many problems, not all of them. Someone still has to look at what gets set aside.
Experts call this: data quality, data observability
An odd figure can be followed back, and changes can be agreed before they break a report.
A record of where each set of numbers came from, a findable map of what data you have and who looks after it, and written agreements between the team that sends data and the team that uses it. Written agreements help teams agree before they change things; they don’t replace talking to each other.
Experts call this: data lineage, data catalog, data contracts
Also in this area
A name on each important set of numbers, and clear rules for private records. This is not legal advice; your legal advisers have the final say.
Each important question has an agreed meaning and a named person to ask.
Name an owner and a day-to-day caretaker for each important set of numbers, agree what words like “sale” and “customer” mean, and tidy duplicate records.
Experts call this: data ownership, data stewardship, master data (one record per customer or product)
Their owner decides, in writing, who may open private records.
Decide who can see which private or personal data, record who opened what, and agree how long it is kept, with laws such as India’s Digital Personal Data Protection (DPDP) Act and Europe’s General Data Protection Regulation (GDPR) in mind. Helping you see who can open private or personal data is not legal advice, and it does not by itself meet any law’s requirements. Your legal advisers have the final say.
Experts call this: data governance, access rules, privacy support
AI helpers can start from material its owners have approved. Tagged, checked material gives AI helpers a better start, not right answers. Who may see what is still the owner’s call.
Prepare documents and records for AI, tag where they came from, and set things up so AI helpers are meant to read only the material their owners have approved.
Experts call this: AI-ready data, provenance (a record of where it came from)
Also in this area
For your technical team: we work with what you already use, for example Databricks, Snowflake, BigQuery, Microsoft Fabric, dbt, Apache Airflow, Apache Kafka, Apache Iceberg and OpenLineage.
Ways to start
Follow one important figure back to its sources and write up a map of what we find.
One working session to name owners and caretakers for your most important numbers.
Add checks to one pipe that matters, with what looks wrong set aside for a named person.
How we work
Five steps in plain words, from following one number back to its source to your team running it with us.
Trace
We pick one number your team argues about and follow it back through every spreadsheet, app and step, writing down what we find.
Together
Side by side with the people who make and use the numbers, we agree who owns each important set of numbers, what the important words mean and which source to fix first.
One pipe
We build one pipe from its sources to one report or AI use, with checks, tags, an owner and who-can-see rules in it from the start.
Next pipe
When the first pipe works for its team, the next one reuses the same pattern, and the pipe map and shared meanings grow with it.
Run it with us
Your owners and engineers run the pipes, look at what the checks set aside and review the rules regularly, with us working beside them. How much your team runs on its own, and how long we stay involved, is agreed with you.
Where it fits
Illustrative examples, not customer stories: the kind of work this approach suits.
Good to know
Plain answers to what owners and tech leads ask first. Something else on your mind?
Ask us directlyGetting your numbers from where they start to the people who use them: moved the same way each time, checked on the way, labelled with where they came from, and looked after by a named person who decides who gets to see them.
No. We start with one number that matters and the pipes behind it. A new shared store comes in only where it helps, and moves happen step by step.
We start from what you already run, for example Databricks, Snowflake, BigQuery, Microsoft Fabric, dbt, Airflow and Kafka. A new tool comes in only where it fills a real gap.
It shouldn’t be. We tie the rules to one real problem first, keep them short and build them into the pipes, so the right person’s glass gets filled without a chase. Rules help when people can follow them easily, so we set them up with the teams who use the data.
People from your business. Each important set of numbers gets a named owner and a caretaker who keeps it running, and your team runs the pipes with us beside them. How much your team runs on its own, and how long we stay involved, is agreed with you.
It helps you see where personal data goes, who can open it and how long it is kept. Helping you see who can open private or personal data is not legal advice, and it does not by itself meet any law’s requirements. Your legal advisers have the final say.
No. Live flows take more effort to run. We use them where waiting would hurt, such as stock on the shelf or an unusual payment, and keep daily runs where daily is fine. As in the film: bread can wait.
It depends on scope. We usually start small, by tracing one number, and agree the plan and cost with you before anything bigger.
Heard it before?
Answer: A store with no sorting, no labels and no owners turns into a swamp, like the Mayor’s giant tank. The shared store is fine; the missing labels and owners are the problem.
Answer: Building and running the pipes is the engineers’ job. What the numbers mean, and who gets to see them, is the business’s call, so each important set of numbers has a business owner.
Answer: Water is checked every day, not once. Checks run on each delivery, and someone still looks at what they set aside.
Answer: A label says where data came from, not that it is right. In the film, Gota’s sticker said mountain spring. Her tag said otherwise.
Answer: Live pipes take more looking after. Use them where waiting would hurt; once a day is fine for many jobs.
Answer: It means a named person decides, quickly, whose glass gets filled, with rules short enough that people follow them.
Download the one-page overview or the playbook to share with your team, or tell us which number your team argues about most. We’ll set up a short call to talk it through.
Or write to contact@algoshred.com
